Human-ai co-creation system

Abstract

Provided is a process including: obtaining an objective for a multi-agent artificial intelligence (AI) platform to generate a software application; determining a domain to which the objective applies; accessing information in the domain; using the information in the domain, with a reasoning AI model, decomposing the objective into sub-objectives to complete the objective; determining with the AI platform, user interface (UI) components of the software application; orchestrating a plurality of AI agents of the AI platform to determine how to configure at least some of the UI components and how to respond to input from at least some of the UI components; and storing a first version of the software application in memory.

Description

PATENT APPLICATIONHUMAN-AI CO-CREATION SYSTEMCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This patent claims the benefit of U.S. Provisional Patent Application 63 / 718,401, filed November 8, 2024, titled IMMERSIVE HUMAN-AI CO-CREATION INTERFACE; U.S. Provisional Patent Application 63 / 718,400, filed November 8, 2024, titled ROBUST EXPERT EXPLAINABLE GENERATIVE Al; and U.S. Provisional Patent Application 63 / 718,392, filed November 8, 2024, titled CREATING CONTEXT-SPECIFIC, VERSATILE EXPERT Al PERSONAS. The entire content of each afore-listed earlier-filed application is hereby incorporated by reference for all purposes.BACKGROUND1. Field

[0002] The present disclosure relates generally to artificial intelligence (Al) and, more specifically, to use of multi Al agent systems to create highly customized special purpose software applications.2. Description of the Related Art

[0003] In many organizations, building enterprise software applications involves gathering requirements, designing user experiences, implementing data flows, and connecting a mix of services and systems. Teams iterate on features, review compliance and security considerations, and deploy updates across environments while coordinating among product, engineering, and operations. This work often spans documents, dashboards, and integrations that should be generally understandable to stakeholders and maintainable over time.

[0004] Low- and no-code tools can speed up parts of this process, but they may limit how interfaces adapt to complex data, how logic scales across use cases, or how teams track what-1- 078474-0586924happens inside a workflow. In some settings, these tools make it hard to blend domain-specific analysis with clear explanations and audit trails, or to evolve an application while preserving user context. As a result, certain organizations seek approaches that keep speed while improving transparency, flexibility, and fit for enterprise environments.SUMMARY

[0005] The following is a non-exhaustive listing of some aspects of the present techniques. These and other aspects are described in the following disclosure.

[0006] Some aspects include a process including: obtaining an objective for a multi-agent artificial intelligence (Al) platform to generate a software application; determining a domain to which the objective applies; accessing information in the domain; using the information in the domain, with a reasoning Al model, decomposing the objective into sub-objectives to complete the objective; determining with the Al platform, user interface (UI) components of the software application; orchestrating a plurality of Al agents of the Al platform to determine how to configure at least some of the UI components and how to respond to input from at least some of the UI components; and storing a first version of the software application in memory.

[0007] Some aspects include a process, including: a parsing a sequence of tokens from naturallanguage text; computing, with a transformer encoder of a neural network, a feature vector of the sequence of tokens; inputting the feature vector into a probabilistic head of the neural network comprising a Gaussian-process layer and determining, with the probabilistic head, both a latent mean and a latent variance for each of a plurality of candidate output classes; computing, with the probabilistic head, predictive class probabilities from the latent means and latent variances; selecting, with the neural network, one of the candidate output classes based on the predictive class probabilities; and determining an uncertainty of the selection based on the latent means and the latent variances.

[0008] Some aspects include a process, including: obtaining a corpus of training records; computing embedding vectors from the training records in an embedding space in which spatial proximity corresponds to similarity in style of communication of the corresponding training records; clustering the embedding vectors to determine a plurality of clusters corresponding to-2- 078474-0586924different styles of communication; obtaining a selection of two styles from among the plurality of styles corresponding to two respective clusters among the plurality of clusters; generating an output communication by applying the two selected styles; and storing the output communication in memory.

[0009] Some aspects include a tangible, non-transitory, machine-readable medium storing instructions that when executed by a data processing apparatus cause the data processing apparatus to perform operations including the above-mentioned process.

[0010] Some aspects include a system, including: one or more processors; and memory storing instructions that when executed by the processors cause the processors to effectuate operations of the above-mentioned process.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above-mentioned aspects and other aspects of the present techniques will be better understood when the present application is read in view of the following figures in which like numbers indicate similar or identical elements:

[0012] Figure 1 A is a block diagram of an example computing environment in which a human-AI co-creation system may be implemented.

[0013] Figure IB is a block diagram illustrating an example of an Al system in accordance with some embodiments of the present techniques.

[0014] Figure 1C illustrates an example of a style transfer system in accordance with some embodiments.

[0015] Figure 2A is an example process by which the techniques of figure 1A may be implemented.

[0016] Figure 2B is a flow chart depicting an example of a process that may be executed by the Al system of figure IB in accordance with some embodiments of the present techniques.-3- 078474-0586924

[0017] Figure 3 illustrates an example of a process that may be executed by the style transfer system of figure 1C in accordance with some embodiments.

[0018] Figures 4A-F illustrate a user interface of a compliance application generated with the techniques of figures 1A and 2A.

[0019] Figures 5A-B illustrate a user interface of an engineering diagram analysis suite software application generated with the techniques of figures 1A and 2A.

[0020] Figures 6A-B illustrate a user interface of a code generation assistant generated with the techniques of figures 1A and 2A.

[0021] Figures 7A-E illustrate a user interface of a network data analyzer software application generated with the techniques of figures 1A and 2 A.

[0022] Figures 8A-F illustrate a user interface of a data analysis application generated with the techniques of figures 1 A and 2A.

[0023] Figure 9 illustrates a dashboard of an application development system like that of figures 1A and 2A.

[0024] Figure 10 is an example of a computing device by which the aforementioned computing environments and processes may be implemented.

[0025] While the present techniques are susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. The drawings may not be to scale. It should be understood, however, that the drawings and detailed description thereto are not intended to limit the present techniques to the particular form disclosed, but to the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present techniques as defined by the appended claims.-4- 078474-0586924DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

[0026] To mitigate the problems described herein, the inventors had to both invent solutions and, in some cases just as importantly, recognize problems overlooked (or not yet foreseen) by others in the fields of Al and human computer interaction (HCI). Indeed, the inventors wish to emphasize the difficulty of recognizing those problems that are nascent and will become much more apparent in the future should trends in industry continue as the inventors expect. Further, because multiple problems are addressed, it should be understood that some embodiments are problem-specific, and not all embodiments address every problem with traditional systems described herein or provide every benefit described herein. That said, improvements that solve various permutations of these problems are described below.

[0027] Several aspects are described below organized under different headings. These aspects may be used together or independently, which is not to suggest that features under a given heading cannot also be used independently.

[0028] HUMAN-AI CO-CREATION SYSTEM

[0029] Certain embodiments may include a UI planner agent integrated within a multi-agent system that receives a natural-language mission, determines a domain, and decomposes the mission into sub-missions that an application can execute. The UI planner agent may determine that a compliance use case calls for uploading policy documents, extracting rules from text and images, applying those rules to evaluation documents, and causing views that associate extracted rules with evaluation outcomes. The UI planner agent may select data sources and tools, determine sequencing, and specify how outputs feed user-visible components and background processes.

[0030] The UI planner agent may determine which UI components to present and how to arrange them. The agent may select uploaders, image galleries, buttons, charts, text inputs, and other widgets, determine interaction logic, and issue instructions by which a UI agent renders those components. Layout decisions may account for screen size and available space so components resize or relocate as conditions change. Event handlers may be generated so the application can obtain inputs, access analysis results, and cause updates without manual rewiring.-5- 078474-0586924

[0031] An autonomous orchestrator may coordinate communication and task distribution across a large pool of agents. Domain-specific agents may contribute sector knowledge such as energy, semiconductor, finance, or engineering practices, while task-specific agents may perform focused operations such as rules extraction, data queries, validations, image analysis, or calculations. The orchestrator may manage execution order, error recovery, and retries, obtain intermediate results, and return outputs to the UI agent so the application remains responsive as processing progresses.

[0032] The system may support real-time change during use. A user may request a layout change or a workflow adjustment, and the platform may obtain this feedback, determine modifications, and update components or logic, in some embodiments while preserving session state. In this way, the application may evolve as users interact with it, with the UI planner agent and the orchestrator causing the necessary updates to design, code, and data flow so that the application continues to operate while adapting to user needs.

[0033] Certain embodiments may receive a natural -language objective and cause a multi-agent platform to generate a working application from that objective. A reasoning model may determine a domain, decompose the objective (such as a mission) into sub-objectives, and select data sources and tools. A UI planner may determine interface components and a layout, while code generation agents may produce event handlers and rendering code. A coordinator module may select taskspecific and domain-specific agents to supply analysis and data to each component, and may update the running application as feedback arrives. In some cases, the platform may change a first version into a second version during a user session while preserving session state.

[0034] Some embodiments may mitigate gaps found in low or no code tools by accessing data at the time of ingestion and recording what was received, how it was processed, and which resources were used. The system may analyze tables and images, extract facts, and create or update a knowledge graph. A processing view may present a flow that shows each step, its inputs, and its outputs, with citations available in a side panel. A question view may allow a user to pose questions to multiple language models, determine relevance scores for each response, and provide a consolidated answer, while allowing the user to fork questions and track how results change across branches.-6- 078474-0586924

[0035] Some embodiments may provide hyper-personalized applications that address enterprise tasks. Examples that may be generated include a compliance analysis application that may obtain a policy document, extract rules from text and images, evaluate another document against those rules, and cause a visualization that associates content with pass or fail determinations. In another example, an engineering application may ingest a design document, detect a single-line diagram or other diagram, determine components, and present an inventory. An example generated network application may ingest a configuration file, determine a topology, and cause a graph that reflects devices and links. Additional applications may provide clustering, time-series views, and supplychain summaries, using charts such as force-directed graphs, clustered graphs, chord diagrams, and time-series plots that the UI planner selects and sizes based on client screen dimensions.

[0036] Further embodiments may support an application library to speed creation and reuse. A user may request a mission, and the platform may determine which agents to call, obtain outputs, and bind those outputs to UI components so interactions remain consistent across the application. Tenant administration may allow an enterprise to manage users, groups, access to websites and data, and deployment within a private network.

[0037] In some embodiments, the present techniques may be integrated with systems and processes described in other patent applications by the applicant filed on the same day as this filing. Some embodiments may apply personas to shape model outputs with the techniques described in the US patent application bearing attorney docket number 078474-0586614, titled CREATING CONTEXT-SPECIFIC, VERSATILE EXPERT Al PERSONAS. Some embodiments may render model outputs explainable with the techniques described in the US patent application bearing attorney docket number 078474-0586619, titled ROBUST EXPLAINABLE ARTIFICIAL INTELLIGENCE. The entire content of each afore-mentioned patent filing in this paragraph is hereby incorporated by reference.

[0038] Certain embodiments of as computing environment 10A in figure 1A may include an application development system 12A that receives objectives and causes applications to be generated and updated. An Al platform 14A may supply reasoning, planning, and tool use forthose operations and may reside within the application development system 12A or operate as an independent service. An enterprise data repository 15A may store documents, databases, and other-7- 078474-0586924records of an enterprise that may be non-public and domain specific. User devices 16 may provide requests, supply feedback, and display results produced by the application development system 12A and the Al platform 14A.

[0039] The computing environment 10A may operate entirely within an enterprise network, span a mix of on-premises and cloud segments, or be fully public. Network 18A may be private and secured through identity controls, network segmentation, private routing, and encrypted transport or may be (or may include) the public Internet. The application development system 12A and the Al platform 14A may access non-public sources inside the enterprise while honoring data residency, access policies, and audit requirements. In some deployments, components may reside on dedicated subnets and use private endpoints or peering links so traffic does not traverse the public internet. The system may determine where to execute processing based on policy and may cause records of access and lineage to be written for later review.

[0040] Multi-tenant versions of the Al platform 14A or the application development system 12A may serve more than one business unit or customer while maintaining isolation. The platform may obtain requests from different tenants, determine tenant context, and enforce data and model separation at runtime. Administrative functions may allow a tenant to manage users, groups, and integrations without exposing resources of other tenants. In hybrid deployments, some tenants may keep sensitive workloads inside the enterprise network while other tenants access hosted services, with controls that limit cross-tenant movement of data and models.

[0041] The data repository 15A may store any or all of a broad range of files, data structures, and records maintained by an organization. The repository may obtain and retain databases that hold transactional records and reference tables, knowledge bases that capture curated facts and relationships, wikis that record policies and procedures, and document repositories that store contracts, reports, and multimedia. The repository may keep logs, configuration files, spreadsheets, presentations, emails, images, diagrams, and time-series feeds. In some cases, the repository may include graph stores that represent entities and links, columnar stores for analytics, and object stores for large binaries. The system may access these sources through connectors or APIs and may resolve permissions based on enterprise policy.-8- 078474-0586924

[0042] A financial institution may keep account ledgers, wire instructions, exception files, scanned checks, KYC (know your customer) profiles, loan packages, pricing models, and audit reports. A healthcare facility may store electronic health records, lab results, imaging studies, discharge notes, and payer guidelines, while a clinical trial provider may keep protocols, informed consent forms, site reports, adverse event reports, sensor streams, and de-identified datasets. A research institution or university may store grant applications, IRB (institutional review board) approvals, course materials, publications, laboratory notebooks, microscopy images, sequencing results, and data management plans. A manufacturer may retain bills of materials, CAD (computer aided drafting) drawings, single-line diagrams, work orders, quality checks, machine telemetry, maintenance logs, and supplier certifications. A media company may keep editorial calendars, scripts, video masters, audio stems, caption files, image libraries, ad copy, rights metadata, and content performance summaries.

[0043] The repository 15A may hold both public and non-public materials and may reflect the specific domain of the enterprise. Records may appear as relational tables, JSON (Javascript™ object notation) documents, XML (extensible markup language) files, PDFs (printable document format), images, or proprietary formats, and the system may determine how to parse and index them so downstream components can retrieve, analyze, and present them. In some deployments, the data repository 15A may keep lineage and access metadata so later processes can determine provenance, apply retention rules, and cause appropriate safeguards when content is retrieved or moved between environments.

[0044] During operation, user devices 16 may communicate over a network 18A that may be the public internet or a private enterprise network. The application development system 12A may obtain content from the enterprise data repository 15 A, access the Al platform 14A for analysis and generation, and cause user interfaces to be delivered to user devices 16. In some cases, the system may determine where to execute processing based on policy or access constraints and may maintain session state so changes appear without interrupting ongoing use.

[0045] In some embodiments, a user device 16A may be a general -purpose computing platform that may run an operating system such as Windows™, macOS™, Linux™, iOS™, or Android™ and may execute a client application that may prepare, transmit, and receive serialized request and-9- 078474-0586924response messages destined for an Al platform 14A over the internet 18A. The client application (e g., a browser or a native application) on the user device 16A may accept user input, may normalize text using configured tokenization and Unicode normalization, and may assemble a request object that may include a prompt string, model selection hints, client-side timestamps, and an idempotency key. The user device 16A may open a Transport Layer Security session to the Al platform 14A via the internet 18A, may attach authentication material such as bearer tokens or mutual Transport Layer Security client certificates, and may send the request over Hypertext Transfer Protocol. The user device 16A may maintain a queue for pending requests and may implement an asynchronous loop that may dequeue the next request, check network status, sign a payload using a device key stored in a secure element such as Secure Enclave™ or Android™ Key store, transmit the payload to the Al platform 14A, and store a response in non-volatile storage on success. On retryable errors, the loop may increment an attempt counter, requeue the request, and back off using a randomized delay. The user device 16A may, in some embodiments, cache context artifacts and evaluation datasets subject to a least-recently-used eviction policy and may record a provenance record containing request identifiers, hash digests of payload fragments, and server-supplied audit metadata associated with responses received from the Al platform 14A.

[0046] In some embodiments, multiple user devices 16A (e.g., more than 10, more than 100, or more than 10,000) may operate within an enterprise deployment and may be geographically remote across regions while accessing the Al platform 14A through the internet 18A. Each user device 16A may register with an identity provider, may fetch configuration profiles from a device management service, and may synchronize policy that may specify permitted endpoints reachable over the internet 18A, storage encryption requirements, and certificate pin sets for connections to the Al platform 14A. A user device 16A may select among service endpoints of the Al platform 14A by issuing health probes, measuring round-trip time, and choosing a preferred endpoint for a session while maintaining a fallback list. The user device 16A may compress payloads using a streaming compressor and may segment large uploads into fixed-size chunks that may be reassembled server-side using chunk indices and a session identifier. Administrative controls for the user devices 16 may include a signed command channel that may trigger policy refresh, cache invalidation, or client updates. The client application on a user device 16A may verify command signatures against a pinned public key and may reject out-of-order commands based on monotonically increasing sequence numbers. User devices 16 may be used to request the-10- 078474-0586924generation of, provide feedback to edit, and to use software applications automatically (e.g., with no or limited human intervention) generated with the system 12A.

[0047] Certain embodiments may include an application development system 12A that coordinates generation and evolution of applications from natural -language objectives. A controller 20A may obtain a mission, determine context, and cause other subcomponents to perform analysis and synthesis in a (pre-defined or dynamically determined) sequence. Document ingest 22A may access enterprise sources, receive files, and extract text, tables, and images while recording provenance so later steps can determine what was processed and when. A UI planner 24A may interpret tasks, decompose them into sub-missions, determine user interface components, and select a layout that adapts to client constraints and data needs.

[0048] A UI generator 26A may obtain plans from the UI planner 24A and cause executable code, event handlers, and bindings to be produced so components render and interact. Domain specific agents 28A may supply sector knowledge for areas such as finance, energy, or engineering, and task specific agents 30A may perform focused operations such as rule extraction, validation, image analysis, search, or calculations. The controller 20A may determine which agents to invoke, obtain intermediate outputs, and pass results to the UI generator 26A, so the interface reflects current analysis.

[0049] A runtime 32A may execute the resulting generated application, manage sessions, and access data and models according to policy. During operation, the runtime 32A may receive inputs from user devices 16 and from the enterprise data repositor 15 A, obtain services from agents of platform 14A, and cause updates to views and workflow state. A feedback module 34A may collect user comments and telemetry, determine requested modifications, and return those to the controller 20A so components, layouts, and logic can change while preserving session context.

[0050] In some deployments, the controller 20A may execute a process described with reference to figure 2 to coordinate these subcomponents. The controller 20A may determine ordering, handle errors and retries, and cause re-planning when objectives change. By obtaining missions, accessing content, determining layouts, invoking agents, and updating generated code through the runtime 32A and feedback module 34A, the application development system 12A may provide adaptation from intake through delivery and use.-11- 078474-0586924

[0051] Document ingest 22A may receive human-readable and machine-readable materials and cause them to be parsed into structured or semi-structured (e.g., structured data containing unstructured content in fields) outputs. The module may obtain files from the enterprise data repository 15A and user devices 16, including PDFs, word processing files, spreadsheets, emails, images, scanned forms, database query results, and wiki pages. For unstructured text, the module may apply language models to segment sections, determine headings, extract lists and tables, and identify relationships stated in natural language. For scanned or image-only sources, the module may apply optical character recognition to recover text and layout, and may preserve page, paragraph, and coordinate references so later stages can locate cited content. The module may normalize encodings, remove artifacts, and record provenance so downstream components can determine when and how a given record was created.

[0052] The module 22A may apply named-entity extraction to determine people, organizations, devices, materials, locations, dates, amounts, and citations, and may link those entities to internal identifiers or external references. The module 22A may determine candidate rules expressed in text by detecting modal statements, obligations, thresholds, conditions, and actions, and may map such statements into predicate form with fields, operators, and values. When tables appear, the module 22A may determine header rows, units, and key columns, and may extract rows into records while preserving source references. When multiple documents pertain to the same subject, the module 22A may align terms, detect duplicates, and resolve conflicts according to policy. Tn some deployments, document ingest module 22A may cause a knowledge graph to be updated with nodes for documents, sections, figures, and extracted entities, with edges that reflect containment, citation, and semantic relations.

[0053] Models tailored for tabular data in module 22A may receive documents that contain grids, implicit tables, or key-value layouts and determine structure before extracting values. A layout analysis stage may obtain line geometry, whitespace, and alignment cues to determine rows, columns, and merged cells even when borders are faint or absent. The models may classify header regions, detect multi-row or multi-column headers, and determine header hierarchies so each data cell can inherit the correct labels. When tables mix text, numbers, and units, the system may normalize formats, parse ranges and inequalities, and associate units found in header lines or footnotes with the correct fields. Optical character recognition may run with table-aware decoding-12- 078474-0586924so characters near ruled lines are not split or joined incorrectly, and confidence scores may propagate to each field for later review.

[0054] Some embodiments may convert the table into a machine-parsable object that captures cell coordinates, spanning, header bindings, and data types. A relation inference step may determine keys, foreign keys, and groupings by analyzing repeated label patterns, subtotal rows, and sorting indicators. When a table includes embedded references or footnotes, the model may resolve them and cause the referenced text to attach to the correct rows or columns. For semi-structured layouts such as stacked key-value lists, the system may determine pairs and collections, align them to a field schema, and emit consistent records across pages. Postprocessing may detect outliers, fill down repeated labels, and reconcile totals, producing structured rows with provenance that downstream components can queryjoin with other sources, and evaluate against rules.

[0055] Certain embodiments may obtain a document and determine candidate rules (or other structured or semi-structured fields) by segmenting text into sections, clauses, and conditions, then classifying each span for modality such as obligation, prohibition, permission, or recommendation. A parser may detect triggers, subjects, actions, thresholds, time limits, locations, parties, and exceptions, and may resolve cross-references to other sections or incorporated standards. The pipeline may normalize quantities and units, map defined terms to entity types, and detect scope statements that limit when a rule applies. When the document includes tables or figures, the system may bind table fields and diagram annotations to the nearest clauses so numeric limits and labeled components appear as rule parameters rather than free text. Each extracted rule or other field may include provenance to page, paragraph, and coordinate ranges so later stages can present citations and request review.

[0056] A machine-readable version of an extracted rule may appear as a structured object that encodes conditions and outcomes. The representation may include a predicate with fields, operators, values, and comparators; a context block with parties, dates, and applicability; and an exceptions list with their own predicates. The system may store rules in a form such as JSON, a declarative policy language, or a graph of nodes and edges that represent conditions, joins, and actions. Confidence measures may attach to each field, with links to the underlying text and any resolved definitions. During evaluation, the platform may obtain evidence records, join them to-13- 078474-0586924required fields, and determine pass, fail, or not-determined, while recording which inputs produced which result.

[0057] Some rules or other fields may not admit a complete structured form because they use terms such as reasonable, adequate, clear and conspicuous, or material. For such clauses, the system may create a hybrid record that includes structured predicates for the objective elements and a naturallanguage subrule for the subjective element. The structured portion may check that a disclosure appears near a claim, that a font size exceeds a threshold relative to body text, and that a required term is present. The natural -language subrule may include the original clause text, a rubric, and references to examples drawn from the same document or a cited standard. A language model may receive the evidence, the rubric, the clause text, and any relevant context, determine a score and short justification, and return a result with citations to input passages or images used in the assessment.

[0058] As an example, a marketing policy may state that a disclosure must be clear and conspicuous, in proximity to the specific claim, and not obscured. The module 22A may encode proximity and size as structured checks while leaving clear and conspicuous as a natural -language subrule evaluated by a model guided by a rubric that lists readability, placement without scroll, contrast, and absence of competing elements. The evaluation may cause a combined result that shows structured checks as pass or fail and the subjective check as a score with a rationale, each linked to page regions and evidence. In some embodiments, module 22A may generate machineparsable rules from natural language, evaluate objective elements deterministically, and apply controlled language-model judgments to subjective elements while maintaining an auditable trail from input text to final determination.

[0059] For images and diagrams, document ingest module 22A may apply computer vision models configured for technical content. The module may determine diagram type and apply detectors tuned for symbols and notations used in engineering drawings, including mechanical assemblies, piping and instrumentation diagrams, single-line electrical diagrams, printed circuit layouts, and network topologies. The models may identify components such as valves, pumps, bearings, gears, transformers, breakers, chips, connectors, and ports, and may determine links such as wires, traces, pipes, shafts, and buses. The system may infer attributes from standard legends and callouts, obtain-14- 078474-0586924units from scales, and derive connectivity graphs that capture nodes, edges, and directions of flow. When diagrams contain embedded text, the module may use optical character recognition guided by detected bounding boxes to associate labels with specific components.

[0060] Computer vision models (like convolutional neural networks or vision transformers) may receive an engineering diagram and determine symbols, connections, and labels that together define a machine-parsable representation. A detector may localize symbols and reference marks using convolutional or transformer-based architectures adapted for line drawings rather than natural scenes. The model may learn thin-line and high-contrast features by using kernels and positional encodings tuned to strokes, hatches, and schematic icons. A segmentation head may separate foreground ink from background noise and recover layers such as wires, pipes, traces, and mechanical boundaries. A text detector may identify label regions and an optical character recognition module may recover strings, units, and part identifiers. The system may apply a line and junction extractor to determine polylines, comers, tees, and crossings so later stages can resolve connectivity.

[0061] Some embodiments may tailor detectors to symbol vocabularies specific to a domain. A mechanical workflow may prioritize bearings, gears, valves, pumps, shafts, and fasteners, while an electrical workflow may emphasize breakers, transformers, single-line symbols, and bus bars, and a printed circuit workflow may focus on pads, vias, packages, and traces. The detector may use anchors and priors that reflect canonical aspect ratios and orientations found in each symbol set, and may augment training with rotations, small skew, scan artifacts, and partial occlusions that occur in photocopies. A two-stage approach may first identify coarse regions, such as panels or legend boxes, and then apply fine-grained classifiers to distinguish visually similar symbols that differ by a small glyph or tick. When symbol families share shapes, a metric-learning head may separate them using label context, nearby text, and surrounding connections.

[0062] Some embodiments may pair detection with vectorization so lines and arcs become parametric primitives. A differentiable vectorization step may fit segments and curves to the segmentation map and may assign confidence to each primitive. A topology solver may then assemble a graph by snapping symbol ports to nearby line endpoints and by resolving ambiguous crossings using learned cues and simple tests such as path continuity and layer priority. A graph-15- 078474-0586924neural network may refine this assembly by passing messages along candidate edges and determining which endpoints should connect, which edges carry flow, and which nodes serve as sources, sinks, or junctions. The model may determine attributes by linking OCR (optical character recognition) text to the nearest symbol or edge, guided by arrows, leaders, and callout boxes that a relation detector identifies.

[0063] Variations may combine learned models with light rules to incorporate standards that appear in engineering drawings. A rules engine may enforce that certain symbols must expose a fixed number of ports or that certain connections cannot occur in a given standard. The system may determine units from title blocks and legends and may normalize values found in callouts. Template priors may accelerate recognition in recurring subcircuits or repeated mechanical subassemblies; the detector may propose matches and a verifier may confirm them by overlay alignment. When training data is limited, the models may benefit from synthetic diagrams generated from symbol libraries and procedural routers that create plausible networks, with fonts, noise, and compression artifacts applied to match scanned documents.

[0064] Some embodiments may operate in a multimodal regime where a vision model and a language encoder exchange features. The model may read a legend to learn symbol meanings for a specific drawing and then condition detection on that local vocabulary. A captioning head may produce short descriptions, such as “three-phase transformer to main bus,” that help align detected structure with known patterns. During postprocessing, the module 22A may determine a bill of materials from symbol counts, a connection table from the graph, and parameter tables from text- linked attributes. Each record may carry source coordinates and confidence so downstream components can present citations and request human review when needed.

[0065] Processing may proceed in stages so partial outputs remain useful. The module 22A may first cause a coarse graph with major components and primary paths, then refine ports, polarities, and part numbers, and then determine flow direction using arrowheads and conventions. When ambiguity persists, the model may generate alternatives and rank them using global consistency, such as whether a circuit can supply power from a declared source to all declared loads, or whether a mechanical loop closes without impossible overlaps. Some embodiments of module 22A may-16- 078474-0586924output structured records that describe components, connectivity, attributes, and derived rules that downstream agents can evaluate and present.

[0066] Document ingest module 22A may combine these analyses to produce structured records, such as executable rules, parts inventories, or the like. The module may output machine-parsable objects that describe entities, attributes, relationships, table rows, diagram components, and connectivity, and may emit rule expressions that downstream agents can evaluate against other documents or data sets. Each output record may include source locations and confidence measures so later stages can determine reliability, request review, or trigger reprocessing. In some embodiments, document ingest 22A may cause unstructured inputs to become structured representations that other components can query, validate, and use during application generation and runtime. In some embodiments, document ingest module 22A may use retrieval augmented generation techniques to search the repository 15 A, for instance with keyword search, vector search, or the like to identify content relevant to a particular domain for ingest.

[0067] In some embodiments, document ingest module 22A may obtain a set of domain documents and cause a domain-specific portion of a knowledge graph to be created or updated. For a compliance workflow, the module 22A may receive policy manuals, guidance letters, and annotated examples, extract defined terms, roles, thresholds, and deadlines, and determine relationships among rules, exceptions, and cited standards. The module 22A may emit nodes for documents, sections, clauses, tables, and figures, and edges that represent containment, citation, dependency, and semantic links such as applies-to, supersedes, or exception-to. Each node and edge may include provenance to page ranges and coordinates, confidence scores, and effective dates so later processes can determine lineage and resolve conflicts. When evaluation documents arrive, such as marketing assets or customer notices, the module may extract claims and disclosures and attach them to the relevant rule nodes, which allows downstream agents to obtain all evidence tied to a rule when determining compliance.

[0068] In some cases, document ingest module 22A may receive engineering design packets such as single-line diagrams, mechanical drawings, and parts lists, determine components and connections, and update a graph that captures equipment, attributes, and connectivity. The module may create nodes for transformers, breakers, pumps, shafts, bearings, nets, and signals, and edges-17- 078474-0586924for electrical, mechanical, or fluid links with direction and capacity. OCR results and callouts may populate attributes such as ratings, tolerances, setpoints, and materials. The module may link components to vendor datasheets and maintenance logs already present in the repository and may attach rule expressions that derive from safety codes or internal standards. In some embodiments, the knowledge graph may reflect a live view of the domain where documents supply facts, diagrams supply structure, and rules attach to the affected entities, helping later agents to access the graph to answer questions, generate views, and evaluate conformance.

[0069] Certain embodiments may include a UI planner 24A that receives a mission description or other objective, determines domain and user context, and produces a plan that the UI generator 26A can render (or generate code that when executed causes rendering of a UI) and wire to data and agents. The planner 24A may access policies, themes, and persona settings from tenant administration and may obtain constraints such as device class, screen size, latency targets, and data locality. The planner 24A may interpret the mission using a reasoning model that identifies tasks, inputs, outputs, and review points, then determine the sequence of views and interactions that satisfy those tasks. In some cases, the planner 24A may reuse prior solutions by retrieving similar missions and adapting their layouts and bindings to current requirements.

[0070] The planner 24A may decompose a mission into view models, component requirements, and data contracts. A view model may describe what a screen shows, what actions a user can take, and what evidence or results must appear. Component requirements may define the set of widgets, such as file uploaders, tables, charts, forms, galleries, graph visualizations, and result panels, while data contracts may define the fields, types, and provenance needed by those widgets. The planner 24A may determine which task-specific or domain-specific agents can supply each contract, may assign agents to components, and may define the order of calls and caching behavior so responses arrive predictably. For compliance and engineering workflows and the like, the planner 24A may include slots for citations, rule explanations, diagram callouts, and relevance scores and may ensure that each slot receives coordinates or references to the source material.

[0071] The planner 24A may select and arrange components using a mix of learned and constraint- driven methods. A layout solver may accept constraints that cover alignment, spacing, minimum readable sizes, and touch targets, then produce a grid or flex layout that adapts across breakpoints.-18- 078474-0586924A content negotiation step may determine responsive variants for each component so dense tables collapse into cards, chord diagrams switch to lists when space is limited, and force-directed graphs add overview and focus panels on large displays. The planner 24A may evaluate alternatives under objective functions that consider readability, scannability, interaction cost, and evidence visibility, and may choose a layout that satisfies those constraints with margin for localization and accessibility. The planner 24A may also assign stable identifiers to components and data bindings so the runtime 32A can hot-swap code without losing session state (or some embodiments may not hot swap versions).

[0072] The planner 24A may generate interaction logic as state machines and event specifications. A state machine may define idle, loading, success, and error states for each view and may specify transitions on events such as submit, select, expand, and filter. Event specifications may include handler signatures, expected payload shapes, debounce or throttle hints, and retry policies. For data-binding, the planner may produce queries and selectors that pull from caches, agent outputs, and repository fetches, and may include guards that check permissions and redaction rules before rendering sensitive values. Where subjective assessments appear, the planner may include a rubric slot that the runtime passes to a language model, and a scoring slot that captures the model’s output with a justification and links to cited passages or image regions.

[0073] The planner 24A may tailor outputs for accessibility and auditability. Accessibility directives may set landmarks, roles, keyboard paths, color contrast targets, and alternatives for canvas-based charts. Audit directives may require that every displayed result carries a provenance link back to a page range, a diagram region, or an agent call with parameters. When conflicts or low-confidence fields appear, the planner may insert review widgets that allow a user to request clarification, view competing interpretations, or trigger reprocessing with different parameters. The planner 24A may also determine telemetry points so the feedback module 34A can collect interaction data and error traces without capturing sensitive content.

[0074] The planner 24A may interact with the controller 20A and an orchestrator of agents during both planning and runtime. During planning, the controller 20A may obtain the mission and constraints, the planner may propose a draft plan, and the controller may request validation from domain agents that check feasibility and compliance. During runtime, the planner 24A may emit-19- 078474-0586924incremental updates when user feedback indicates a layout change or when a data contract changes, and the controller 20 A may cause the UI generator 26 A to apply those updates as patches. The planner 24A may also provide fallback pathways when an agent is unavailable, including degraded views that surface partial results with clear indications of completeness.

[0075] The planner 24A may use several algorithmic variants to produce its plans. A language-to- plan model may generate a first-pass specification from the mission text, which a constraint solver may refine. A retrieval component may suggest component patterns based on similar missions, such as two-pane compare views for compliance or topology plus table for network analysis. A ranking model may score candidate layouts using signals derived from readability metrics, historical success on similar tasks, and device characteristics. For complex visualizations, the planner may run a miniature simulation that populates components with representative data to verify that labels fit, legends remain legible, and interactions complete within latency targets.

[0076] Outputs from the UI planner 24 A may appear in formats suited to the UI generator 26 A. A structured plan may be emitted as JSON that describes views, components, bindings, events, state machines, and constraints. A component tree may be emitted as an abstract syntax tree that the generator can translate to framework code. A style and theme bundle may define tokens for spacing, type scale, color roles, and motion durations. A data contract map may list endpoints, agent calls, cache lifetimes, schema versions, and provenance fields. An interaction map may specify handler stubs and middleware hooks for validation, authorization, logging, and redaction. A patch stream may represent diffs between versions so the generator can apply changes without recompiling the whole application.

[0077] For targets that favor web delivery, the planner 24A may produce a virtual DOM description or a framework-neutral intermediate representation that the generator translates into components such as React™, Web Components, or server-rendered templates. For native or hybrid targets, the planner may emit a platform-neutral structure that the generator maps to native widgets while preserving accessibility roles and navigation models. In some cases, the output may include stable identifiers and compatibility notes so existing state, focus, and scroll positions carry forward when a new version arrives.-20- 078474-0586924

[0078] The planner 24A may also emit artifacts that support development, testing, and governance. A fixture set may include small example payloads that exercise empty, typical, and edge cases for each component. A contract test specification may describe required fields and validation rules so the runtime can detect schema drift. A policy map may record where personal or confidential data appears so masking and consent prompts occur at the right time. Theme hooks may allow a tenant to apply brand settings without changing layout logic, and localization hooks may reserve space for longer strings and right-to-left scripts.

[0079] Certain embodiments may include a UI generator 26A that receives plans, component trees, data contracts, and interaction maps from the UI planner 24A and causes executable user interfaces to be produced and updated. The generator 26A may accept a structured specification that describes views, components, bindings, events, and state machines, then determine framework targets and rendering strategies. The generator 26A may translate an abstract component tree into concrete widgets, assign stable identifiers, and produce code for event handlers and data adapters so views render and respond without manual wiring. When the planner 24A supplies a patch stream, the generator 26A may apply diffs that change components, layout constraints, and bindings while preserving session state, focus, and scroll positions.

[0080] The UI generator 26A may support multiple targets and may select an appropriate output format based on deployment goals. For web delivery, the generator 26A may emit framework code such as React™ components, web components, or server-rendered templates, and may include code splitting and lazy loading for large visualizations. For native or hybrid environments, the generator may map the same intermediate representation to native widget sets while keeping accessibility roles and navigation models consistent. In some cases, the generator may use a virtual DOM (document object model) or a platform-neutral layout engine to ensure that responsive rules and content negotiation behave the same across devices. Theme tokens and localization hooks provided by the planner may be compiled into style bundles that control spacing, type scale, color roles, and motion, with runtime switches for dark mode and right-to-left scripts.

[0081] The generator 26A may implement interaction logic by materializing (e.g., generating the code for) the state machines and event specifications from the plan. Each view may have defined idle, loading, success, and error states, and transitions may be wired to user actions and agent-21- 078474-0586924responses. Event handlers may include validation, debouncing, retries, and authorization checks, and may call agent endpoints or repository connectors according to the data contracts. The generator 26A may set up data stores and selectors that hydrate components from caches and live calls, and may attach guards that prevent rendering of redacted fields. Where subjective assessments are requested, the generator 26A may deliver rubric text, evidence snippets, and image regions to the language model interface and may capture scores and justifications with provenance links.

[0082] Rendering of complex visuals may be optimized by choosing canvas, SVG (scalable vector graphics), or WebGL based on size and interactivity. The generator 26A may produce (code and assets that form) clustered graphs, force-directed graphs, chord diagrams, timelines, and topology views and may attach tooltips, focus rings, and keyboard paths that satisfy accessibility requirements. When space is limited, the generator 26A may apply responsive variants specified by the planner so dense tables collapse into cards and graphs offer overview plus detail panels. For diagram callouts and citations, the generator may embed coordinate anchors and bounding boxes so a user can jump from a rule or result to the precise page region or symbol in the source document.

[0083] Runtime safety and governance may be enforced by isolating untrusted content and restricting permissions. The generator 26 A may set content security policies, sandbox third-party widgets, and separate credentials from front-end bundles. Secrets and keys may remain in serverside services, while the client receives scoped tokens with limited lifetimes. Telemetry points defined by the planner may be instrumented to record latency, errors, and interaction patterns without capturing sensitive payloads. The generator 26A may write audit traces that connect each visible result to the agent call or repository read that produced it, including parameters and version identifiers for models and rules.

[0084] Build and update workflows may favor incremental compilation and hot replacement. The generator 26A may cache compiled templates and keep a mapping from stable identifiers to component instances so a new version can replace logic without discarding local edits or selections. When schema versions change, the generator 26A may consult the contract tests provided by the planner and may insert compatibility shims or request re-planning. If an agent is-22- 078474-0586924unavailable, the generator 26A may activate degraded views that display partial results and suggested next steps, then restore full interactions when services resume. Error boundaries may capture failures at component granularity and present clear messages with retry options rather than halting the whole application.

[0085] Testing and quality controls may be produced alongside the application code. The generator 26A may create fixtures that exercise empty, typical, and edge cases and may run snapshot and interaction tests to confirm that rendering and state transitions match expectations. Accessibility checks may verify roles, labels, tab order, and contrast before deployment. Performance budgets may be enforced by rejecting bundles that exceed size or by inserting dynamic imports where large dependencies appear. The generator 26A may emit sourcemaps, diagnostics, and lint outputs so operators can trace issues in production without exposing source.

[0086] Outputs from the UI generator 26A may include executable code bundles, server-rendered pages, or native packages, along with manifest files that describe component versions, data contracts, and provenance fields. The generator 26A may also emit a registry entry that allows the runtime 32A to discover views, routes, and permissions, and a patch artifact that expresses the delta from the prior version. For environments with strict change control, the generator 26A may produce a signed artifact that records hashes of code, schemas, and policies, enabling verification at load time.

[0087] Certain embodiments may include domain specific agents 28A that receive tasks within a known sector (or other type of domain) and return analyses, extractions, and decisions tuned to that sector’s data and practices. The controller 20A may obtain an objective, determine that it pertains to a given domain, and cause the relevant agent set to engage. Selection may use signals such as detected terminology in the objective and ingested documents, source system identifiers, file templates, and knowledge graph context. The controller 20A may query a registry of agents, obtain capability descriptors and schemas, rank candidates by domain fit and confidence, and invoke one or more agents with inputs and policies appropriate to the enterprise.

[0088] In finance, a domain agent may access account ledgers, exception files, and payment messages and determine derived features such as counterparty profiles, transaction patterns, and exception categories. Another agent may parse check images and advice files, recover routing and-23- 078474-0586924account fields, and cause validation against internal formats. A portfolio analysis agent may obtain positions, price histories, and risk factors and determine exposure summaries and limit checks that a view can present alongside citations to the underlying rows. When a marketing review objective arrives, a policy agent may extract claim and disclosure pairs from assets and apply formatting and proximity rules, while a records agent may determine retention requirements based on document class and effective dates.

[0089] In healthcare, a clinical documentation agent may receive progress notes, lab panels, and imaging reports and extract problems, medications, orders, and timelines mapped to local code systems. A quality review agent may determine whether required elements appear in discharge instructions and cause a checklist view with links to note sections and scanned pages. A trial operations agent may obtain protocol documents, visit schedules, and case report forms and determine windows, dosing rules, and adverse event triggers, then emit structured rules that downstream components can evaluate against site activity and sensor feeds. The controller 20A may pass de-identification policies to these agents and obtain outputs that preserve lineage to source pages while masking fields marked as sensitive.

[0090] In manufacturing, a product definition agent may ingest bills of materials, routings, and revision histories and determine structure, alternates, and effectivity. A quality agent may access inspection records and machine telemetry and determine defect patterns and process capability metrics, then cause a dashboard that links charts to the exact lots and stations. An engineering diagram agent may analyze single-line or mechanical drawings, detect components and links, and output connectivity graphs and part attributes that a downstream view can render as topology plus table. When a supply objective arrives, a planning agent may obtain lead times, purchase orders, and inventory snapshots and determine shortages and mitigation options with references to the originating records.

[0091] Other sectors may rely on agents with similar patterns. In energy, an operations agent may obtain SCADA (supervisory control and data acquisition) tags and alarms and determine event summaries per asset and feeder, while a maintenance agent may join work orders and condition data to recommend actions. In media, a rights agent may read contracts and metadata to determine permitted uses and expirations and cause warnings when an edit timeline includes assets that-24- 078474-0586924exceed scope. In research and education, a grants agent may parse calls, budgets, and compliance checklists and determine required submissions and dates, and a publications agent may extract authors, affiliations, and embargo terms and attach them to repository items.

[0092] The controller 20A may coordinate these agents as part of a plan from the UI planner 24A. It may determine input contracts, obtain outputs, validate schemas, and merge results so the UI generator 26A can bind components without manual wiring. When an objective spans multiple domains, the controller 20A may invoke more than one domain agent and reconcile overlaps using precedence rules and confidence scores. Agents may return structured records, rule expressions, explanations, and provenance, along with capability hints that allow re-use during runtime when a user filters, drills down, or requests a recalculation. In some embodiments, the domain specific agents 28A are expected to allow applications to present sector-correct views and actions while preserving traceability to enterprise sources. The domain specific agents may be used both during generation of a software application and during execution of the software application, for instance, as resources called by that software application.

[0093] Certain embodiments may include task specific agents 30A that receive narrowly defined jobs and return structured outputs usable across domains. The task specific agents may also be used both during generation of a software application and during execution of the software application, for instance, as resources called by that software application. The controller 20A may obtain a mission, determine the sequence of steps from the UI planner 24A, and cause one or more task agents to execute functions such as optical character recognition, table detection, named-entity extraction, de-dupli cation, data quality checks, rules evaluation, vector indexing, retrieval, summarization, translation, redaction, and unit normalization. These agents may expose capability descriptors and schemas so the controller 20A can determine inputs, outputs, and latency characteristics and may return results with provenance and confidence values that downstream components can display or review.

[0094] In a compliance workflow, document ingest 22A may pass a scanned policy to an optical character recognition agent to recover text and coordinates, then cause a table parser to extract limits and thresholds into rows, and invoke a rule extractor to emit predicate forms with exceptions. A retrieval agent may index the recovered text and figures, and a citation agent may determine-25- 078474-0586924page and region links for any later answer. When a user submits a marketing asset for review, a claim extraction agent may obtain statements and disclosures, a layout analyzer may determine proximity and contrast, and a rubric scorer may produce a relevance score and rationale that the interface displays with jump-to-source actions. The controller 20A may coordinate retries and fallbacks, such as using a secondary optical character recognition engine when confidence is low or a language model-based table reader when borders are missing.

[0095] In healthcare, a named-entity agent may map problems, medications, and procedures to local codes, while a timeline agent may order events and determine gaps relative to protocol windows. A de-identification agent may mask identifiers before any downstream visualization, and a quality check agent may determine whether required discharge elements appear. The controller 20A may enforce policies that restrict which records each agent can access and may record lineage so each checklist item links to note sections and scanned pages. When a user drills into an outlier, a summarization agent may obtain the most relevant passages and cause a short explainer to appear alongside citations and confidence scores.

[0096] In manufacturing and engineering, an image segmentation agent may separate foreground ink from background and feed a symbol detector that returns components with ports. A vectorization agent may convert lines and arcs into primitives and a topology agent may determine connectivity graphs with flow directions. A unit harmonization agent may normalize tolerances and ratings, and a comparison agent may determine differences across revisions and cause highlights in both drawing and table views. When a supply analyst requests risk, a join agent may link bills of materials to supplier data and lead times, a scoring agent may compute shortage risk, and a mitigation agent may propose substitutes with references to approved alternates.

[0097] Across finance and other sectors, task agents may include validators that check field formats, joiners that align records across systems, deduplicators that merge near-matches, and redaction agents that remove sensitive values before display. A retrieval-augmented generation agent may accept a question, access indexed sources, and return an answer with citations, while a relevance agent may score multiple model responses and cause the interface to present a consolidated result. The controller 20A may compose these agents into pipelines based on the plan, pass cache hints and timeouts, and select variants tuned for speed or accuracy.-26- 078474-0586924

[0098] Certain embodiments may include a runtime 32A that receives build artifacts from the application development system 12A and causes an application to execute for users. The runtime may obtain the component code, data contracts, event specifications, and provenance directives produced by the UI generator 26A and may determine how to load them for a given device and tenant. The runtime may manage sessions, maintain view state, and apply updates while preserving focus and scroll positions. The runtime may access the enterprise data repository 15A and agent endpoints according to policy and may enforce authentication, authorization, and redaction before rendering results.

[0099] Examples of a runtime may include a browser-based client that executes web bundles with server helpers, a native container that hosts views on desktop or mobile, and a server-side Tenderer that produces pages and streams updates to thin clients. In some deployments the runtime may operate in containers behind an API gateway, while in others it may run as serverless functions that scale with demand. The runtime may determine where to execute a step (e.g., on the client for responsiveness, on the server for data locality, or within a private subnet for sensitive calls) and may route requests over network 18A using private endpoints when available.

[0100] During operation, the runtime may obtain inputs from user devices 16, call task specific agents 30A and domain specific agents 28 A through the controller 20A, and bind outputs to views defined in the plan. State machines from the plan may drive loading, success, and error transitions, and handlers may implement validation, retries, and fallbacks. When the feedback module 34A signals a change, the runtime may apply a patch to modify components, layouts, or bindings without restarting the session. If a new version arrives, the runtime may substitute it for the prior version and cause state to carry forward so users continue without interruption.

[0101] The runtime 32A may record audit trails and telemetry without exposing sensitive content. Each displayed result may carry references to the agent call or repository read that produced it, including parameters and model versions. The runtime 32A may determine performance budgets and cause large visualizations to load lazily, may cache data according to the plan’s directives, and may invalidate caches when schemas change. In a multi-tenant setting, the runtime 32A may isolate configuration, themes, and data access, and may apply tenant policies that control which agents and repositories a session can reach.-27- 078474-0586924

[0102] Resilience features may include circuit breakers, backoff policies, and degraded modes that present partial results when services are unavailable. The runtime 32A may detect schema drift using contract tests shipped with the plan and may request re-planning or apply compatibility shims. Error boundaries may capture failures at component granularity and present clear messages with options to retry or view citations for context.

[0103] Feedback module 34A may receive natural -language (e.g., complaints or feature requests) or structured language comments (e.g., thumbs up / down, ratings from 1-5, etc.) from the user who provided an objective and from other users of the generated application. The module 34A may obtain text typed into comment fields, transcripts from voice input, and signals from in- app prompts that ask what worked and what did not. The module 34A may parse this input, determine targets such as a specific view, component, rule, or data contract, and extract intents such as add a field, change a layout, relax a threshold, or fix a failing check. When feedback refers to evidence, the module may access citations and coordinates so the controller 20A can reproduce the context. The module 34A may also accept attachments such as example documents or screenshots and may link them to the affected plan elements for review and traceability.

[0104] Feedback module 34A may also obtain programmatic signals that indicate correctness and quality. The module 34A may run unit tests and interaction tests produced with the application, verify schema contracts, and execute probes that check accessibility, performance budgets, and security guards. When a check fails or a threshold is exceeded, the module may determine which component or data contract is implicated and record a machine-parsable finding with provenance. The module 34A may aggregate human and automated feedback, prioritize items by severity and frequency, and cause the controller 20A to select a regeneration path such as re-planning a view, modifying a handler, swapping an agent variant, or updating a rule expression.

[0105] When changes are warranted, the module may direct the controller 20A to obtain an updated plan from the UI planner 24A and cause the UI generator 26A to produce a new version of the affected parts. The controller 20A may determine whether a full rebuild or a patch suffices and may request diffs that isolate only the modified components, layouts, or bindings. The runtime 32A may receive the update during the current session and substitute the new version for the prior one while preserving program state such as selected rows, filters, scroll positions, focus, cached-28- 078474-0586924results, and pending requests where safe to do so. If certain state is incompatible, the module may determine a migration map that transforms values into the shapes expected by the new code.

[0106] In some cases, the module 34A may stage the change behind a toggle or limited rollout, obtain confirmation from the reporting user, and then cause broader activation. The module 34A may capture before-and-after telemetry to verify that an issue no longer occurs and that performance remains within targets. If the update degrades behavior, the module may direct the controller 20A to revert and request a revised plan. In some embodiments, feedback module 34A may cause applications to evolve during live use without disrupting the session or cause generated applications to evolve between sessions.

[0107] A generated software application may include user interface code, server logic, data contracts, models, and supporting assets that the application development system 12A obtains from plans and causes to be built. The application may present views, accept inputs, call agents, and display results with citations and provenance. The build may include artifacts such as executable bundles, configuration files, schemas, test fixtures, release notes, and deployment manifests. Visual assets may include icons, diagram callouts, and images that a diffusion model produces from prompts, with alt text and usage licenses attached for audit. Code artifacts may include handlers, state machines, policy checks, and adapters that connect to enterprise systems.

[0108] Some deployments may generate a monolithic executable that contains both front-end rendering and back-end logic. The system 12A may package a single binary or archive that embeds templates, routing, and data access layers, and may run it as a service or a desktop app. The monolith may expose an internal API (application program interface) for user devices 16 while keeping data processing local to a secure subnet, and may log lineage and metrics to enterprise stores. In other cases, the build may produce a front end and a back end as separate deliverables. The front end may render UI components in a browser or a native shell and may call the back end over network 18A using endpoints that the plan defines. The back end may host rules evaluation, retrieval, and aggregation, and may cache responses according to policy.

[0109] Client-side only variants may load a static bundle that runs entirely in the browser or a native Web View. The bundle may obtain data from the enterprise data repository 15A through preauthorized gateways, apply rules in the client, and render graphs and tables without a dedicated-29- 078474-0586924server. This mode may suit read-heavy tasks or environments where server compute is restricted. Web application variants may include server-rendered pages, single-page applications, or hybrid models that stream updates. Native application variants may target desktop or mobile and may package compiled code with platform widgets, offline stores, and background tasks, while preserving accessibility roles and navigation models defined in the plan.

[0110] Packaging may vary by target. The system 12A may emit container images with health checks and resource limits, archives with hashed assets and content security policies, signed desktop installers, or mobile packages for internal distribution. The build may include sourcemaps and diagnostics for operators, and signed manifests that record versions of models, rules, and schemas. For rich visuals, the artifacts may include prerendered thumbnails, sprite sheets, and font subsets that improve load time. For generated images, the package may include prompt metadata and license terms so reviewers can trace sources and approve usage.

[0111] Hosting may occur inside the enterprise data repository 15A or services adjacent to it. Source code and manifests may reside in a code repository, and compiled bundles may appear in a binary registry. Desktop and mobile packages may be published to an internal application store, while web bundles may deploy to a private content delivery path with access controls. The repository may retain prior versions and signatures so the runtime 32A can verify integrity and roll back if needed. In some environments, an external app store may distribute approved builds with enterprise provisioning, and the repository 15A may keep the corresponding source and evidence for audit.

[0112] In some embodiments, the generated application may preserve stable identifiers for components and data bindings so updates can substitute a new version during a session without losing context. The packaging may carry policy files that define permissions, redaction, telemetry, and retention. In some embodiments, the system 12A may cause applications to be deployed, updated, and reviewed in a manner that aligns with enterprise controls.

[0113] Certain embodiments may include a dashboard generator 36A that receives telemetry and build artifacts from the application development system 12A and causes a dashboard to be presented with usage, version identifiers, resource consumption, and other statistics for generated applications. An example is shown in figure 9 and described below. The dashboard generator 36A-30- 078474-0586924may obtain counts of active users and sessions, request rates to domain specific agents 28A and task specific agents 30A, cache hit ratios, latency percentiles, error types, and peak memory or CPU during generation and runtime. The component may determine lineage for each metric to the underlying plan, model version, and data contract and may present charts, tables, and timelines that allow an operator to filter by tenant, application, version, or environment. In some cases, the dashboard generator 36A may access cost and quota data and cause rollups that show the resources consumed by specific plans, agents, or visualization types.

[0114] The dashboard generator 36A may also serve as an interface by which a user requests updates and submits feedback. A user may select an application and a version, enter naturallanguage comments, attach example documents, and request a change such as adding a field, modifying a workflow, or upgrading an agent variant. The dashboard generator 36A may obtain these inputs, determine the affected views and contracts, and direct the controller 20A to initiate regeneration or to create a new copy of the application. Where safe, the controller 20A may cause a patch that substitutes the updated version during active sessions while preserving state, and the dashboard may display the status of that substitution along with any migration steps that were applied.

[0115] Feedback may also arrive from within the applications themselves, and the dashboard generator 36A may obtain those signals from the feedback module 34A and present them alongside automated checks such as unit tests, contract tests, accessibility probes, and performance budgets. The dashboard may determine priorities based on severity and frequency and may cause notifications to owners when thresholds are crossed. Administrative users may access controls that schedule rollouts, pin versions, or revert to a prior build, with the dashboard writing audit records of requests, approvals, and outcomes. By receiving telemetry and comments, presenting actionable context, and coordinating with the controller 20A and feedback module 34A, the dashboard generator 36A may provide a central place to observe generated applications and to request and track changes.

[0116] Certain embodiments may host Al agents within the Al platform 14A and expose their capabilities to the application development system 12A through defined interfaces. The controller 20A may obtain an objective, determine that one or more agents pertain to the domain and tasks-31- 078474-0586924at hand, and cause those agents to execute on the Al platform 14A. In other deployments, a generated software application may call Al agents 42 of the Al platform 14A directly at runtime to obtain extractions, summaries, classifications, or code updates. The same planning and data- contract logic may govern these calls so inputs, policies, and provenance travel with each request, and so outputs appear in the views that the UI generator 26A renders (e.g., generates code that when executed causes a client to render). Selection of where an agent runs may depend on tenant policy, data locality, latency targets, and cost, and the controller 20A may determine whether to route a call to an agent co-located with enterprise resources or to a shared, multi-tenant instance on the Al platform 14A.

[0117] Al models 42 used by agents may execute on remote infrastructure while still being treated herein as part of the component that calls them. For instance, an agent wrapper may receive a request, apply redaction and validation, forward inputs to a hosted model over a secure channel, obtain outputs, attach model and policy versions, and return a structured result under the agent’s contract. From the perspective of this specification, the agent that performs these steps remains the executing component even when inference occurs on remote hardware, because the calling agent controls preprocessing, invocation parameters, postprocessing, and delivery of machine-readable results with provenance. This arrangement may allow the application development system 12A and the Al platform 14A to swap or version models without changing plans or bindings, and may allow the runtime 32A to obtain auditable, reproducible outputs while keeping sensitive data within approved boundaries.

[0118] In some embodiments, the Al platform 14A may include an orchestrator 40 and multiple artificial intelligence models 42 exposed through service interfaces. The orchestrator 40 may accept incoming requests that may include a system prompt, a content prompt, and decoding settings, may parse routing metadata such as model identifiers and stage assignments, and may dispatch the request to one or more artificial intelligence models 42 according to configured policies. The orchestrator 40 may maintain per-request context such as correlation identifiers, may apply rate limits and batching rules, and may sequence multi-stage flows by issuing a series of sub-requests in which outputs from earlier stages may be transformed and forwarded to later stages. The orchestrator 40 may record request and response metadata, may handle retries on-32- 078474-0586924transient failures, and may expose streaming or non-streaming response modes to downstream consumers.

[0119] In some embodiments, each artificial intelligence model 42 may provide an inference endpoint that may accept prompt text and decode settings and may return a response payload containing generated text and optional auxiliary data such as token counts, per-token scores, or tool-call traces. The artificial intelligence models 42 may represent distinct model families or versions and may support configurable decoding parameters, schema guards, and resource controls communicated by the orchestrator 40. The models 42 may support reuse of intermediate state across related requests within time windows, may emit partial results during generation when requested, and may surface diagnostics that may be persisted by the Al platform 14A. The Al platform 14A may maintain registrations for available artificial intelligence models 42, may publish their capabilities to the orchestrator 40, and may provide administrative interfaces through which configurations and stage definitions may be updated without interrupting request handling.

[0120] By way of example, the illustrated Al platform 14A has four artificial intelligence models 42 in figure 1A for clarity, while other embodiments may register substantially more models and versions at once (e.g., more than 10, more than 100, more than 1,000, or more than 10,000). The platform may maintain a catalog in which each model 42 may advertise input and output modalities, supported decoding settings, schema constraints, resource limits, and health status. A controller or orchestrator 40 may read this catalog to select one or more models 42 for a request, while administrative interfaces may allow models to be added, disabled, or rolled back without interrupting service. Models 42 may be addressed individually or through stage aliases so that a pipeline may target a class of models rather than a single identifier, which may allow staged migrations and A / B splits across a fleet.

[0121] In some embodiments, models 42 may be trained separately or together. Separate training may proceed in its own pipeline per model: ingest training data, prepare batches, run forward passes, compute a loss signal according to the task, and apply parameter updates; checkpoints may be exported and registered when validation may meet a gate. Concurrent training may coordinate two or more models in a shared loop, for example by distilling a teacher model into a smaller student, by alternating updates between a retriever and a generator, or by sharing-33- 078474-0586924embeddings that may be updated jointly. A scheduler may partition accelerators across jobs, synchronize checkpoints at specified intervals, and write model cards that may summarize training data windows, hyperparameters, and evaluation scores so that the orchestrator 40 may route requests only to models that may satisfy deployment policy.

[0122] In some embodiments, the models 42 may be multimodal and heterogeneous. A text model may accept prompts and emit text; a vision model may accept images and emit labels, captions, or embeddings; an audio model may accept waveforms and emit transcripts or speaker turns; and a diffusion model may accept text and emit images through iterative sampling. Cross- modal adapters may be registered to convert outputs from one model to inputs for another, for example mapping an image encoder’s embedding to a language model’s hidden space, or converting a transcription into a structured prompt template. The orchestrator 40 may attach these adapters based on a stage definition so that heterogeneous models may be composed into a single request path.

[0123] In some embodiments, a large language model may be represented as a sequence model that may consume tokenized prompts and, during inference, may maintain a working cache of intermediate state while producing the next token repeatedly until a stop rule may be met. Training such a model may follow a loop that may read batches of token sequences, run forward passes to predict the next token at each position, compare predictions to references to compute a loss, and update parameters; fine-tuning may continue this loop on domain examples, and instruction-tuning may add formatting and constraint-following demonstrations. Decoding at inference may be controlled by settings such as sampling temperature, nucleus probability, maximum tokens, repetition penalties, and stop sequences, all of which the Al platform 14A may apply per request.

[0124] In some embodiments, a state space model may process long sequences by maintaining a compact state that may be advanced with each new input chunk. During inference, the model may read a segment of tokens or features, update its state using learned transition operators, and emit outputs for that segment; this segment-wise procedure may allow long contexts to be processed in a streaming manner. Training may sweep over long sequences with sliding windows, apply teacher forcing for stability, and update parameters based on a prediction or reconstruction-34- 078474-0586924objective. The platform may expose the state as an opaque handle so that later stages may continue from the same point without reprocessing earlier chunks.

[0125] In some embodiments, a diffusion model may synthesize or transform images by iteratively refining a noisy representation. During inference, the model may start from noise seeded by a sampler, and in a fixed number of steps may apply learned denoising updates that may be conditioned on a text prompt or an image reference. Schedulers may determine step sizes, and guidance scales may adjust adherence to conditioning. Training may proceed by adding noise to ground-truth images at sampled levels, asking the model to predict the noise or a denoised target, and updating parameters to reduce the prediction error. The platform may wrap this procedure behind an endpoint that may accept text, images, and control hints such as masks or edge maps.

[0126] In some embodiments, a computer vision model may perform classification, detection, segmentation, or optical character recognition. During inference, the model may read an image tensor, compute hierarchical features with convolutional or attention-based blocks, and emit class probabilities, bounding boxes, masks, or extracted text. Post-processing may apply thresholding and non-maximum suppression. Training may construct batches with augmentations, run forward passes to produce predictions, compute task-specific losses, and update parameters. The platform may expose pre-processing and post-processing steps as configurable handlers so that outputs may be normalized before being passed to later stages.

[0127] In some embodiments, a reinforcement learning model may interact with an environment to learn a policy. During training, the agent may observe a state, choose an action according to a policy network, receive a reward, and update the policy and, for example, a value estimator using stored trajectories. Curriculum schedules may adjust difficulty, and off-policy replay buffers may stabilize updates. During inference, the agent may read state representations and emit actions without parameter updates. The platform may host simulators or connect to external environments, and may expose the policy behind an endpoint that may accept state observations serialized from an upstream stage.

[0128] In some embodiments, additional model classes may be registered, including retrieval models that may rank passages, speech models that may perform recognition or synthesis, program synthesis models that may emit code, and graph models that may reason over structured relations.-35- 078474-0586924Each model 42 may define supported settings, pre-processing contracts, and output schemas, and the Al platform 14A may route requests so that heterogeneous and multimodal components may be composed into a single flow, whether four models are displayed in a figure or many more may be present in a deployment.

[0129] In some embodiments, the orchestrator 40 may coordinate the plurality of artificial intelligence models 42 by accepting a request that may include a system prompt, content prompt, context references, and routing hints, constructing an execution plan, and issuing a sequence of sub-requests to selected models 42 according to that plan. The orchestrator 40 may parse the incoming payload, may attach correlation identifiers, and may initialize a call graph that may record nodes for planned model invocations and edges for data dependencies among those nodes. The orchestrator 40 may evaluate routing policy to choose model 42 identifiers for each node, may assign decoding settings per node, and may submit sub-requests in an order that may satisfy data dependencies. As responses may arrive, the orchestrator 40 may extract artifacts such as generated prompts, structured fields, embeddings, or tool traces, may normalize these artifacts to a shared interchange format, and may inject them as inputs to downstream nodes in the call graph.

[0130] In some embodiments, the orchestrator 40 may implement agentic workflows by hosting a reasoning component that may construct and revise a plan at runtime. The reasoning component may run inside the orchestrator 40 or may be implemented as one of the artificial intelligence models 42. The reasoning component may ingest the user objective and available tools, may propose a sequence of steps that may reference specific model 42 capabilities, and may emit a plan object containing steps, branching conditions, and data mappings. The orchestrator 40 may execute the plan by iterating steps: submit a call to the designated model 42 with a prompt assembled from the current context; await a response; evaluate guard conditions expressed as simple checks or scoring functions; and either proceed to the next step, branch to an alternate step, or request a plan update from the reasoning component when checks may not pass. The orchestrator 40 may maintain a working memory that may store intermediate prompts and outputs and may serialize that memory so that later steps may reference earlier results without repeating prior calls.

[0131] In some embodiments, the orchestrator 40 may route requests among heterogeneous models 42 and may transform outputs from one model into inputs for another. For example, a-36- 078474-0586924vision model may emit a caption and detected entities that the orchestrator 40 may combine with a system prompt for a language model to generate a report; a retrieval model may emit passages and scores that the orchestrator 40 may attach as context to a question-answering prompt; a diffusion model may produce an image that the orchestrator 40 may pass to a second vision model for safety checks before releasing to a caller. These transformations may be expressed as adapters that may map fields, add delimiters, or enforce schema guards. The orchestrator 40 may also support fan-out and fan-in patterns in which a step may branch to multiple models 42 in parallel with varied prompts or settings, followed by an aggregation step that may select or merge the results using rules or a separate evaluator model.

[0132] In some embodiments, the orchestrator 40 may incorporate quality evaluation during execution. The orchestrator 40 may attach evaluators that may score intermediate responses against label functions, constraint checkers, or comparison heuristics, and may record those scores with the call graph. If a score may fall below a configured bound, the orchestrator 40 may trigger a retry with adjusted settings, may select an alternate model 42, or may ask the reasoning component for a revised plan. The orchestrator 40 may maintain stop conditions such as reaching a target score, exhausting a model 42 list, or hitting a budget of calls, and may terminate the workflow when a stop condition may be met. Final outputs may be assembled from nodes designated as sinks in the call graph and may include generated text, structured records, and provenance that may list model 42 identifiers, prompts, and settings used.

[0133] In some embodiments, prompt composition inside the orchestrator 40 may be dynamic. A node may specify a template whose placeholders may be filled with values produced by upstream nodes, such as inserting extracted fields, reformatting tables, or embedding citations. The orchestrator 40 may construct prompts by concatenating a system prompt and a content prompt and may append context documents or summaries. When a node may receive a prompt produced by another model 42, the orchestrator 40 may sanitize and tag that prompt before forwarding. Settings may be assigned per node by reading defaults from a registry and overriding fields such as sampling temperature, nucleus probability, maximum tokens, stop sequences, or schema constraints according to plan hints or evaluator feedback.-37- 078474-0586924

[0134] In some embodiments, the orchestrator 40 may operate in synchronous or asynchronous modes. In synchronous mode, the orchestrator 40 may execute the call graph inline, awaiting each dependency before advancing. In asynchronous mode, the orchestrator 40 may submit independent nodes concurrently, may await completion events, and may resume dependent nodes as their inputs may become available. The orchestrator 40 may record a timeline of submissions and completions, may propagate cancellation if a branch may become irrelevant, and may checkpoint the call graph so that long-running workflows may resume after transient failures.

[0135] In some embodiments, planning may be performed once at the start or iteratively. A one-shot plan may be constructed from the initial request and executed as written. An iterative plan may be updated after each major step: the orchestrator 40 may solicit a new plan from the reasoning component by providing a compact summary of the current state, including successes, failures, and evaluator scores; the reasoning component may propose additional steps, altered branches, or revised prompts; and the orchestrator 40 may apply the update to the running call graph. This arrangement may allow branching based on responses, repeated refinement cycles, or backtracking when an approach may not meet checks, while keeping the flow grounded in explicit calls to the artificial intelligence models 42.

[0136] In some embodiments, the orchestrator 40 may expose administrative controls to register models 42, attach adapters, define plan templates, and configure evaluators and thresholds. The orchestrator 40 may log prompts, settings, responses, and decisions with identifiers so that replay or audit may be performed, and may export summarized traces to downstream systems. The orchestrator 40 may support staged deployments in which only a fraction of traffic may exercise new plans or model 42 versions, with the remainder using prior configurations, and may switch traffic based on observed evaluator scores.

[0137] In some embodiments, an orchestrator 40 may perform retrieval-augmented generation by constructing context to pair with prompts submitted to artificial intelligence models 42. The orchestrator 40 may parse an incoming request, extract queryable terms or entities, and issue retrieval calls to one or more backends such as vector indexes, structured databases, key-value stores, or web connectors. For vector retrieval, the orchestrator 40 may compute or request an embedding for the request, search a vector database for nearest neighbors under a configured-38- 078474-0586924similarity rule, and fetch the corresponding passages together with metadata such as source identifiers and timestamps. For structured retrieval, the orchestrator 40 may prepare parameterized SQL statements that may target tables designated for facts, events, or configurations, and may execute the statements with bound parameters to obtain rows that may be normalized into text spans or key-value fragments. The orchestrator 40 may merge results across sources by deduplicating near-identical passages, ranking candidates using a learned or heuristic ranker, and assembling a context window that may satisfy token budgets and policy filters prior to pairing the context with a system prompt and content prompt.

[0138] In some embodiments, the orchestrator 40 may call an artificial intelligence model 42 to assist with retrieval by generating or refining queries. The orchestrator 40 may submit a step that asks the model 42 to produce search strings, embeddings, or structured queries given the user objective and available schema hints. For example, the model 42 may emit a vector-query description, a set of Boolean search clauses, or a parameterized SQL statement. The orchestrator 40 may validate and sanitize the proposed query, may execute it against the configured backend, and may return retrieved snippets to the model 42 for summarization or citation selection. The orchestrator 40 may iterate this loop by asking the model 42 to propose follow-up queries for uncovered aspects, may expand abbreviations or entity aliases, and may re-rank retrieved items based on the model’s extracted signals such as answerability or freshness labels.

[0139] In some embodiments, the orchestrator 40 may construct the final context pack by chunking documents to configured sizes, adding canonical citations, and applying filters that may remove low-confidence or stale items. The orchestrator 40 may enforce per-source quotas so that no single repository dominates the context, may insert guardrail headers that may list provenance and usage instructions, and may compress or summarize overlong passages using a model 42 prior to assembly. The resulting context pack may be concatenated ahead of or after a system prompt and content prompt and may be sent with decoding settings to a selected model 42. The orchestrator 40 may cache embeddings, intermediate search results, and assembled context packs keyed by request fingerprints so that repeated or related requests may reuse retrieval results within defined lifetimes, and may align the context layout to maximize shared prefixes across related prompts where reuse of a key-value cache on the target model 42 may be supported.-39- 078474-0586924

[0140] In some embodiments, the one or more artificial intelligence models 42 may be hosted locally, including fine-tuned variants deployed within an enterprise environment. In other embodiments, the models may be remote, third-party hosted, general-purpose foundation models accessed over a network through service endpoints. Hybrid arrangements may be used in which certain stages run on local fine-tuned models while other stages call external foundation models.

[0141] Figure 2A is a flow chart of a process 50A that may be executed by some embodiments of the controller 20A or by other components to automatically create a custom software application. In some embodiments, the process 50A includes obtaining an objective for a multiagent artificial intelligence platform to generate a software application, as indicated by block 52A. Some embodiments may obtain an objective by receiving a natural -language request, a structured form, or an API call that states what application a user wants and why, then determining context such as user role, tenant, domain indicators, data sensitivity, timelines, and device constraints. The system 12A may access prior missions and relevant documents, normalize the objective into a machine-parsable record with fields for goals, inputs, outputs, success criteria, and review points, and resolve ambiguous terms using definitions from enterprise sources. The platform 14A may validate scope against policy, attach provenance for who requested what and when, and create a mission record with identifiers and versioning so later steps can trace changes. In some cases, the intake path may also obtain preferences, personas, compliance requirements, and target environments, and cause an initial session to be created so downstream agents can decompose tasks, select user interface components, and begin generation under the captured constraints.

[0142] Some embodiments may determine a domain to which the objective applies, as indicated by block 54A. Some embodiments may determine a domain to which the objective applies by obtaining the mission text, attached files, and contextual signals and then analyzing them for domain indicators. The system may access language models and classifiers to detect terminology, entity types, units, regulatory citations, document templates, and source-system identifiers that correlate with domains such as finance, healthcare, manufacturing, or research. The platform may query a knowledge graph to resolve defined terms and product names, determine relationships to prior missions, and compute similarity to known domain exemplars. The controller 20A may combine these signals into a confidence score per domain, apply tenant policies that restrict eligible domains, and select one or more domains when the score exceeds a threshold. When ambiguity-40- 078474-0586924persists, the platform may request a short clarification, rank alternatives, and record the chosen domain with provenance to the inputs that supported the decision.

[0143] In some cases, the system 12A may determine that the objective spans multiple domains and cause a composite assignment. The platform 14A may partition tasks by domain, select domain specific agents 28A accordingly, and bind outputs so later steps can evaluate cross-domain constraints. The determination may adapt over time as additional documents arrive or as users supply feedback, and the controller 20A may update the domain assignment and trigger replanning when new evidence shifts confidence. The process may preserve privacy by redacting sensitive fields during analysis and may enforce tenant rules so domain detection only accesses sources and models authorized for the requesting user.

[0144] Some embodiments may access information in the domain, as indicated by block 56A. Some embodiments may access information in the domain by obtaining documents, datasets, and service endpoints associated with the selected domain and tenant, then determining which sources to query based on policy, relevance, and data locality. The system 12A may connect to the enterprise data repository 15A to retrieve records from databases, knowledge bases, wikis, document stores, and object archives, and may call internal or external APIs that expose domain feeds and reference tables. The system 12A may apply authentication, authorization, and redaction before transfer; normalize encodings and units; and extract text, tables, and images for downstream analysis. Metadata such as source identifiers, schema versions, effective dates, and ownership may be recorded so later steps can trace lineage and enforce retention and residency rules. Where private networks are used, the platform may route requests through private endpoints or peering links and may perform computation near the data to reduce movement of sensitive content.

[0145] In some cases, the system 12A may index or cache retrieved material under a domain- scoped namespace, determine embeddings or other retrieval features, and attach provenance to page ranges and diagram coordinates so later views can present citations. The system 12A may resolve cross-references among documents, map defined terms to canonical entities, and reconcile overlapping sources using precedence and confidence scores. When streaming or time-series inputs are present, the system 12A may subscribe to updates and maintain snapshots aligned to evaluation timestamps.-41- 078474-0586924

[0146] Some embodiments may use the information in the domain with a reasoning Al model to decompose the objective into sub-objectives to complete the objective, as indicated by block 58A. Some embodiments may obtain a mission and apply a reasoning model that plans, tests, and revises a path from objective to working features. The model may access domain material, align the mission to known entities and processes, and determine candidate steps that move the objective forward while honoring policy and resource limits. The model may form a task graph with dependencies and acceptance conditions, name the data each step requires, and predict the artifacts each step must produce, such as structured records, rules, or views. The controller 20A may request several alternative breakdowns;, and the model may score them for feasibility, clarity of evidence, and expected latency, and select a version that balances coverage with simplicity.

[0147] Some embodiments may combine different forms of reasoning to improve reliability. A planner may outline high-level phases, a solver may fill in concrete inputs and outputs, and a checker may verify that each step can be satisfied with available data and agents. The model may run short simulations with representative samples from the enterprise data repository 15A to estimate whether a step will succeed, then modify the plan when a data field is missing or a constraint would be violated. The model may generate rubrics for subjective checks and predicates for objective checks so later evaluation can mix deterministic rules with language-guided scoring while keeping provenance to source passages and diagram regions.

[0148] Some embodiments may constrain generation with explicit limits that reduce error and drift. The model may bind variables to schemas known to domain specific agents 28A and task specific agents 30A, restrict tool calls to whitelisted capabilities, and require that every produced field carry a citation or an origin. When multiple interpretations exist, the model may fork candidates, rank them using retrieval over prior missions and patterns, and discard branches that fail quick consistency tests, such as whether a proposed rule references a defined term or whether a planned view can render within size limits. The model may also compute counter-examples that would break a step and then adjust the step so the counter-examples no longer apply.

[0149] Some embodiments may use self-review to raise confidence before handing a plan to the controller 20A. A first pass may draft sub -objectives and interfaces, a second pass may critique missing validations, unsafe data flows, or unclear outputs, and a third pass may rewrite steps with-42- 078474-0586924explicit acceptance conditions. The model may express each sub-objective as a contract that names inputs, expected outputs, error cases, and fallback variants so the UI planner 24A and the orchestrator can call agents predictably. During runtime the same reasoning loop may operate at a smaller scale: when feedback indicates a gap, the model may localize the affected step, propose a minimal change, and produce a patch that the UI generator 26A can apply in place while the runtime 32A preserves state.

[0150] Some embodiments may use language models to interpret an objective, align terms to domain entities, and draft candidate decompositions. The models may obtain examples from prior missions and the enterprise data repository 15 A, determine tasks, inputs, and outputs, and express a first-pass plan with acceptance conditions and citations. Constrained decoding and toolgrounding may keep outputs within known schemas so each sub-objective names agents, data contracts, and views that downstream components can execute. When ambiguities appear, the models may generate alternatives, score them against retrieved precedents, and select a version that satisfies policy, latency, and evidence requirements.

[0151] Some embodiments may apply reinforcement learning to improve the decomposition policy overtime. The system may define rewards based on task success, coverage of requirements, user satisfaction signals, test pass rates, latency budgets, and audit completeness. A planner policy may choose among decomposition actions such as splitting a task, merging steps, swapping an agent, or changing evaluation order, while a bandit or contextual bandit may select agent variants or prompts given domain and data features. Offline logs may supply trajectories for off-policy training, and online updates may refine the policy when unit tests, contract checks, and human feedback indicate better choices. Safety constraints may limit exploration to whitelisted tools and schemas, and shaped rewards may encourage plans that carry provenance and minimize sensitive data movement.

[0152] Other models may contribute structure and verification. A symbolic or SAT / ILP (Boolean Satisfiability / Integer Linear Programming) solver may check dependency and resource constraints, a graph neural network may refine precedence and data flow on the task graph, and probabilistic calibration may attach confidence to each sub-objective so low-confidence steps trigger review or alternative branches. Program synthesis models may generate validators and-43- 078474-0586924transformation code that implement acceptance conditions, while retrieval models may surface templates for common workflows such as compliance compare or topology analyze.

[0153] Some embodiments determined with the Al platform 14A, user interface components of the software application as indicated by block 60A. Some embodiments may determine user interface components by obtaining the mission record, domain assignment, and constraints, then causing the Al platform 14A to propose views and widgets that satisfy the stated tasks. The platform 14A may interpret required inputs and outputs, select components such as fde uploaders, text inputs, tables, charts, galleries, graph visualizations, compare panes, and result panels, and determine bindings that connect each component to agent outputs and repository fields. The platform 14A may also determine interaction logic by naming events, handler signatures, validation rules, and provenance slots so results appear with citations and so subjective checks include rubric text and scoring placeholders.

[0154] Some embodiments may evaluate alternatives under device, accessibility, and policy constraints and may choose responsive variants that adapt to screen size and density. The platform 14A may assign stable identifiers, determine event sequencing for loading and error recovery, and specify content negotiation rules so components resize, collapse, or switch representations when space or data changes. Where the objective spans multiple domains, the platform may insert domain-specific views (such as compliance rule browsers, diagram callouts, or topology panels) and determine the data contracts and agent calls required to keep them current. The result may be a component plan that the UI generator can translate into executable code without manual wiring.

[0155] Some embodiments orchestrate a plurality of Al agents of the Al platform to determine how to configure at least some of the UI components and how to respond from input from at least some of the UI components, as indicated by block 62A. Some embodiments may orchestrate a plurality of agents on the Al platform 14A to determine configuration for user interface components and to generate responses to user input. The controller 20A may obtain the component plan from the UI planner 24A and cause agent calls that supply each component’s data contract, validators, and interaction rules. Domain specific agents 28A may return sector-aware schemas, rule expressions, and example payloads, while task specific agents 30A may produce extractors, selectors, summarizers, and formatters. The orchestrator 40 may assign call order, fan out requests-44- 078474-0586924where parallelism is possible, and join results into a single payload per component that the UI generator 26A can bind. For components that require subjective scoring or explanation, the orchestrator may obtain rubric text and prompt templates and attach them to the handler specification so the runtime 32A can call a language model when the user submits input.

[0156] During operation, the orchestrator 40 may receive events from the runtime 32A that reflect user actions such as upload, select, filter, compare, and annotate, and may determine which agents to invoke and which caches to read before returning an updated view model. For streaming or long-running work, the orchestrator may cause partial results to arrive incrementally and may signal state transitions for loading, success, and error. When an agent becomes unavailable or returns low-confidence output, the orchestrator may select a fallback variant, request a secondary check, or downgrade a component to a reduced representation while preserving provenance. The orchestrator may also enforce policy by redacting fields before display, by routing sensitive calls through private endpoints, and by recording per-call parameters, model versions, and citations so each response can be traced to its sources.[00157J In some cases, the orchestrator 40 may adjust configuration in response to feedback and context. If filters produce sparse results, the orchestrator may obtain relaxed predicates and propose an alternate component such as a list in place of a dense table. If a compliance view receives a new policy document, the orchestrator may re-run rule extraction and update bindings so highlights and links remain aligned with source coordinates. Where multiple agents can satisfy the same role, the orchestrator may select among them using latency, cost, and accuracy signals and may propagate the choice back to the controller 20A for later regeneration, allowing the application to evolve without manual rewiring while keeping input handling and component behavior consistent with the plan.

[0158] Some embodiments may generate code for event handlers by obtaining the component plan and emitting functions that receive events, validate inputs, call agents or repositories, and update view state. The generator may determine handler signatures, expected payloads, and error cases and produce code that debounces rapid inputs, retries on transient faults, and records provenance for displayed results. Handlers may include guards that check permissions and redaction rules before rendering sensitive fields and may attach citations or coordinates to any-45- 078474-0586924output so a user can trace where a value came from. For long-running actions, the code may cause optimistic updates, stream partial results, and transition components through loading, success, and error states according to a state machine defined in the plan.

[0159] Some embodiments may generate content negotiation code that adapts components to different screen sizes and client resources. The generator may determine breakpoints and minimum readable sizes and emit responsive logic so dense tables become cards on small screens, wide graphs switch to list or overview-plus-detail modes, and media downscales when bandwidth or memory is limited. The code may detect device class, pixel density, and input method and adjust touch targets, focus order, keyboard paths, and motion preferences to meet accessibility guidelines. Layout code may compute grid placements and flex rules, reserve space for localization, and preserve stable identifiers so when a layout changes during a session, focus, scroll position, and selected items remain consistent.

[0160] Some embodiments may emit security-focused code paths that reduce the risk of prompt injection, SQL injection, cross-site scripting, and related attacks. For language-model prompts, the generator may produce wrappers that separate instructions from user content, strip or neutralize model-control phrases in user input, label untrusted strings, and include signed, minimal context rather than raw page text. For data access, the generator may cause all joins and filters to use prepared statements or parameterized queries and may validate types and ranges against schemas before any call reaches a datastore or agent. For rendering, the code may escape or sanitize untrusted content by default, restrict HTML insertion to vetted templates, and isolate risky widgets in sandboxes with strict content security policies. The generator may also add origin checks for cross-site requests, same-site cookie settings, and CSRF (cross-site request forgery) tokens for state-changing operations, and may ensure secrets remain server-side while the client receives only scoped, short-lived tokens.

[0161] Some embodiments may include compile-time and runtime checks that enforce these protections. The build may fail when a handler tries to interpolate untrusted strings into queries or templates, and the runtime may block unsafe operations and log structured errors with minimal payloads. The generator may instrument audit hooks so any result shown to a user links to the exact agent call or query that produced it, including parameters and model versions, and may-46- 078474-0586924include test fixtures that simulate adversarial inputs to confirm that prompt wrappers, sanitizers, and parameterization hold under stress.

[0162] Some embodiments may store a version of a generated application in memory by writing build artifacts and manifests under a unique identifier with timestamps, provenance, and policy metadata. The system may persist code bundles, templates, schemas, model and agent versions, configuration, and test fixtures, along with a registry entry that maps the identifier to routes, permissions, and data contracts. Storage may include both volatile caches for rapid rollout and durable stores for audit and rollback, with integrity checks, signatures, and hashes to verify contents at load time. In environments with residency or classification rules, the platform may assign storage locations by tenant and domain and may encrypt artifacts at rest with keys controlled by the enterprise.

[0163] During operation, the controller 20A may cause the runtime 32A to load a stored version, hydrate it with session state, and apply patches that represent deltas from prior versions. The platform may maintain immutability of prior versions while recording lineage between versions so operators can revert or compare. Snapshots may capture dependent resources such as prompts, rubric text, and data-contract schemas to ensure reproducibility, and deduplication may reduce storage when large assets repeat across versions. When a new version is created, the system 12A may stage it alongside the active version, run contract and unit tests, and then substitute it for users with state preservation where compatible, while keeping the retired version available for investigation or rollback.

[0164] Some embodiments determine whether feedback has been received, as indicated by block 66A. If feedback is received, program flow may return to block 62A, otherwise it may proceed to block 68A, in response. Some embodiments may determine whether feedback has been received by obtaining signals from multiple channels and evaluating them against recorded expectations for the active mission and version. The platform may receive natural -language comments, thumbs-up or thumbs-down selections, in-line annotations, and uploaded examples from within the application and from the dashboard generator 36A, and may collect automated results such as unit tests, contract checks, accessibility probes, and performance budgets. The feedback module 34A may normalize inputs, identify the affected view, component, rule, or data-47- 078474-0586924contract, and record provenance that links each signal to user identity, session, page region, or test case. The controller 20A may cause periodic or event-driven scans of these stores, determine whether any new or unresolved items exist, and update a status that indicates whether feedback is present for the application, for a specific version, or for an element within that version.

[0165] Some embodiments may apply thresholds and policies to decide if captured signals qualify as actionable feedback. The system 12A may aggregate duplicates, merge near-matches, and assign severity and confidence based on source, frequency, and test reliability. Time windows may bound consideration to recent sessions or post-release intervals, and tenant rules may limit who can trigger regeneration. When the system 12A determines that feedback has been received, it may notify the controller 20A with a summary that names targets and suggested actions. When no qualifying signals appear, system 12A may record a check with timestamp for audit and continue monitoring. Privacy and security controls may ensure that only authorized reviewers can access the underlying comments or artifacts, with redaction applied to sensitive fields before any analysis or display.[00166J Some embodiments may execute the version of the software application as indicated by block 68. Some embodiments may execute a stored version by causing the runtime 32A to load its code bundles, configuration, and schemas, initialize routes and permissions, and hydrate state for the active session. The runtime 32A may obtain credentials for the requesting user, determine tenant and policy context, and establish connections to authorized sources in the enterprise data repository 15A and to eligible agents on the Al platform 14A. View definitions and state machines may drive rendering and interactions, with handlers validating inputs, calling agents, applying redaction, and attaching provenance so displayed results link to the underlying queries or model calls. Caches and data stores may warm as the session progresses, while accessibility and content negotiation rules adapt layouts to the client’s screen and input method.During execution, the platform may monitor latency, errors, and resource use, apply backoff and retries on transient faults, and select fallbacks when an agent or source is unavailable. When the feedback module 34A or controller 20A provides a patch or a regenerated plan, the runtime 32A may apply the update in place, substitute the new components or bindings, and carry forward compatible state such as selections, filters, focus, and scroll positions. Multi-tenant controls may-48- 078474-0586924isolate configuration and data per tenant, and private networking may route calls over restricted links. Audit trails may record which version produced each output with model and schema identifiers, enabling rollback or comparison if needed.

[0167] Some embodiments are distinct from other approaches in a variety of aspects, which is not to suggest that those other approaches are disclaimed or disavowed. Many approaches to generating applications from natural-language prompts rely on a single pass from text to screens, often using a single model or a loosely coupled chain. These systems tend to produce brittle plans that are hard to adapt when objectives change or when domain context turns out to be incomplete. In contrast, some embodiments may decompose objectives into smaller sub-goals using a coordinated set of specialized agents, each focused on planning, retrieval, evaluation, tooling, or execution. The agents may share a structured workspace that tracks assumptions, intermediate outputs, and confidence, allowing replanning of only the portions that need it rather than regenerating everything.

[0168] Many approaches to UI creation focus on template filling and static layouts, which can struggle with different form factors and with dynamic state that emerges at runtime. Some embodiments may compile planning artifacts directly into responsive UI bundles and event handlers, with content negotiation for screen size and client capabilities. The code may include accessibility affordances and telemetry hooks by default, so the same high-level plan can be realized across web and native environments with consistent behavior and observability.

[0169] Many approaches to code generation treat security as an afterthought or rely on generic sanitizers that are inserted late in the process. Some embodiments may generate security measures alongside the UI and backend logic, including prompt-boundary wrappers, input validation, output encoding, SQL parameterization, and policy checks at tool and model boundaries. By emitting these controls as first-class code and configuration, the resulting application may resist prompt injection, cross-site scripting, and injection attacks without extensive manual hardening.

[0170] Many approaches to orchestration assume a fixed pipeline or a single model family, which can limit robustness when inputs vary or when tasks require different strengths. Some embodiments may route work across heterogeneous models and tools using adapters and evaluators that compare candidates, enforce budget or latency constraints, and carry forward-49- 078474-0586924provenance of what data and models influenced a decision. This arrangement may improve reproducibility and auditability because each step records its inputs, outputs, and rationale in a way that can be re-played or inspected later.

[0171] Many approaches to improvement run offline loops that require users to restart sessions or accept disruptive redeployments. Some embodiments may operate a live runtime that supports state-preserving hot patches, targeted code substitutions, and guarded rollbacks. Telemetry from usage, tests, and synthetic probes may feed a feedback module that decides when to re-plan or regenerate specific components, so updates can be small, reversible, and tied to observed issues rather than broad, risky releases.

[0172] Many approaches to knowledge ingest focus on plain text and bag-of-words extraction, which can miss structure, context, and provenance. Some embodiments may parse mixed-media documents into machine-readable objects with explicit links back to page ranges and coordinates, and may separate objective facts from subjective clauses while retaining both. For engineering drawings and schematics, some embodiments may detect symbols and connections, perform vectorization, and assemble a topology graph that captures how components relate. Local legends and references may condition detection, and the system may export connection tables and bills of materials with confidence scores and citations to source regions.

[0173] Figure 4A through 4F show user interfaces of an example software application created with the techniques described above. A user interface may evolve while still being the same UI. These figures are segments of the user interface in two different states, with figures 4A through 4C showing one state in which requirements are extracted and figures 4D through 4F showing another state in which requirements are applied. The bottom of figure 4A corresponds to the top of figure 4B and so on through the UI figures as indicated.

[0174] This example may ingest a natural language document or otherwise unstructured document describing rules or other requirements to which an organization or user is expected to adhere. Extract those requirements and then apply them to other documents. The user interface 80 may include a file ingest component 82 by which documents expressing requirements may be uploaded upon selecting an upload button 84, which is another user interface component. The application may be configured to process images and display those images as indicated in region-50- 078474-058692486 of the user interface. This component may display different images that are processed. Some embodiments may display a graphical representation 88 in another user interface component with a hierarchical visualization of the document processing pipeline showing relationships between documents, pages, and extracted rules.

[0175] Mode selector user interface component 90 may include an option to select an evaluation mode in which new documents may be uploaded as evaluation fdes through user interface component 94, and the user may request to start compliance evaluation with user interface element 92. Examples may be presented in component 96 showing the application of requirements to various images and other aspects in this example.

[0176] Figure 4F shows another mode of the software application with an image table reasoning feature in component 98 for exploring compliance analysis, and a chat component 100 for asking answering questions with one or more of the Al models 42 described above.

[0177] Figures 5 A and 5B illustrate user interfaces 102 of another example generated software application. This example may be for analyzing technical documents, such as technical diagrams. Some embodiments may provide a user interface component 104 by which engineering diagrams may be uploaded. Some embodiments may analyze diagrams in those images and annotate them as indicated in user interface component 106 and generate an inventory of components therein as indicated in user interface component 108.

[0178] Figures 6A and 6B illustrate another example of a generated software application, User Interface 110, for a code generation assistant. Some embodiments include a code search user interface component and a generate code user interface component 112 and 114 respectively. Embodiments may further include statistics on a knowledge base in component 116 generated upon analyzing a code base. Embodiments may have a user interface component 118 showing results of code search.

[0179] Figure 7A through 7E may show a user interface 120 of a network data analyzer generated application. The application may include a user interface component 122 to upload network configuration files and a status indicator component 124 indicating status of uploading and processing files. Components may include a data processing summary component 126 with a-51- 078474-0586924network data processing summary should be emphasized that components may include components themselves in a hierarchy such as those illustrated. Embodiments may include further interfaces and components 128 and 130 for creating a network graph and generating network insights respectively. Figure 7D shows an example of a network graph in a UI component 132 illustrated by selecting component 128. Figure 7E shows an example of a user interface component with recommended actions which may be generated with one of the above described Al models 42.

[0180] Figures 8A through 8F show example user interface components for a generated data analysis application. Figure 8A shows data perception insights. Figure 8B shows time series insights. Figure 8C shows further time series insights. And figure 8D shows maintenance processing insights. Figure 8E shows a user interface to process a warranty claim with a preview of steps to be performed by the application when doing so. And figure 8F shows a chord diagram for the processing of warranty claims in this example generated software application.

[0181] Figure 9 shows an example of a dashboard showing usage and status of various generated software applications.

[0182] ROBUST EXPLAINABLE ARTIFICIAL INTELLIGENCE

[0183] Many Al models, including generative artificial intelligence models and systems such as transformer-based architectures, generate complex predictions without incorporating mechanisms for quantifying uncertainty or providing interpretability. Many existing approaches produce deterministic outputs, often offering limited insight into confidence levels or the influence of specific features on the prediction. This constraint presents challenges in applications where understanding factors influencing predictions is expected to enhance reliability. Furthermore, while certain traditional methods may generate probabilistic outputs, such methods often involve intensive computational demands and do not integrate seamlessly with transformer architectures in high-dimensional and scalable task environments. Accordingly, there is a need for a computer system that combines the strengths of probabilistic reasoning, uncertainty quantification, and interpretability in a computationally feasible manner, which is not to suggest that embodiments are limited to systems addressing all of these issues or any of these issues.-52- 078474-0586924

[0184] None of the preceding should be read to imply that any approach is disclaimed or disavowed, and this clarification should not be read to imply that any other material is disclaimed or disavowed herein where no such clarification is provided. Further, the discussion of various issues with other approaches herein should not be read to imply that embodiments are limited to systems that fully solve, or even mitigate, all of these issues or any of these issues, which is not to imply that any other description is limiting.

[0185] In some embodiments, a hybrid computing architecture may combine Gaussian Processes, transformer models, Bayesian training through Markov Chain Monte Carlo, and Sobol sensitivity analysis to enhance interpretability and uncertainty quantification. This architecture may be specifically configured to address computational and interpretability constraints observed in other models, rendering it suitable for complex, high-dimensional tasks.

[0186] Example embodiments may mitigate some or all of these problems or other problems and have the following features.

[0187] Some embodiments include a hybrid probabilistic system architecture that includes a fusion of Gaussian Processes and transformer models, with adaptive uncertainty layers designed to adjust probabilistic outputs based on input data complexity. These adaptive Gaussian Process layers may modulate their influence within the network, dynamically scaling uncertainty measures for complex inputs while conserving computational resources for simpler data, allowing the system to operate effectively across varied data types and complexities. This dynamic adjustment of uncertainty contributions may distinguish the system from models using static probabilistic layers, providing a more efficient response to differing data demands (which is not to suggest that embodiments are limited to systems having this feature.)

[0188] In some embodiments, the Gaussian Process component may serve as a probabilistic layer within a deep neural network, estimating the likelihood of various potential predictions. This probabilistic estimation may support robust uncertainty modeling, addressing scenarios where deterministic models may not quantify prediction uncertainty effectively.

[0189] To further enhance the integration of Gaussian Processes within the transformer architecture, some embodiments may include a structured sparse attention mechanism. This-53- 078474-0586924mechanism may prioritize high-variance regions within the input data, directing computational resources toward critical data segments. By allocating attention to these high-impact regions, the system may optimize uncertainty estimation, setting it apart from typical transformer architectures that lack targeted sparsity in their attention mechanisms, particularly in uncertainty-focused applications.

[0190] Certain embodiments may draw on biological analogies, where organisms make critical decisions with incomplete information, utilizing probabilistic reasoning in response to uncertain and changing conditions. For instance, biological systems, such as the nervous system, may operate on partial sensory data to make probabilistic predictions guiding behavior. In alignment with this principle, the system may integrate Gaussian Processes within the transformer architecture to explicitly quantify uncertainty. Similar to biological systems that assess and respond to varying certainty levels, this architecture may provide confidence metrics alongside predictions, supporting informed decision-making even with limited or noisy data.

[0191] In some embodiments, an enhanced Bayesian training system may be implemented with optimized Markov Chain Monte Carlo (MCMC) sampling techniques, featuring adaptive adjustments to chain length based on parameter convergence. In some forms of MCMC -based training, computational demands may increase significantly with extensive sampling. To address this, some embodiments may use adaptive stopping conditions, modifying the chain length for each parameter according to convergence criteria. This adaptation, in some embodiments, allows for high-fidelity Bayesian inference without redundant sampling, potentially reducing computation time and enabling scalable performance in large-scale applications where exhaustive MCMC sampling might otherwise be impractical (which is not to suggest that this approach is disclaimed).

[0192] The architecture may also employ hierarchical parameter sampling rather than a flat MCMC approach. In some embodiments, higher-level system parameters may be sampled initially, followed by lower-level parameters conditioned on these broader distributions. This hierarchical strategy may accelerate convergence of critical structural parameters, facilitating efficient sampling within complex model architectures by focusing computational resources on parameters that contribute most significantly to system performance.-54- 078474-0586924

[0193] In some embodiments, a design inspired by biological processes may inform the adaptive mechanisms within the Bayesian inference process. Biological systems frequently prioritize responses based on situational demands; for example, the immune system may dynamically amplify responses to high-priority pathogens while conserving energy in low-risk situations. Reflecting this principle, the adaptive MCMC chain length and hierarchical sampling mechanisms may selectively apply Bayesian inference to predictions of higher priority, conserving computational resources for scenarios requiring enhanced accuracy. This selective prioritization may allow the model to operate responsively and efficiently, mirroring the energy-efficient strategies observed in biological systems.

[0194] In some embodiments, a hybrid optimization mechanism may incorporate Stochastic Gradient-Based Bayesian Inference (SGBI) to facilitate efficient training by combining MCMC sampling with gradient-based parameter updates. The SGBI component may use mini-batch gradients to accelerate parameter updates, enhancing scalability for Bayesian training. This hybrid approach may provide a balance between the exploration facilitated by MCMC sampling and the efficiency of gradient-based updates, offering a potential improvement over methods that rely solely on MCMC or gradient optimization.

[0195] To manage computational complexity, the architecture may include optimized MCMC sampling methods alongside quasi-Monte Carlo techniques, allowing it to scale Bayesian inference effectively within high-dimensional parameter spaces. This scaling may balance computational feasibility with high-quality uncertainty estimation, enabling the system to maintain robustness in complex tasks.

[0196] In some embodiments, the system may employ probabilistic regularization by integrating dropout-inspired priors within Gaussian Process-transformer layers. This dropoutbased regularization technique may simulate the effects of multiple posterior distributions, smoothing the posterior landscape and producing a broader array of MCMC samples for uncertainty analysis. By introducing variability into the model’s response, this probabilistic regularization may reduce the risk of overfitting and increase adaptability to new data, strengthening the system’s performance in dynamic environments.-55- 078474-0586924

[0197] The system architecture may also reflect principles inspired by hierarchical organization observed in biological networks, where different levels of processing allow for efficient resource allocation. For instance, the brain processes sensory input in stages, moving from basic feature detection to complex pattern recognition, with specialized pathways dedicated to handling high- priority signals. In some embodiments, the system may incorporate a Hierarchical Parameter Sampling method within the MCMC training process, whereby high-level parameters converge quickly, allowing for focused refinement of lower-level parameters only when necessary. This staged processing may contribute to resource efficiency by directing computational resources to critical tasks, emulating the selective allocation seen in biological systems.

[0198] In some embodiments, enhanced explainability may be achieved through mechanisms for variance detection and topological attribution. The system may apply Sobol sensitivity analysis with multi-level feature grouping, allowing related features, such as linguistic constructs, semantic clusters, or syntactical patterns, to be grouped and analyzed for their collective impact on prediction variance. By organizing features into multi-level groups, the system may provide insights into how broader feature categories, such as technical terminology versus colloquial language, influence overall uncertainty, offering interpretability at both granular and grouped levels.

[0199] The system may further include a Variance Attribution Mapping (VAM) module that generates visual explanations of feature contributions to predictive variance. This module may create heatmap-style visualizations, highlighting influential feature groups and interaction effects as determined through Sobol indices. Sensitivity-layered visual explanations may help users intuitively grasp the significance of individual features and grouped interactions, enhancing the interpretability of system predictions in decision-critical applications.

[0200] To capture feature relationships within a topological network, the system may use a graph-based clustering technique for topological variance detection. This network may organize features into clusters that reflect their structural relationships, such as phrase structures, thematic groupings, or cross-domain terms. By analyzing clusters and attributing variance to each, this topological approach may reveal feature neighborhoods in which similar features collectively influence prediction uncertainty.-56- 078474-0586924

[0201] Building on VAM, the topological variance detection module may produce a topological heatmap, displaying clusters as connected nodes with edge thickness representing inter-cluster interactions. Nodes may be color-coded according to variance levels, providing a visual trace of feature clusters or network "hotspots" that contribute substantially to predictive uncertainty. This approach may reveal interconnected feature structures affecting system predictions beyond isolated features, offering a holistic view of feature interactions.

[0202] For adaptability, the system may employ adaptive node clustering and real-time variance tracking, allowing topological clustering to update in real time as new data arrives. Node and edge representations may adjust dynamically, reflecting variance shifts due to evolving input features. This real-time tracking may enable users to observe how predictive uncertainty changes in response to new data characteristics.

[0203] The system may also offer comparative analysis across multiple topological layers, enabling users to view variance contributions at various levels, from fine-grained feature nodes to aggregated cluster layers. This layered comparative capability may assist domain experts in pinpointing the sources of variance and tracing them through different topological levels, yielding a comprehensive perspective on variance across feature hierarchies.

[0204] To maintain computational efficiency, low-discrepancy sampling techniques, such as Sobol sequences, may be applied to ensure sensitivity analysis remains feasible even in highdimensional settings, supporting deployment in real-world applications without incurring prohibitive computational costs.

[0205] In some embodiments, biological analogies may inform the system's design. Biological systems, such as the visual processing pathways in the brain, frequently process information across multiple hierarchical layers, from initial feature detection to complex pattern recognition, to arrive at holistic interpretations. Reflecting this multi-layered approach, the model may use Sobol Sensitivity Analysis with Multi-Level Feature Grouping to decompose complex predictions into understandable parts, providing insights into individual features and group interactions, in a manner similar to how the brain integrates signals across multiple layers for robust perception.-57- 078474-0586924

[0206] In some embodiments, an adaptive sensitivity-driven inference mode selection mechanism may be incorporated to dynamically adjust inference modes based on real-time sensitivity analysis of input features. For features identified as high-sensitivity — those with a significant influence on prediction variance — the model may switch to full Bayesian inference, which may maximize interpretability by generating a richer probabilistic output. Conversely, for low-sensitivity features, the model may use faster, deterministic inference to enhance computational efficiency while preserving interpretability.

[0207] The system may also include a real-time sensitivity threshold calibration mechanism that adjusts sensitivity thresholds based on recent inference patterns. For example, if specific features repeatedly demonstrate low sensitivity, the threshold may adaptively adjust to reduce computational demands associated with those inputs. This continuous feedback system allows the model to optimize resource allocation dynamically, supporting adaptability in environments where data profiles may shift frequently.

[0208] In some embodiments, the design of the adaptive sensitivity-driven inference mechanism may draw on biological analogies. Living organisms frequently adjust responses based on input sensitivity and feedback from their environment; for instance, sensory adaptation allows humans to focus on important or novel stimuli while filtering out background information, thereby conserving cognitive resources. Like this principle, the sensitivity-driven inference mode selection and real-time sensitivity threshold calibration mechanisms may enable the system to adjust its inference strategy according to input sensitivity. This dynamic calibration may allow the model to prioritize high-impact inputs, optimizing computational resources by minimizing processing for low-sensitivity features, in a manner similar to how biological systems allocate attention and resources to critical stimuli.

[0209] Some embodiments may offer several expected benefits of topological variance detection and attribution. By mapping predictive variance across a topological network of features, this approach may provide holistic interpretability, giving a comprehensive view of feature interactions and their cumulative impact on uncertainty. Such a global perspective may be especially useful in applications involving interrelated variables or complex, multifaceted feature relationships. The topological heatmap, which visually highlights high-variance clusters and-58- 078474-0586924interdependencies, may offer enhanced visual clarity, allowing users to intuitively understand the factors influencing system uncertainty. This visual transparency may assist users in interpreting intricate, interconnected features, contributing to increased confidence in the system’s reliability. Further, with adaptive clustering and real-time variance tracking, users may observe how predictive uncertainty shifts in response to incoming data, rendering this approach suitable for dynamic environments with evolving data distributions or changing prediction patterns.

[0210] The technical implementation may provide advantages related to probabilistic prediction, Bayesian inference, and sensitivity-driven interpretability. By incorporating Gaussian Processes within the transformer architecture, the system may generate probabilistic outputs that represent uncertainty associated with each prediction, a feature expected to be valuable in applications where interpretable confidence metrics are helpful. Bayesian inference via Markov Chain Monte Carlo (MCMC) may help the system to maintain a distribution over model parameters, offering deeper insights into parameter uncertainty and supporting robust generalization in out-of-sample scenarios. Furthermore, Sobol sensitivity analysis may be integrated within the Bayesian framework to provide interpretability by indicating how individual input features contribute to the system’s probabilistic output, thereby enhancing user confidence in the model’s predictions.

[0211] Some variations may include alternative sensitivity analysis approaches, sampling techniques, attention mechanisms, probabilistic layers, and adaptations for real-time applications. For sensitivity analysis, although Sobol sensitivity analysis may be used for its robust decomposition capabilities, other methods, such as variance-based approaches or Shapley values, may also be implemented to offer different interpretability perspectives. These alternative methods may be advantageous in specific domains where varying interpretability needs are present.

[0212] In terms of sampling, variants on Markov Chain Monte Carlo (MCMC), including variational inference or particle-based techniques, may achieve approximate Bayesian inference with reduced computational requirements. While these techniques may provide less precise Bayesian sampling, they may be valuable in settings with constrained computational resources.

[0213] Some embodiments may use kernelized attention mechanisms in place of traditional transformer attention, enhancing interpretability and enabling more selective feature sparsity. This-59- 078474-0586924adjustment may streamline the architecture and reduce computational demands, potentially making it more suitable for lightweight applications.

[0214] In instances where Gaussian Processes may introduce significant computational demands, other probabilistic models, such as Bayesian neural networks or probabilistic linear regression, may be substituted. Although these alternatives may offer less granular uncertainty quantification, they could serve as computationally lighter options, especially in resource-limited environments.

[0215] Additionally, adaptations may be made for real-time applications by adjusting sampling frequency or refining the scope of sensitivity analysis. In such cases, certain layers or analyses may be streamlined to emphasize speed over comprehensive uncertainty quantification. For example, using only first-order Sobol indices may allow for faster interpretability while still retaining insights.

[0216] In some embodiments, the present techniques may be integrated with systems and processes described in other patent applications by the applicant filed on the same day as this filing. Some embodiments may apply personas to shape model outputs with the techniques described in the US patent application bearing attorney docket number 078474-0586614, titled CREATING CONTEXT-SPECIFIC, VERSATILE EXPERT Al PERSONAS. Some embodiments may provide a user interface with the techniques described in the US patent application bearing attorney docket number 078474-0586620, titled HUMAN- Al CO-CREATION SYSTEM. The entire content of each afore-mentioned patent filing in this paragraph is hereby incorporated by reference.

[0217] It should be assumed that the results described herein are generally prophetic, rather that describing the result of actual tests performed.

[0218] In some embodiments, the above architecture may be implemented on one or more computing devices forming a computing system, e.g., a client-server architecture. Having memory storing instructions that when executed, implement the described functionality. In some embodiments, users may access this computing system via a network such as the internet. Remotely, using their own computing devices, which may be personal computers, desktop computers, wearable computing devices, laptop computers, and the like. In some embodiments,-60- 078474-0586924the described system may be implemented in a cloud architecture, in a hybrid cloud architecture, and on-premises architecture, or in other architectures. In some embodiments, an orchestrator module may coordinate the various models in the execution path, and a view generator may generate the user interfaces, which may be presented client-side in a special purpose application or in a web browser.

[0219] As shown in the block diagram of computing environment 10B of figure IB, and as described in more detail below, in some embodiments, an Al system 12BB may implement some or all of the above techniques or related approaches. In some embodiments, one or more user devices 14 may communicate with the Al system 12BB over the internet 13 to submit inputs, retrieve outputs, and initiate training or evaluation sessions.

[0220] In some embodiments, user devices 14 may be geographically distributed client systems that initiate network sessions with the Al system 12BB, and may operate under a single organization or under distinct tenant accounts associated with different organizations. A user device 14B may include one or more processors, volatile and non-volatile memory, a network interface supporting wired or wireless links, and local storage containing a client application and configuration data specifying tenant identifiers and endpoint addresses. A user device 14B may execute an operating system such as Windows™, macOS™, Linux™, Android™, or iOS™, and may provide clock synchronization, secure key storage, and certificate trust settings that may be referenced during authenticated sessions. A user device 14B may maintain local logs and may persist request identifiers and response artifacts to allow resubmission or later reconciliation.

[0221] In some embodiments, a user device 14B may interact with the Al system 12BB through a web browser, a special-purpose native application, or a headless process that communicates via an application programming interface (API). A browser-based client may issue requests over a secure transport, may send authentication tokens scoped to a tenant account, and may transmit input payloads encoded as structured data such as JavaScript™ Object Notation (JSON) or a binary message format, while rendering response data and links returned by the Al system 12BB. A native application may establish a persistent session, may batch multiple inference or training-control requests, and may stream partial results to a display or file sink. An API client may run without human interaction, may execute as a background service on the user device 14B, and may schedule-61- 078474-0586924calls to the AT system 12BB based on local triggers, queued jobs, or a periodic timer. In some embodiments, a user device 14B may request inference by the Al system 12BB, view the result, and be used to interrogate data indicative of uncertainty in the response and factors contributing to that uncertainty, as described further below.

[0222] In some embodiments, the internet 13 may comprise packet-switched networks that route Internet Protocol version 4 or version 6 traffic across public and private links, and may instead or additionally include a private network such as an enterprise wide-area network connected through virtual private network tunnels, software-defined wide area networking, or dedicated circuits. The internet 13 may carry requests from user devices 14 to the Al system 12B over Transport Layer Security sessions, may resolve service endpoints through Domain Name System queries, and may traverse network address translation boundaries, firewalls, intrusion detection sensors, and proxy gateways that apply policy rules. The internet 13 may include cloud provider backbone segments and virtual private clouds that expose endpoints through load balancers and reverse proxies, and may pass traffic through peering exchanges and content routing layers that select paths based on latency measurements and health probes. The Al system 12B may be deployed in a public cloud tenancy, may be hosted on-premises within a data center rack, or may be arranged as a hybrid where control-plane services run in a cloud tenancy and data-plane services run on-premises, and the internet 13 may provide interconnection between these sites using encrypted tunnels, private peering links, or cross-connects that carry application programming interface calls, model artifacts, and telemetry streams under tenant scoping metadata.

[0223] In some embodiments, as described further below, the Al system 12B may include a controller 15B that may orchestrate data ingress, job scheduling, and inter-component messaging among an Al model 16B, a hybrid Bayesian training module 17B, a sensitivity scorer 18B, a mode selector 20B, and a user interface module 22B. The controller 15B may receive requests, assign them identifiers, enqueue them to processing queues, and forward intermediate artifacts and parameter snapshots between components. The hybrid Bayesian training module 17B may run training and update procedures and may write parameter states to a repository that the controller 15B may reference when activating models for inference. The sensitivity scorer 18B may compute token- or group-level sensitivity signals from inputs and intermediate features and may publish-62- 078474-0586924those signals for consumption by the mode selector 20B. The mode selector 20B may evaluate the sensitivity signals against one or more thresholds and may emit routing directives that the controller 15B may apply to choose between inference paths. The user interface module 22B may prepare response payloads, may render or serialize visualizations and logs, and may format outputs for delivery to user devices 14. The Al model 16B may process inputs and may produce classification scores and associated uncertainty values for use by the other components.

[0224] In some embodiments, the controller 15B may execute as one or more services that may expose APIs for receiving requests from user devices 14 and for coordinating the flow of data through the Al system 12B. The controller 15B may assign request identifiers, may validate authentication tokens and tenant scope, and may record metadata such as timestamps, client attributes, and routing tags. The controller 15B may persist request envelopes, intermediate artifacts, and output records to a durable store, which may include a relational database for transactional state and an object store for larger payloads. The controller 15B may maintain a registry of active model versions and configuration parameters, and may select a model version for each request based on routing rules, tenant configuration, or an experiment assignment. The controller 15B may enqueue work items onto internal queues, may apply backpressure and rate limits, and may schedule execution on worker processes that interact with the Al model 16B, the sensitivity scorer 18B, and the mode selector 20B.

[0225] In some embodiments, the controller 15B may implement a stateless request handler tier and a background orchestration tier. The request handler tier may deserialize inputs, may normalize text encoding, may attach tenant metadata, and may publish an inference task to a message queue, while returning an acknowledgment that includes the request identifier. The background orchestration tier may poll the queue, may fetch the associated configuration, and may submit a call to the Al model 16B for feature computation and candidate class scoring. The controller 15B may request sensitivity scores from the sensitivity scorer 18B, may forward those scores to the mode selector 20B, and may receive a directive that identifies an inference path. The controller 15B may execute the selected path, which may include additional sampling and marginalization steps or a deterministic evaluation, and may aggregate outputs into a response record that includes class probabilities, uncertainty values, and diagnostic fields. The controller-63- 078474-058692415B may apply idempotency checks based on the request identifier, may retry failed operations with bounded backoff, and may emit structured logs and metrics for later analysis.

[0226] In some embodiments, the controller 15B may execute a process described below with reference to figure 2B. The controller 15B may initialize this process definition at startup, may refresh it from a configuration service, and may branch among steps according to status signals produced by downstream components. The controller 15B may update thresholds and routing rules at run time by consuming feedback streams that report latency, throughput, and calibration measurements, and may write the updated values to a configuration store for consistent consumption across services. The controller 15B may maintain secure connections to data stores and to the internet 13B endpoints, may rotate credentials according to tenant policy, and may verify message signatures where applicable. The controller 15B may also prepare artifacts for the user interface module 22B, which may include compact summaries, references to stored visualizations, and links to audit logs, and may transmit those artifacts to user devices 14 after the response record is persisted. The controller 15B may direct inference and training operations of the Al model 16B.[00227J In some embodiments, at run time (as opposed to during prior training), the Al model 16B may receive tokenized text and associated metadata from the controller 15B, may construct intermediate representations for the input, and may execute an inference pass that produces candidate class scores and uncertainty-related quantities for those candidates. The AT model 16B may accept a request context that specifies a model version, precision policy, and may branch accordingly to perform either a deterministic evaluation or a sequence that includes stochastic sampling and marginalization steps. The Al model 16B may process multiple inputs in a batch, may apply masking to account for variable-length sequences, and may emit outputs that include per-class predictive probabilities, an uncertainty value derived from the predictive distribution, and auxiliary diagnostics such as intermediate feature summaries and sensitivity signals when requested by the controller 15B. The Al model 16B may expose service endpoints for synchronous calls and asynchronous jobs, may record timing and status markers for each stage of the pass, and may write structured artifacts to storage for later retrieval by the user interface module 22B.

[0228] In some embodiments, as explained further below, the Al model 16B may compute per- output-class predictive distributions rather than single-point scores by applying a probabilistic-64- 078474-0586924head that may include a Gaussian process layer and a likelihood mapping with marginalization. For instance, the model 16B may classify as input sequence as belonging to one of a set of classes or as being followed by one of a set of classes or the like. The Al model 16B may draw latent samples or apply moment-matching to propagate uncertainty through the likelihood and may aggregate the resulting class probability vectors. The Al model 16B may also compute uncertainty measures determined from the predictive distribution, and may emit both the probability outputs and the uncertainty values as first-class results alongside intermediate artifacts requested by the controller 15B. These operations may be performed under either a deterministic evaluation path or a stochastic path selected by the mode selector 20B, and the same or other interfaces may be used during both training and inference.

[0229] In some embodiments, as explained further below, the Al model 16B may record feature sensitivities at token and group levels and may provide variance attribution signals determined from sampling traces or from auxiliary estimators. The Al model 16B may apply structured sparse attention masks that may be conditioned on sensitivity scores and may adjust computation budgets for blocks, heads, or layers based on thresholds received from the controller 15B. The Al model 16B may expose programmatic hooks to generate visual attribution artifacts, such as heatmaps layered by sensitivity level and graphs that may summarize interactions among grouped features, and may write references to those artifacts to storage for later retrieval by the user interface module 22B. These mechanisms are expected to provide clearer explanations of how inputs contribute to outputs and are expected to help with understanding which inputs contribute to higher or lower uncertainty during inference.

[0230] In some embodiments described further below, the Al model 16B may be trained with procedures that may combine stochastic gradient-based Bayesian updates with targeted Markov chain Monte Carlo steps over selected hyperparameters. The Al model 16B may apply adaptive stopping conditions for chains, may incorporate quasi-Monte Carlo sampling for variance reduction, and may apply probabilistic dropout priors that sample gating variables during forward and backward passes. The resulting parameter states may be checkpointed with metadata recording sampler settings and effective sample counts, and the inference path may marginalize over posterior samples or variational parameters to produce outputs. These operations are expected to improve calibration of the predictive distribution in settings where high accuracy and clear-65- 078474-0586924communication of uncertainty are needed, and are expected to allow downstream systems to apply risk-aware decision rules based on the provided uncertainty values.

[0231] In some embodiments elaborated upon below, the Al model 16B may include a tokenizer 24B that may segment input strings (or other sequences of symbols) into tokens according to a subword or wordpiece scheme and may output token identifiers and masks, a token embedding module 26B that may map token identifiers to dense vectors stored in a table and may emit a sequence of embedding vectors, and a positional encoding module 28B that may combine positional information with the embedding vectors by adding, concatenating, or otherwise injecting a position-dependent signal. A transformer encoder 3 OB may consume the position- conditioned vectors and may compute contextual representations across the token sequence using attention and feed-forward operations, and may output hidden states that may be pooled or otherwise summarized. A Gaussian -process layer 32B may receive a pooled representation and may compute class-wise latent quantities that may include measures of central tendency and dispersion for downstream use. A likelihood and marginalization module 34B may map the latent quantities to class probabilities according to a selected likelihood, may average or otherwise combine results across sampled or approximated latent values, and may emit probability vectors and uncertainty values for use by other components.

[0232] In some embodiments, the tokenizer 24B may receive an input payload that may include raw text, markup such as HyperText Markup Language (HTML), structured records such as JSON, or newline-delimited logs, and may produce a sequence of token identifiers and associated masks for use by downstream components. The tokenizer 24B may apply canonicalization steps that may include Unicode normalization, script detection, case folding subject to configuration, and whitespace collapsing while preserving code point offsets that may support later alignment to the original input. The tokenizer 24B may segment the normalized stream into initial units such as words, punctuation, and numeric spans, and may further segment those units into subword fragments according to a stored vocabulary and a set of merge or split rules. The tokenizer 24B may output token identifiers, an attention mask that may distinguish padding from content, optional type identifiers that may distinguish segments, and position indexes that may be consumed by the token embedding module 26B and the positional encoding module 28B.-66- 078474-0586924

[0233] In some embodiments, the tokenizer 24B may implement a rule-driven subword procedure that may begin with a character or byte sequence, may apply a sequence of merges that may combine adjacent fragments that appear in a learned vocabulary, and may stop merging when no higher-priority rule applies. In other embodiments, the tokenizer 24B may implement a probabilistic segmentation procedure that may score candidate segmentations and may select a sequence according to stored scores, while falling back to byte-level fragments when an input substring is not present in the vocabulary. The tokenizer 24B may maintain a table of special tokens that may include classification markers, separators, padding markers, unknown markers, and user- defined control symbols, and may insert those tokens based on configuration or explicit markup in the input. The tokenizer 24B may support detokenization by storing span boundaries and may expose offsets that may allow the user interface module 22B to highlight tokens or groups of tokens in visual artifacts.

[0234] In some embodiments, the tokenizer 24B may process multilingual inputs by first applying language and script identification, may select a vocabulary associated with the detected language or a shared multilingual vocabulary, and may apply language-specific normalization such as diacritic handling or punctuation folding. For structured inputs, the tokenizer 24B may offer modes that may retain or discard syntactic delimiters and field names; for example, a structured- record mode may treat keys and values as separate token streams and may emit segment identifiers so that downstream components may distinguish among fields. For markup, the tokenizer 24B may include a mode that may strip tags while retaining text content, and another mode that may map tags and attributes to special tokens to preserve layout cues. For code inputs, the tokenizer 24B may expose a lexical mode that may treat identifiers, string literals, and operators as separate categories and may retain formatting characters that may convey block structure.

[0235] In some embodiments, non-text modalities may be provided as text-like inputs to the tokenizer 24B after preprocessing performed by other components. For example, an audio stream may be transcribed to text prior to tokenization and an image may be converted to text through optical character recognition prior to tokenization. Or embodiments may use a tokenizer for a vision transformer. The tokenizer 24B may then apply the same segmentation procedures and may record provenance metadata indicating the upstream conversion. The tokenizer 24B may support batch and streaming operation. In streaming operation, the tokenizer 24B may emit tokens-67- 078474-0586924incrementally as new input bytes arrive, may maintain parti al -fragment state across boundaries, and may flush or resegment when a later substring causes a different merge choice under the segmentation rules. The tokenizer 24B may apply length constraints that may include truncation and padding to a target sequence length, and may record the extent of truncation so that the controller 15B may request a continuation pass if needed.

[0236] In some embodiments, the tokenizer 24B may maintain tenant-scoped vocabularies and configuration profiles so that different organizations may apply distinct segmentation policies. The tokenizer 24B may support dynamic vocabulary updates by loading additional merge rules or special tokens at runtime and may version those updates so that inference requests may reference a particular configuration. The tokenizer 24B may implement security checks that may include redaction of specified patterns, quarantine of oversized or malformed inputs, and normalization of bidirectional control characters. The tokenizer 24B may expose diagnostics that may include token counts, out-of-vocabulary rates, and per-category histograms, and may write these diagnostics to logs referenced by the controller 15B. The tokenizer 24B may be implemented as a library linked into the Al model 16B process, as a microservice with a remote procedure call interface, or as a plug-in to the user device 14B client application, and may use a memory-mapped vocabulary file or an in-memory trie to accelerate segmentation.

[0237] In some embodiments, the tokenizer 24B may output a structured record that may include, for a sequence length L, an array of token identifiers of length L, a parallel attention mask of length L indicating content or padding positions, optional segment type identifiers of length L, position indexes of length L, and character or byte offsets mapping each token back to the source string; for example, given the input text “Reset my password, please.” and a maximum length of 12, the tokenizer 24B may emit token identifiers such as [101, 12287, 602, 4219, 117, 7335, 102, 0, 0, 0, 0, 0] where 101 may denote a classification marker, 102 may denote a separator marker, 0 values may denote padding, and intervening values may denote subword units for “Reset,” “ my,” “ password,” “,” and “ please,” respectively; an attention mask such as [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]; segment type identifiers such as [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]; position indexes such as [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]; and offsets such as [(0, 0), (0, 5), (6, 8), (9, 17), (17, 18), (19, 25), (25, 26), (0, 0), . .. ] where each non-padding pair may identify the start and end character positions in the normalized input for the associated token; other embodiments may include additional fields-68- 078474-0586924such as vocabulary version, tokenizer configuration identifiers, and per-token categories, and may emit the record in a binary or text encoding for consumption by downstream components.

[0238] In some embodiments, a token embedding module 26B may map token identifiers produced by the tokenizer 24B to fixed-length vectors drawn from a learned embedding space and may emit a sequence of vectors aligned one-to-one with the tokens and any padding positions. The token embedding module 26B may maintain an embedding table stored in device memory that may be addressed by token identifier, may retrieve the corresponding rows, and may arrange the rows into a tensor whose leading dimension corresponds to sequence length. The token embedding module 26B may support mixed precision, and may store the table in eight-bit or sixteen-bit formats with dequantization to a higher-precision accumulator during arithmetic. The token embedding module 26B may maintain separate rows for special tokens such as classification, separator, padding, and unknown markers, and may expose configuration to freeze or fine-tune subsets of the table during training. The token embedding module 26B may initialize rows using a seedable procedure and may apply post-initialization normalization or scaling so that downstream layers of the transformer encoder 30B receive inputs within a configured range.

[0239] In some embodiments, the token embedding module 26B may compose embeddings from multiple sources prior to emission. For example, the token embedding module 26B may sum or concatenate a lexical embedding with a segment-type embedding and a feature embedding that may reflect token categories produced by the tokenizer 24B, and when concatenation is used the token embedding module 26B may apply a projection layer to match a target vector length. The token embedding module 26B may also compute a subword-composition embedding by combining character-level or byte-level representations for identifiers that are not present in the main embedding table, and may cache the composed result to a small dictionary for later reuse within a batch. The token embedding module 26B may implement hashing-based embeddings that map rare or adversarial substrings to a bounded number of buckets, and may fold multiple hash functions to reduce collisions. The token embedding module 26B may support runtime adapters that apply an affine transformation or a small multilayer perceptron to the retrieved vectors to incorporate tenant-specific adjustments without retraining the entire table.-69- 078474-0586924

[0240] In some embodiments, the token embedding module 26B may accommodate different input modalities that are represented as text after upstream preprocessing. For structured records, the token embedding module 26B may use separate per-field or per-schema embeddings that may be combined with the lexical embeddings so that identical lexemes from different fields may be distinguished. For markup, the token embedding module 26B may assign distinct embeddings to tag tokens and attribute tokens and may optionally attenuate their magnitudes when a configuration indicates that content tokens should dominate. For code inputs, the token embedding module 26B may maintain separate subspaces for identifiers, literals, and operators and may include a composition routine that derives identifier embeddings from constituent subtokens so that unseen identifiers may still be represented. For multilingual inputs, the token embedding module 26B may select language-specific slices of the embedding table or may apply a shared table and prepend a language indicator embedding that may be combined with the lexical vector for each token.

[0241] In some embodiments, the embedding space of the token embedding module 26B may differ from the hidden dimension used by the transformer encoder 30B. The token embedding module 26B may therefore emit vectors of a first length and may provide a projection layer or gating layer that maps the emitted vectors to the input dimension expected by the transformer encoder 30B, or the transformer encoder 30B may include an input projection that performs this mapping. The token embedding module 26B may also expose an interface to return both the preprojection and post-projection vectors for diagnostics requested by the controller 15B, and may record the projection parameters per model version so that saved artifacts remain compatible with later inference passes. The token embedding module 26B may further implement dropout on the emitted vectors during training, may apply learned scale factors per token category, and may include a normalization step that maintains statistics across batches for stability during optimization.

[0242] In some embodiments, the token embedding module 26B may output, for a sequence length L and an embedding width E, a tensor of shape L by E containing a floating-point vector for each token position, along with the attention mask and any segment type identifiers passed through unmodified; for example, given token identifiers [101, 12287, 602, 4219, 117, 7335, 102, 0, 0, 0] and E equal to 8 for illustration, the token embedding module 26B may emit vectors such as [[0.14, -0.07, 0.22, 0.03, -0.11, 0.09, 0.18, -0.05], [-0.02, 0.31, 0.08, -0.12, 0.05, 0.27, -0.04,-70- 078474-05869240.10], [0.06, -0.15, 0.19, 0.04, 0.02, 0.01, 0.12, 0.07], ..., [0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00]] where the final rows may correspond to padding tokens and may be all zeros or learned padding vectors according to configuration; the attention mask may remain [1, 1, 1, 1, 1, 1, 1, 0, 0, 0], and segment type identifiers may remain [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], The token embedding module 26B may also emit, when configured, a projected tensor of shape L by D that matches an input width D expected by the transformer encoder 30B, and may attach metadata fields indicating the vocabulary version, embedding table checksum, numeric precision used for the vectors, and any dropout or scaling settings applied during emission so that downstream components may interpret the vectors consistently.

[0243] In some embodiments, a positional encoding module 28B may receive a sequence of token embeddings or un-embedded tokens and may add or otherwise inject position information so that downstream attention operations may distinguish ordering and distance relationships. The positional encoding module 28B may accept a batch of vectors and a corresponding attention mask from the token embedding module 26B or tokens from tokenizer 24B and may output a position- conditioned tensor with the same leading dimensions. The positional encoding module 28B may maintain configuration identifying the positional scheme to apply, may track sequence length and index offsets, and may expose an interface to generate or look up position values for any index in the sequence, including indices beyond previously observed ranges. The positional encoding module 28B may operate on vectors prior to entry into a transformer encoder 30B or may provide position-dependent transformations that a downstream attention block may consume during score computation.

[0244] In some embodiments, the positional encoding module 28B may implement learned absolute position embeddings by maintaining a table that may map each position index to a vector of the same width as the token embeddings. The positional encoding module 28B may retrieve vectors for indices from zero through a configured maximum and may add the retrieved vectors elementwise to the token embeddings to produce position-conditioned embeddings. The positional encoding module 28B may support index shifting so that sequences composed from multiple segments may receive contiguous indices, and may include a padding position that may map to a zero or a learned vector when the attention mask indicates padding. The positional encoding module 28B may persist the learned table with model checkpoints and may expose routines to-71- 078474-0586924extend the table by appending additional rows and initializing them according to a seedable procedure when longer sequences are required.

[0245] In some embodiments, the positional encoding module 28B may implement sinusoidal position encodings by computing a set of deterministic vectors indexed by token position, where each vector may be derived from a family of periodic functions with different characteristic wavelengths. The positional encoding module 28B may generate these vectors on demand for any index without consulting a stored table, may add them to token embeddings, and may cache recently used vectors for efficiency. The positional encoding module 28B may provide this scheme when inputs may have unknown or varying maximum lengths because the deterministic generation may allow position vectors to be assigned for indices that were not seen during training, which is expected to allow models to process sequences longer than those used during parameter estimation. The positional encoding module 28B may also provide a mixed mode that may combine deterministic vectors with a small learned correction that may be stored in a compact table.

[0246] In some embodiments, the positional encoding module 28B may implement relative position methods in which the module may compute position information as a function of the difference between a query index and a key index and may supply this information to attention score computation. The positional encoding module 28B may precompute a banded set of relative offsets within a configured window and may provide per-offset vectors or scalar biases that downstream attention code may add to raw similarity scores. The positional encoding module 28B may maintain a policy for clamping or extrapolating offsets beyond the window and may expose routines to shift or scale offsets when sub-sampling or downsampling operations affect effective stride. The positional encoding module 28B may support rotary position transforms by applying a position-dependent rotation to query and key vectors prior to attention score computation; the module 28B may construct per-index rotation parameters and may apply the rotation in-place to minimize memory traffic.

[0247] In some embodiments, the positional encoding module 28B may account for different input modalities or formatting. For structured records containing multiple fields, the positional encoding module 28B may apply per-field position domains so that indices may restart for each field and may add field-domain identifiers that may be combined with the positional values to-72- 078474-0586924distinguish identical indices in different fields. For markup inputs, the positional encoding module 28B may apply layout-aware positions by incrementing indices based on both token order and line or block boundaries and may include a separate channel that may encode nesting depth for tags. For code inputs, the positional encoding module 28B may apply token positions that reflect lexical order and may attach block-level positions that may represent indentation or brace depth; the module 28B may expose controls to include or omit the block channel based on configuration. For multilingual inputs, the positional encoding module 28B may apply the same positional scheme across scripts or may maintain per-script offsets that may be combined with position vectors so that different scripts may not overlap in the index space when such separation is desired.

[0248] In some embodiments, the positional encoding module 28B may output, for a sequence length L and a positional width P, a position tensor of shape L by P and metadata that may include the attention mask and the zero-based position index assigned to each token position. The position tensor may contain one vector per position aligned to the input order; for example, for L equal to 6 and P equal to 8, the module 28B may emit [[0.00, 0.10, -0.07, 0.21, -0.03, 0.05, 0.12, -0.04], [0.00, 0.20, -0.14, 0.19, -0.06, 0.10, 0.24, -0.08], [0.00, 0.30, -0.21, 0.17, -0.09, 0.15, 0.36, -0.12], [0.00, 0.40, -0.28, 0.15, -0.12, 0.20, 0.48, -0.16], [0.00, 0.50, -0.35, 0.13, -0.15, 0.25, 0.60, -0.20], [0.00, 0.60, -0.42, 0.11, -0.18, 0.30, 0.72, -0.24]], an attention mask such as [1, 1, 1, 1, 1, 0] where the final position may be padding, and a position index array such as [0, 1, 2, 3, 4, 5], In some embodiments that apply relative or rotary encodings, the module 28B may instead or additionally output a compact structure that may include per-position rotation parameters or a table of relative-offset identifiers and associated coefficients, while preserving the same alignment to L so that downstream components may associate each position with either a direct position vector or with parameters required to compute position-conditioned attention scores.

[0249] In some embodiments, the outputs of the positional encoding module 28B may be combined with the outputs of the token embedding module 26B by pairing vectors at the same index so that the position representation for a given unit of input may be applied to the token embedding for that unit. When absolute position vectors are used and P matches the embedding width E (or after projecting one of the vectors to a shared width), the controller of the Al model 16B may instruct a preparation stage to add the vectors elementwise; for example, if the token embedding at index 2 may be [0.06, -0.15, 0.19, 0.04, 0.02, 0.01, 0.12, 0.07] and the position-73- 078474-0586924vector at index 2 may be [0.00, 0.30, -0.21, 0.17, -0.09, 0.15, 0.36, -0.12], the combined vector emitted to the transformer encoder 30B may be [0.06, 0.15, -0.02, 0.21, -0.07, 0.16, 0.48, -0.05], In some embodiments, the vectors may be concatenated to produce a length E+P vector and then mapped by a learned projection to the width expected by the transformer encoder 30B; in other embodiments, the token embedding module 26B may pass embeddings unchanged while the positional encoding module 28B may deliver rotation or offset parameters that the attention code may apply during score computation, with the pairing maintained by using the same position index and attention mask across both outputs.

[0250] In some embodiments, a transformer encoder 30B may consume a sequence of such vectors and may apply a repeating pattern of sublayers across a configurable number of blocks, e g., in a pipeline. A block may include an attention sublayer and a feed-forward sublayer with residual connections and normalization applied around each sublayer. The transformer encoder 30B may maintain configuration identifying the number of blocks, the number of attention heads, the hidden dimensions for projections, and the activation functions used in the feed-forward path. The transformer encoder 30B may accept masks indicating which positions are padding or otherwise excluded and may propagate those masks so that score computations and updates do not modify excluded positions. The transformer encoder 30B may operate in pre-normalization mode in which inputs to each sublayer may first be normalized and then transformed, or in postnormalization mode in which the sublayer output may be normalized after the residual merge, and the same ordering may be applied consistently within a run.

[0251] In some embodiments, the attention sublayer may compute per-head query, key, and value projections by multiplying the input sequence by learned weight matrices for each head. The attention sublayer may then compute similarity scores between queries and keys for each head, may apply attention masks that zero out or down-weight illegal or padded positions, and may apply a normalization over the score dimension to obtain per-head weights. The attention sublayer may multiply the per-head weights by the corresponding values to produce per-head outputs, may concatenate the per-head outputs along a channel dimension, and may apply an output projection to produce the sublayer output. The attention sublayer may incorporate relative position information by adding a learned or computed bias to the similarity scores as a function of the distance between positions, or may apply rotary transformations that rotate query and key channels-74- 078474-0586924according to position index prior to score computation. The attention sublayer may also implement structured sparsity by selecting a subset of key and value positions for each query according to a mask supplied by upstream logic, by restricting attention to local windows around each position, or by routing subsets of tokens to specialized attention experts, and these selections may be recomputed per block or held fixed across multiple blocks.

[0252] In some embodiments, an attention sublayer may implement scaled dot-product attention by projecting the input sequence into query, key, and value vectors, computing a score for each query-key pair by applying a similarity function over their projected channels, applying one or more masks that may zero or down-weight prohibited or padded positions, normalizing the scores across the key dimension using a selected normalizer such as a softmax or a sparse normalizer, and forming a weighted sum of the value vectors according to the normalized weights. The attention sublayer may repeat these steps independently across multiple heads using distinct projection matrices and may concatenate the per-head outputs, followed by a linear projection to the model dimension. The attention sublayer may support causal masks that may restrict each position to attend only to earlier positions, segment masks that may prevent score contributions across segment boundaries, and bias additions that may incorporate absolute or relative position information prior to the normalization step. The attention sublayer may accept alternative normalizers such as sparse normalization functions that may assign zero weights to low-scoring positions, and may implement temperature scaling and clipping prior to normalization.

[0253] In some embodiments, local or windowed attention may partition the sequence into windows and, for each query position, may restrict the set of candidate keys to positions within a configured neighborhood. The attention sublayer may compute scores only within the selected window, may optionally extend the neighborhood by a dilation stride that may skip positions at uniform intervals, and may merge overlapping neighborhoods by summing or averaging overlapping contributions. The attention sublayer may support blockwise schemes in which the sequence may be divided into fixed-size blocks and, for each query block, a set of key blocks may be selected using a static pattern, an index list, or a runtime policy derived from upstream signals; scores may then be computed only across the selected block pairs and aggregated per query position. The attention sublayer may apply top-k selection by first computing approximate or exact-75- 078474-0586924scores for a superset of candidate keys and then retaining only the k highest scores per query for normalization and value aggregation, where k may be constant or may vary with the query position.

[0254] In some embodiments, kernelized attention may approximate the softmax or other score functions with feature maps so that attention weights may be computed through linear operations. The attention sublayer may transform queries and keys with a random or deterministic feature map that may approximate the exponentiated similarity, may compute a key-side summary for each head by accumulating transformed keys multiplied by values across the sequence or block, and may obtain the output at a query position by multiplying the transformed query by the key-side summary and applying a normalization computed from the transformed query and a separately accumulated key-only summary. The attention sublayer may update these summaries incrementally across positions to support streaming inputs, and may reset or carry the summaries across block boundaries based on configuration. The attention sublayer may select the feature map from a library that may include trigonometric, Gaussian, or orthogonal feature constructions and may maintain per-head seeds or parameters for reproducibility.[00255J In some embodiments, low-rank attention may reduce computation by projecting keys and values to a smaller set of basis vectors before score computation. The attention sublayer may compute a set of basis keys and basis values by applying linear projections or learned pooling over the key and value dimensions, may compute scores between queries and basis keys, and may form outputs by weighting basis values according to the normalized basis scores. The attention sublayer may determine the number of basis vectors by configuration or may compute them adaptively from the sequence using a clustering or sketching routine executed per batch. The attention sublayer may also implement Nystrbm-style procedures by sampling a subset of landmark positions, constructing an attention approximation from those landmarks, and applying the approximation to all query positions using precomputed pseudoinverses or factorizations stored for the duration of the forward pass.

[0256] In some embodiments, multi-query and grouped-query attention may share key and value projections across multiple heads to reduce memory movement while maintaining separate query projections per head or per group of heads. The attention sublayer may compute a single set of keys and values for all heads or for a subgroup, may compute per-head or per-group queries,-76- 078474-0586924and may reuse the shared keys and values during score computation and value aggregation. The attention sublayer may implement expert attention by routing tokens to one or more attention experts according to a learned gate that may emit routing weights per token; each expert may perform attention over a subset of the sequence or over expert-local projections, and the outputs may be combined according to the gate weights. The attention sublayer may record routing statistics and may apply per-expert capacity limits, overflow handling, or auxiliary regularization during training.

[0257] In some embodiments, cross-attention may accept an external memory consisting of keys and values computed from another sequence or from an index that may have been built offline. The attention sublayer may project the current sequence into queries and may project the memory into keys and values, may compute scores between the queries and the memory keys, and may form outputs by aggregating the memory values according to normalized scores. The attention sublayer may combine self-attention and cross-attention within a block by applying self-attention first to refine the current sequence and then applying cross-attention to incorporate external context, or by interleaving the two with residual connections. The attention sublayer may use retrieval indices to select a subset of memory entries prior to score computation and may attach provenance identifiers to outputs so that downstream components may audit which memory entries contributed to the aggregation.

[0258] In some embodiments, relative-position and rotary-position attention may incorporate position information directly into the score computation. The attention sublayer may compute a relative offset between a query index and a key index and may add a learned or computed bias that depends on this offset to the raw similarity score prior to normalization. The attention sublayer may apply a rotation to the channels of the query and key vectors according to per-index rotation parameters, and may compute scores from the rotated channels while leaving values unmodified; the rotation parameters may be generated on demand for any sequence length and may be cached for reuse. The attention sublayer may clamp or bucket relative offsets beyond a configured window and may map large offsets to a shared set of parameters.

[0259] In some embodiments, normalization and stabilization procedures may be applied around attention computations. The attention sublayer may apply layer normalization or other-77- 078474-0586924normalizers to inputs before projection, may clip scores or subtract a per-query maximum prior to normalization, and may apply dropout to the normalized weights or to the value outputs during training. The attention sublayer may accept precision control signals that may select lower- precision storage with higher-precision accumulation, and may apply checkpointing to recompute intermediate tensors on demand. The attention sublayer may expose configuration to select among the described attention forms per block, may mix forms within a block by assigning different heads to different schemes, and may switch forms at runtime in response to control inputs from upstream components.

[0260] In some embodiments, the feed-forward sublayer may apply a position-wise transformation to each token representation independently. The feed-forward sublayer may include a first linear transformation that expands channel width, an activation such as a rectified linear unit, a gated linear unit, or a Gaussian error linear unit, and a second linear transformation that reduces channel width back to the model dimension. The feed-forward sublayer may include optional dropout on intermediate activations and may include layer-wise scaling parameters that modulate the output before the residual merge. The feed-forward sublayer may be replaced in some blocks by an expert routing layer in which a learned gate may select one or more experts to process a token’s vector and may combine expert outputs according to the gate weights, and the gate may receive an auxiliary loss or regularization to maintain balanced routing across experts.

[0261] In some embodiments, residual connections may add the input of a sublayer to the output of that sublayer, and normalization layers such as layer normalization may be applied either before or after the sublayer depending on the selected ordering. The transformer encoder 30B may support mixed-precision arithmetic for projections and activations with accumulation in a higher precision for stability, and may include gradient checkpointing during training to recompute certain activations on demand. The transformer encoder 30B may accept per-sequence or per-token metadata channels, including segment identifiers and position information, and may incorporate those channels into either the attention score computation or the sublayer inputs. The transformer encoder 30B may also accept control masks that specify causal relationships or block boundaries when inputs are formed from multiple segments, and the attention sublayer may clamp or rescale scores at those boundaries to prevent information flow across restricted segments.-78- 078474-0586924

[0262] In some embodiments, the transformer encoder 30B may expose hooks that permit intermediate features to be pooled or extracted at specified blocks. The encoder may accept a pooling directive from an upstream component indicating whether a classification token, a mean over unmasked positions, or an attention-weighted summary should be emitted at the end of the stack. The encoder may process mini-batches by padding sequences to a common length and may apply the attention mask so that padding positions do not affect similarity scores or value aggregation. The encoder may maintain per-block statistics such as activation ranges, token drop counts for sparse attention selections, and head-level routing summaries when expert attention is used, and may emit these statistics for diagnostics or downstream sensitivity scoring.

[0263] In some embodiments, the transformer encoder 30B may output, for each input sequence, a set of contextual token representations arranged as a tensor whose first dimension may correspond to sequence length and whose second dimension may correspond to a model width. The tensor may align one vector to each token position, and a parallel attention mask may indicate which positions are padding. For example, for a sequence of ten tokens and a model width of seven hundred sixty-eight, the transformer encoder 30B may emit ten vectors each of length seven hundred sixty-eight together with a mask that marks content positions as active and padding positions as inactive. These vectors may encode information derived from the surrounding tokens and any position signals applied earlier in the pipeline.

[0264] In some embodiments, the transformer encoder 30B may also emit a sequence-level feature vector that may summarize the token representations. The sequence-level feature vector may be obtained by selecting a classification token representation, by averaging vectors over unmasked positions, or by applying an attention-based pooling that may compute a weighted combination of token vectors using learned weights, and the selected method may be recorded in metadata. The sequence-level feature vector may be further mapped by a projection so that its length matches an input width expected by downstream components. The outputs may therefore include the per-token tensor, the attention mask, and the sequence-level feature vector, which may together describe what the model knows about the input at the end of the encoder: a fixed-length representation of the sequence and position-aligned token vectors that may be referenced by the Gaussian-process layer 32B and the likelihood and marginalization module 34B.-79- 078474-0586924

[0265] In some embodiments, a Gaussian process layer 32B may act as a probabilistic head that may take the sequence-level feature vector coming from the transformer encoder 30B and may compare that vector to a set of reference points (e.g., in an embedding space of the feature vector) stored in memory. The layer 32B may measure how similar the input vector is to each reference point, may combine those similarities with learned values kept at the reference points, and may produce, for each candidate class, a latent score that may represent what the model would predict and a companion spread value that may represent how uncertain that score may be. The layer 32B may repeat these steps independently for all classes or may compute them together when class relationships are modeled jointly, and may output a table of per-class scores and uncertainties that the likelihood and marginalization module 34B may turn into class probabilities.

[0266] In some embodiments, the Gaussian process layer 32B may also perform small randomized trials during inference by drawing several possible latent scores consistent with what the stored parameters may allow, may pass each draw through the same label-making step, and may average the resulting probability vectors so that the final probabilities may reflect both what the model predicts and how unsure it may be. The layer 32B may be trained by adjusting kernel settings that may control how similarity falls off with distance, by moving or adding reference points so that they cover the regions the feature vectors may visit, and by updating the stored values at those points using batches of examples. The layer 32B may refine certain settings by running short sequences of random updates that may stop when a stability check may be met, and may store the resulting settings so that later requests may use them directly or with a small number of additional draws.

[0267] In contrast to models that just infer an output, the Gaussian process layer, in some emobdiments, also says how sure it is about that inference, in a principled way. One can of the transformer as turning an input sentence into a detailed “fingerprint,” and the Gaussian process layer as checking how much that fingerprint resembles a library of known cases. When it finds close matches, it produces a strong score for a class and a small uncertainty, and when the input looks unfamiliar, it lowers the score and raises the uncertainty. That uncertainty lets the system act more intelligently at inference time: it can route challenging inputs to a slower, more careful path, ask for more context, or hand them to a person. It also is expected to give better-calibrated probabilities (e.g., that outputs are correct) and can adapt with relatively few new examples by-80- 078474-0586924adding or moving its reference points, so the user receives predictions (or other classifications) that are not only accurate but also candid about when they might be wrong.

[0268] In more precise terms, in some embodiments, a Gaussian process layer 32B may operate as a probabilistic head that may accept a sequence-level feature vector emitted by the transformer encoder 30B and may produce, for each candidate output class, latent quantities that may include a measure of central tendency and a measure of dispersion. The Gaussian process layer 32B may maintain parameters that may include kernel hyperparameters, noise parameters, and an inducingpoint set that may represent reference locations in the feature space. During an inference pass, the Gaussian process layer 32B may compute similarities between the input feature vector and the inducing-point set according to a selected kernel, may combine those similarities with stored posterior parameters over the inducing points, and may generate, for each class, a latent score together with a corresponding uncertainty. The Gaussian process layer 32B may emit these latent quantities for consumption by a likelihood and marginalization module 34B, and may also emit auxiliary diagnostics such as per-class influence weights of inducing points and summary statistics over the computed similarities.

[0269] In some embodiments, the Gaussian process layer 32B may implement a sparse variational procedure in which the inducing-point set may be smaller than the training set and may be learned jointly with kernel hyperparameters. The layer 32B may store a posterior over the function values at the inducing points as a set of learned means and learned covariances, and may retrieve a conditional latent distribution for a new feature vector by combining input-inducing similarities with those stored parameters. The layer 32B may support automatic relevance determination by maintaining a separate length-scale or bandwidth parameter per feature-channel group and may apply those parameters when computing similarities. The kernel used to compute similarities may be drawn from a library that may include radial basis, Matem family, periodic, linear, spectral mixture, or sums and products of those kernels, and the layer 32B may accept a configuration that selects one kernel or a composition per class or per head.

[0270] In some embodiments, the Gaussian process layer 32B may implement a multi-output procedure for classification in which either one independent latent process per class may be maintained or a joint latent process with cross-class covariance may be maintained. For the-81- 078474-0586924independent procedure, the layer 32B may keep separate inducing sets and kernel parameters per class or may share the inducing points while keeping class-specific posterior parameters. For the joint procedure, the layer 32B may store a block-structured set of posterior parameters over inducing points indexed by class and may compute class latents together so that cross-class dependencies may be captured. The layer 32B may export an interface to retrieve either only the per-class latent means and variances or, when requested, selected off-diagonal terms that may describe how pairs of class latents may co-vary.

[0271] In some embodiments, the Gaussian process layer 32B may support deep-kernel operation by passing the sequence-level feature vector through a projection network prior to kernel evaluation. The projection network may be a linear map, a gated linear block, or a small multilayer perceptron whose parameters may be trained with the rest of the system, and the projected vector may be used as the input to the kernel. The layer 32B may maintain normalization settings so that projected vectors may lie within a configured range before kernel evaluation, and may record projection parameters with model checkpoints. The layer 32B may also support heteroscedastic noise by accepting an auxiliary noise estimate per input, which may be produced by a small head fed from the transformer encoder 30B, and may incorporate that estimate into the dispersion measure it returns for each class.

[0272] In some embodiments, the Gaussian process layer 32B may be trained using variational inference that may optimize an objective formed from a fit term and a regularizer over the stored posterior parameters, and may process training data in mini-batches. The layer 32B may accept an initialization produced by maximizing a marginal-likelihood objective with gradient methods and may refine those parameters during variational updates. The layer 32B may also be trained using Markov chain Monte Carlo procedures applied to selected hyperparameters such as length-scales, kernel amplitudes, and noise terms. The layer 32B may implement adaptive chain lengths by monitoring chain statistics and may stop sampling a parameter when a convergence criterion may be satisfied, and may employ hierarchical sampling in which global or structural parameters may be sampled before lower-level parameters. The layer 32B may accept stochastic gradient-based Bayesian steps that may interleave with sampling steps so that hyperparameters may move with mini-batch gradients while still exploring uncertainty by sampling. The layer 32B may further-82- 078474-0586924employ quasi-Monte Carlo draws when forming predictive averages during training and evaluation to reduce the number of samples required for a given accuracy.

[0273] In some embodiments, the Gaussian process layer 32B may implement probabilistic dropout regularization during training by associating latent gating variables with connections that contribute to kernel computations or to inducing-point interactions. For each batch, the layer 32B may sample the gating variables to form a mask, may apply the mask by omitting or scaling contributions from selected connections or inducing points, and may compute a training objective that may represent an expectation over those gates using either direct sampling or a differentiable relaxation. When Bayesian inference may be active, the layer 32B may marginalize or sample the gating variables together with other parameters so that posterior evaluations consider multiple masked subnetworks. The layer 32B may record mask statistics per step for diagnostics and may prune connections whose gates may remain near zero for an extended period based on configuration.

[0274] In some embodiments, the Gaussian process layer 32B may be deployed in multiple configurations to support different architectural patterns. A single probabilistic head may produce latent quantities for all classes and may feed a single likelihood and marginalization module 34B. Multiple probabilistic heads may be arranged in parallel, where each head may receive the same feature vector and may produce separate latent quantities that may be combined by an aggregation module; in this arrangement, per-head kernels, inducing sets, or deep-kernel projections may differ, and the aggregation module may compute a weighted combination of the resulting latents or probabilities. In other embodiments, probabilistic heads may be stacked, where an upper head may receive as input the latent quantities or the aggregated probabilities emitted by a lower head and may refine them by computing an additional layer of latent quantities; for example, a lower head may operate with independent per-class processes while an upper head may apply a joint process to capture residual cross-class dependencies.

[0275] In some embodiments, the Gaussian process layer 32B may export uncertainty signals that the sensitivity scorer 18 may consume. The layer 32B may compute per-class dispersion measures and may combine them into an entropy-like signal or a mutual-information-like signal by forming averages over multiple latent draws or hyperparameter draws when requested by the-83- 078474-0586924controller 15B. The layer 32B may emit a compact record per request that may include inputinducing similarity weights, per-class latent measures, a scalar uncertainty used for routing, and optional provenance identifiers that may indicate which inducing points contributed most to the prediction. These fields may be logged and may be referenced by the mode selector 20B when deciding whether to invoke a deterministic path or a sampling-heavy path on subsequent steps of the request.

[0276] In some embodiments, the Gaussian process layer 32B may provide alternative algorithms or architectures that may produce uncertainty-aware latents with different tradeoffs. A Bayesian linear head may accept the sequence-level feature vector and may maintain a posterior over linear weights for each class; the head may draw or marginalize weights to produce latent means and dispersions and may expose the same likelihood interface as the Gaussian process layer 32B. A Monte Carlo dropout head may apply dropout at test time across multiple forward passes and may aggregate the resulting logits to approximate a distribution over latents, while exposing the same outputs and metadata fields. A deep-ensemble head may maintain multiple deterministic classifiers trained from different initial states and may combine their logits and spreads to produce latent means and dispersions. A Laplace-approximation head may compute a second-order approximation around a trained deterministic head and may produce a Gaussian posterior over weights that may be used to draw latent samples. These alternatives may plug into the same likelihood and marginalization module 34B and may be selected per model version or per tenant.

[0277] In some embodiments, the Gaussian process layer 32B may implement batching and caching strategies to reduce latency. The layer 32B may batch kernel computations across inputs by arranging feature vectors into a matrix and may compute input-inducing similarities in a single call so that memory movement may be reduced. The layer 32B may cache factorization artifacts associated with inducing-point posterior parameters, such as matrix decompositions or preconditioners, and may reuse those artifacts across requests until a parameter update may occur. The layer 32B may support mixed precision by storing kernel intermediates in reduced precision and accumulating sensitive reductions in higher precision. The layer 32B may expose a streaming interface that may accept one input feature vector at a time and may update running summaries so that outputs for the current input may be produced without recomputing summaries for previous inputs.-84- 078474-0586924

[0278] In some embodiments, the Gaussian process layer 32B may maintain tenant-scoped parameter sets so that different organizations may deploy tailored kernels, inducing sets, and priors without sharing state. The layer 32B may also maintain per-class calibration settings that may include temperature or link-function parameters used by the likelihood and marginalization module 34B, and may emit those settings with the latent quantities so that downstream computations may remain consistent with the stored configuration. The layer 32B may export lifecycle hooks that may respond to controller 15B messages to rotate parameter snapshots, to warm-start sampling chains with prior draws, or to update the inducing-point set by running a selection routine over recent feature vectors. The selection routine may score candidate inducing locations using coverage metrics over the observed feature distribution and may add, remove, or relocate inducing points according to configured budgets.

[0279] In some embodiments, the Gaussian process layer 32B may participate in active data selection by emitting acquisition scores that may be computed from the returned dispersion measures, from disagreement across multiple probabilistic heads, or from sensitivity signals that the sensitivity scorer 18B may compute. The controller 15B may write those acquisition scores to a queue consumed by the hybrid Bayesian training module 17B, which may then schedule future training batches that include records with higher scores. The Gaussian process layer 32B may record per-request latency and sample counts when sampling may be requested and may adjust its internal sampling budgets in response to mode selector 20B directives so that aggregate compute targets may be met. The layer 32B may further expose controls to cap the number of inducing points used at inference, to clip dispersion measures to bounded ranges for downstream stability, and to return compact summaries in place of full diagnostic records when a user device 14B may request a minimal response.

[0280] In some embodiments, the Gaussian process layer 32B may output, for each input sequence, a structured record that may include a per-class latent mean vector and a per-class latent variance vector, and in some cases a compact representation of cross-class covariance, together with identifiers for the model version and any hyperparameter sample used to produce the latents. For example, for three candidate output classes labeled “reset,” “billing,” and “other,” the layer 32B may emit latent means such as [1.20, -0.30, 0.10] and latent variances such as [0.05, 0.40, 0.20], where the first list may be interpreted as class-specific latent scores prior to any likelihood-85- 078474-0586924mapping and the second list may be interpreted as dispersion values paired positionally with those scores; when configured to report dependencies, the layer 32B may also include a small payload such as a lower-tri angular covariance summary or a per-class correlation list. The record may further include auxiliary fields such as the indices of the inducing points that most influenced the computation, their contribution weights, and a flag indicating whether heteroscedastic noise was applied, so that the likelihood and marginalization module 34B may transform the latents into predictive class probabilities and may derive uncertainty summaries using either deterministic or sampling-based paths.

[0281] In some embodiments, a likelihood and marginalization module 34B may receive, from a probabilistic head such as the Gaussian process layer 32B, records that may include per-class latent quantities for a sequence-level feature vector together with metadata describing hyperparameter samples, covariance structure, and requested inference mode. One can think of the likelihood and marginalization module 34B, in some embodiments, as the step that turns the Gaussian process layer’s “raw scores with uncertainty” into actual class probabilities the user can read and use, plus a number that summarizes how unsure the system is. In some embodiments, module 34B takes in, for each class, a latent mean (the raw score) and a latent variance (how wobbly that score might be), and sometimes extra samples or settings that describe uncertainty in the model’s own parameters. First, in some embodiments, module 34B applies a likelihood (e.g., with a rule for turning each possible set of latent scores into per-class chances that add up to one). Then module 34B, in some embodiments, does marginalization, e.g.., it does not rely on a single set of scores: it considers many plausible “what-if’ versions consistent with the uncertainty, converts each one into probabilities, and averages them. If time is limited, module 34B may do a quick one-pass version, or if the case is challenging, module 34B may draw more “what-ifs” before averaging.

[0282] The outputs, in some embodiments, are a probability for each class (for example, [reset: 0.82, billing: 0.06, other: 0.12]) and one or more uncertainty measures (for example, an entropy value showing how spread out those probabilities are). These results may go back to the controller 15B for logging and storage, to the user interface module 22B for display, and to the mode selector 20B so the system can decide whether to take a faster or more careful path next time. In short, in some embodiments, the module 34B takes the GP layer’s uncertain scores, applies a consistent-86- 078474-0586924rule to turn them into probabilities, averages over the uncertainty rather than ignoring it, and hands off clean probabilities and uncertainty numbers for the rest of the system to use.

[0283] To these ends or others, the module 34B may parse these records, may construct an internal work plan that identifies which dimensions are to be integrated by sampling and which dimensions are to be handled by closed-form updates, and may initialize accumulators for probability vectors, uncertainty summaries, and diagnostics. The module 34B may operate in batch across multiple inputs by arranging latents and optional covariance factors into contiguous device memory, may apply attention masks to ignore padded items, and may set random number generator seeds and sampling budgets according to directives from the controller 15B or the mode selector 20B.

[0284] In some embodiments directed to multi-class classification, the module 34B may apply a link function that may map per-class latent quantities to a probability simplex. The module 34B may, for a configured number of trials, draw one or more latent vectors consistent with the stored posterior over latents for a given input, may transform each draw by a selected link such as a normalized exponential or a probit-style mapping, and may accumulate the resulting probability vectors into a running average. When hyperparameter uncertainty may be present, the module 34B may nest latent draws inside hyperparameter draws by first selecting a hyperparameter record from a pool produced by a training procedure, then drawing latents conditional on that record, and then emitting contributions to the accumulators. The module 34B may track the number of effective draws used for each input and may record convergence indicators such as running variance of the accumulated probabilities and change in summary metrics over a sliding window of draws.

[0285] In some embodiments, the module 34B may implement approximate marginalization paths in place of or in addition to sampling. The module 34B may compute local curvature information around the latent mode and may apply a second-order approximation to estimate the contribution of latent dispersion to the class probabilities without drawing explicit samples, and may repeat the procedure for each relevant hyperparameter setting. The module 34B may apply expectation propagation style site updates by iteratively refining moment estimates that match transformed latents to an auxiliary family and may stop when a threshold on parameter movement may be reached. The module 34B may also support moment-matching procedures that may-87- 078474-0586924estimate transformed means and spreads by applying deterministic quadrature in a reduceddimensional subspace identified by principal directions of the latent covariance, and may combine those estimates with closed-form normalization steps. For correlated class latents, the module 34B may factor a stored covariance representation into a product of a low-rank term and a diagonal term, may apply draws or moment computations in the low-rank subspace, and may fold the diagonal contribution into the normalization.

[0286] In some embodiments, the module 34B may support likelihood families beyond multiclass classification. A regression path may accept a scalar latent and a noise parameter and may output a predictive mean and dispersion that may include both latent uncertainty and observation noise, with closed-form updates when the noise model may be Gaussian and with sample-based or quadrature-based updates for non-Gaussian noise models. A multi-label path may apply per-class binary links and may integrate each class marginal independently or with a stored dependence structure when provided. An ordinal path may map a latent score against learned thresholds to produce category probabilities and may integrate over the latent and thresholds according to the requested mode. A count-model path may apply a Poisson- or negative-binomial-style likelihood by transforming the latent by an appropriate nonlinearity and may average the resulting rate parameters over latent or hyperparameter variability. A heteroscedastic path may accept an auxiliary noise estimate and may condition the dispersion of the predictive distribution on that estimate during the combination step.

[0287] In some embodiments, the module 34B may implement sampling procedures with variance-reduction and budget-control features. The module 34B may employ quasi-Monte Carlo draws by generating low-discrepancy sequences in the base parameterization of the latent space and may transform those sequences to match the stored posterior by applying a reparameterization map that may not require explicit matrix inversion. The module 34B may generate antithetic pairs by reflecting base draws to reduce estimator variance, may apply stratification by dividing the draw space into bins according to magnitude of latent perturbations and sampling evenly across bins, and may compute control -variate corrections by subtracting a baseline transform with a known expectation. The module 34B may adapt the number of draws per input by estimating the stability of requested summary metrics, may stop when successive partial averages change by less than a configured tolerance, and may escalate to larger budgets when a gate supplied by the mode-88- 078474-0586924selector 20B may request a higher-fidelity evaluation. The module 34B may cache intermediate transform states for reuse across nearby inputs and may invalidate caches when kernel or hyperparameter identifiers change.

[0288] In some embodiments, the module 34B may apply normalization and stabilization steps to protect against numeric issues. The module 34B may subtract a per-sample offset from transformed latent values prior to normalization so that the resulting probability computation may remain within representable ranges, may clip intermediate values according to configuration, and may apply temperature scaling as a post-processing step when a calibration record may be present. The module 34B may handle masked positions by zeroing contributions and renormalizing over unmasked classes where a class subset may be active, and may handle cases where all but one class may be masked by emitting a one-hot probability vector. The module 34B may record normalization constants per input and draw and may expose those constants on request for audit or reproducibility.

[0289] In some embodiments, the module 34B may compute uncertainty summaries and diagnostics from the accumulated outputs. The module 34B may compute a predictive entropy for each input by applying a summary over the accumulated probability vector, may compute a perclass variance of the predicted probability by comparing per-draw probabilities to the average, and may compute a mutual-information-style metric when hyperparameter draws may be present by comparing entropy across and within draws. The module 34B may emit, for each input, the final probability vector, one or more uncertainty scalars, and a diagnostics record that may include the number of latent and hyperparameter draws used, the random seed identifiers, the presence or absence of variance-reduction techniques, and an indicator of whether adaptive stopping may have triggered. The module 34B may populate fields that the controller 15B may use to decide whether to retain detailed per-draw traces or only aggregate summaries and may write references to stored artifacts for later visualization by the user interface module 22B.

[0290] In some embodiments, the module 34B may use alternative architectures that may perform the same mapping from latent quantities to predictive distributions. A Dirichlet-mapping path may convert a vector of logits and a dispersion control into concentration parameters of a Dirichlet distribution and may compute class probabilities as the normalized expected values of-89- 078474-0586924those concentrations while integrating concentration uncertainty by draws or by a closed-form update when configured. A logistic-normal path may represent the predictive distribution as a transformed normal in the simplex and may apply sampling or moment procedures in the pretransform space before applying the link and normalization. A calibration-mapping path may apply a learned monotone transformation to logits prior to normalization, where the transformation may be stored per tenant or per model version, and may integrate over transformation parameters when those parameters may be expressed with a posterior from training. A stacked path may run multiple likelihood heads in sequence, where a first head may compute class probabilities from latents and a second head may refine those probabilities by mixing with a reference distribution conditioned on metadata.

[0291] In some embodiments, the module 34B may be organized for throughput and latency targets by combining vectorized kernels and asynchronous execution. The module 34B may execute per-batch transforms on a graphics processing unit device, may pipeline latent drawing and link application so that one set of draws may be in flight while aggregation may occur for the previous set, and may shard hyperparameter draws across workers that may return partial aggregates to a coordinator. The module 34B may export a streaming interface that may accept one input at a time, may emit partial probability vectors after a configured number of draws, and may finalize the output when a stopping condition may be met or when a maximum budget may be reached. The module 34B may also expose a deterministic path that may apply only a single transform of the latent means followed by normalization, and may switch between the deterministic and stochastic paths according to directives from the mode selector 20B or thresholds computed internally from preliminary sensitivity scores.

[0292] In some embodiments, the Al model 16B may be trained end-to-end on batches of records that may include tokenized inputs, segment and position metadata, and supervision targets such as class labels, ordinal categories, numeric responses, or multi-label indicators. During a training step, gradients may be computed with respect to parameters of the token embedding module 26B, the positional encoding module 28B when learned positions are used, the transformer encoder 30B, and parameters associated with a probabilistic head that may include the Gaussian process layer 32B and any projection feeding it. The system may apply one or more loss terms derived from a likelihood applied to outputs of the probabilistic head and, when uncertainty-90- 078474-0586924supervision may be provided, auxiliary losses defined on calibration or dispersion summaries. Parameters may be updated with an optimizer while maintaining checkpoints that may record model version, tokenizer configuration, and positional encoding settings so that inference services may reference consistent artifacts.

[0293] In some embodiments, training may be staged so that different parts of the Al model 16B may be trained independently or with different update policies. A pretraining phase may train the transformer encoder 30B on self-supervised objectives constructed from unlabeled corpora, such as masked token prediction or next-span prediction, while the token embedding module 26B and the positional encoding module 28B may be updated j ointly or partially frozen depending on configuration. A subsequent fine-tuning phase may introduce task labels and may update the transformer encoder 30B together with a probabilistic head. In some cases, the transformer encoder 30B may be held fixed while only a projection and the Gaussian process layer 32B may be trained, and in other cases small adaptation modules such as low-rank adapters may be trained while the base encoder remains read-only. The Gaussian process layer 32B may receive its own training schedule, which may include fitting kernel and noise hyperparameters, learning inducing-point locations and posterior statistics, and, when used, training an auxiliary heteroscedastic noise head emitted by the transformer encoder 3 OB.

[0294] In some embodiments, the Al model 16B may be trained on heterogeneous datasets drawn from tenant-specific domains and shared pools. Example training sources may include customer-support transcripts labeled into categories, compliance documents labeled with risk classes, software issue reports with multi-label tags, product descriptions mapped to taxonomy nodes, and semi-structured records with fields mapped to target attributes. Additional variants may include ordinal ratings for prioritization, continuous targets such as time-to-resolution for regression, and weak labels produced by heuristic rules or distant supervision. When multiple tasks may be present, the controller 15B may alternate tasks per batch and may route task-specific heads or adapters while sharing the transformer encoder 30B, and the probabilistic head may be instantiated per task or shared with task identifiers passed as conditioning inputs. Tenant-scoped fine-tuning may train small adapter weights or a projection into the Gaussian process layer 32B while leaving shared parameters unchanged, and periodic refresh cycles may retrain selected components using newly accrued data without modifying frozen components.-91- 078474-0586924

[0295] In some embodiments, a hybrid Bayesian training module 17B may coordinate parameter estimation for the Al model 16B and a probabilistic head that may include the Gaussian process layer 32B, and may operate as a set of services that may prepare data, drive optimization and sampling loops, evaluate diagnostics, and commit checkpoints. One can think of hybrid Bayesian training as using two complementary learning styles together so the model learns quickly and also knows how unsure it is. The first style may be gradient training, e.g., where the system looks at many examples in small batches and nudges its knobs in the direction that makes mistakes smaller. This approach is often quick and scales to large datasets. The second style may be Bayesian sampling, e.g., where for a few particularly impactful parameters (like the Gaussian- process layer’s kernel settings or noise levels), the system does short, guided “what-if’ runs to explore multiple plausible values that fit the data. Instead of locking those parameters to a single number, the system 12B keeps a small set of reasonable possibilities.

[0296] This mix, in some embodiments, is used because it is expected to provide the best of both worlds. The gradient part learns good features and reasonable defaults quickly. The Bayesian part keeps track of uncertainty about the model’s own settings, which tends to produce probabilities that line up better with reality and honest “I’m not sure” signals. In practice, training cycles may alternate: many fast gradient steps to improve the network, then brief sampling bursts on the uncertain parameters, stopping each burst when simple convergence checks say “you have sampled enough.” Later, when making predictions, the model may average across those sampled settings rather than pretending it knows them exactly.

[0297] In short, some embodiments of this hybrid training entails: prepare batches of labeled data; do standard back-prop updates on the transformer and head; every so often, pause to sample several reasonable settings for the Gaussian-process hyperparameters; keep the ones that fit well; repeat. Along the way the system may use approaches like low-discrepancy “random” draws, lightweight dropout-style regularization, or small adapter layers, all to keep training stable and efficient while preserving a clear picture of uncertainty.

[0298] To these ends or others, the module 17B may ingest training records, may normalize and tokenize inputs using the tokenizer 24B configuration selected by a tenant, and may create shuffled mini-batches with sequence-length bucketing to reduce padding. The module 17B may-92- 078474-0586924construct per-batch requests to the AT model 16B that may include mode flags for training, precision settings, dropout seeds, and an identifier of the current parameter snapshot. The module 17B may maintain a schedule that may interleave stochastic gradient-based updates with Bayesian sampling phases and may advance that schedule according to wall-clock targets, gradient-norm monitors, and convergence signals received from downstream samplers.

[0299] In some embodiments, a stochastic gradient-based Bayesian inference loop may run for a configured number of mini -batches and may update parameters of the transformer encoder 3 OB, any projection network feeding the Gaussian process layer 32B, kernel hyperparameters, and variational parameters tied to inducing-point values. The module 17B may compute per-batch losses formed from a fit term and regularizers, may backpropagate through the network, and may apply an optimizer that may include momentum, adaptive moments, or a schedule with warmup and decay. When a stochastic gradient Markov method may be enabled, update steps may incorporate calibrated noise into parameter updates so that iterates may approximate draws from a posterior over parameters; the module 17B may set the noise scale from the mini-batch size and a temperature parameter and may reduce or increase the scale over time under a schedule recorded with the run metadata. The module 17B may support variational inference by maintaining means and covariances for a distribution over inducing-point values, may compute gradients of an evidence lower bound, and may optionally apply natural-gradient or coordinate updates to variational blocks before returning to the outer optimizer. The module 17B may track training and validation metrics including negative log likelihood, calibration error, accuracy, effective sample counts for variational parameters, and gradient statistics keyed by layer, and may attach these measurements to checkpoints.

[0300] In some embodiments, a sampling phase may draw hyperparameters and, when configured, inducing -point variables from their posteriors. The module 17B may allocate chains across devices, may initialize each chain from the current point estimate or a nearby perturbation, and may step each chain using a selected transition rule such as a gradient-informed proposal, a slice step, or a random-walk proposal with adaptive scaling. The module 17B may compute convergence diagnostics including a Gelman-Rubin statistic and an effective sample size estimate on sliding windows, and may terminate or extend chains on a per-parameter basis according to thresholds. The module 17B may maintain a hierarchy in which global kernel-amplitude and noise-93- 078474-0586924parameters may be sampled first, followed by length-scale groups, and then any class-specific parameters, and may condition proposals at each level on draws from higher levels. The module 17B may thin chains to reduce autocorrelation, may store draws in a ring buffer with checksums, and may expose a sampler-state snapshot so that subsequent sessions may warm-start from a previous endpoint.

[0301] In some embodiments, the module 17B may reduce sampling cost by applying quasiMonte Carlo and variance-reduction procedures. For predictive averaging used during training or validation, the module 17B may generate low-discrepancy sequences in a base space, may transform those sequences to the target latent space using a reparameterization map that may depend on a stored factorization, and may pair draws with antithetic counterparts to cancel oddorder error terms. The module 17B may stratify draws by magnitude bands and may sample evenly across bands, and may apply a control-variate baseline formed from a deterministic transform of latent means so that the residual sampling variance may be smaller. The module 17B may adapt draw counts to reach stability tolerances on tracked summary metrics and may route difficult batches to a higher-fidelity path while keeping an overall budget by reducing draws on easier batches identified by a sensitivity scorer 18B operating in training mode.

[0302] In some embodiments, the module 17B may apply probabilistic regularization during training by sampling latent gating variables that may stochastically omit or scale connections or inducing-point contributions. The module 17B may draw gates per batch from a specified prior family, may apply the resulting mask to kernel or value computations when computing the training objective, and may backpropagate through a differentiable relaxation when required. The module 17B may log per-connection gate frequencies and may apply pruning passes that may remove units or inducing points whose gates may remain near zero under a defined window. The module 17B may further run inducing-point maintenance, in which the set of inducing locations may be scored against a reservoir of recent feature vectors, and may add, relocate, or remove inducing points to improve coverage, while updating cached factorizations used for fast prediction. The module 17B may coordinate these maintenance steps with the controller 15B so that inference services may switch to new parameter snapshots only after consistency checks may pass.-94- 078474-0586924

[0303] In some embodiments, the module 17B may support multi-output Gaussian process configurations and may select between independent per-class processes and joint processes with cross-class covariance. For independent processes, the module 17B may maintain separate variational or sampler states per class and may share inducing locations while storing class-specific variational means and covariances. For joint processes, the module 17B may maintain block- structured states and may update off-diagonal covariance blocks with low-rank parameterizations to control memory. The module 17B may support deep-kernel configurations by training a projection network in front of the Gaussian process layer 32B and may normalize projected vectors to a configured range before kernel evaluation. The module 17B may optionally train a heteroscedastic noise head that may output per-input noise estimates, and may include a step to calibrate noise head outputs against held-out data before including them in predictive dispersion during validation.

[0304] In some embodiments, the module 17B may coordinate with a mode selector 20B and a sensitivity scorer 18B to apply feedback from inference to training. The module 17B may receive streams that may summarize inputs with high uncertainty, disagreement across probabilistic heads, or rising latency, and may allocate additional draws or parameter updates to batches drawn from these streams. The module 17B may compute acquisition scores based on predicted uncertainty, label availability, or tenant-provided priorities, and may select records for future annotation or reweighting. The module 17B may update sensitivity thresholds used at inference by analyzing recent calibration metrics and may write those thresholds to a configuration store, where the controller 15B may pick them up and route future requests accordingly.

[0305] In some embodiments, the module 17B may support alternative training variants that may produce similar outputs without maintaining a full Gaussian process posterior. A Bayesian linear head variant may maintain a posterior over linear weights on top of transformer features and may update that posterior with stochastic gradient Markov methods or variational updates; the module 17B may record weight draws and may export them to the likelihood and marginalization module 34B for predictive averaging. A deep-ensemble variant may train multiple deterministic heads from different initial conditions, may checkpoint each head separately, and may aggregate logits and spreads across heads; the module 17B may balance data shards across heads and may track per-head calibration. A Monte Carlo dropout variant may keep a single deterministic head,-95- 078474-0586924may apply dropout at training and test time, and may record seed streams so that repeated predictive passes may be reproducible; the module 17B may schedule test-time passes during validation to produce uncertainty summaries. A Laplace-approximation variant may fit a second- order approximation around a trained head and may derive a Gaussian approximation over weights; the module 17B may compute curvature information with a block-diagonal or low-rank approximation and may export the approximation parameters for use during predictive marginalization.

[0306] In some embodiments, the module 17B may implement rigorous checkpointing and reproducibility procedures. The module 17B may assign monotonically increasing version identifiers to parameter snapshots, may store optimizer states, sampler states, and random number generator seeds with each snapshot, and may write manifest files that may list configuration hashes, vocabulary and positional encoding versions, and training data slices. The module 17B may expose atomic snapshot handoff by writing new snapshots to a staging location, running an integrity check, and then updating a pointer consumed by inference services. The module 17B may support rollbacks by retaining a window of previous snapshots, and may support canary deployments by stamping tenant allow-lists so that only selected tenants may receive new snapshots until metrics may indicate stability.

[0307] In some embodiments, the module 17B may be designed for distributed execution. The module 17B may shard training batches across accelerator devices, may run all-reduce operations to aggregate gradients and sampler statistics, and may synchronize variational parameters or sampler hyperparameters at configurable intervals. The module 17B may run chains on separate workers and may merge draws when computing validation metrics, and may route heavyweight updates such as inducing-point relocation to off-peak windows coordinated by the controller 15B. The module 17B may compress communication by quantizing gradient and sampler messages and may apply error compensation to maintain accuracy. The module 17B may expose a monitoring interface that may stream metrics and sample traces to the user interface module 22B for inspection by a tenant.

[0308] In some embodiments, the module 17B may enforce policy and security constraints during training. The module 17B may honor tenant-scoped parameter sets, may isolate data and-96- 078474-0586924parameter states by tenant identifiers, and may rotate signing keys and access tokens when writing and reading parameter snapshots from storage. The module 17B may apply differential privacy procedures when configured by adding calibrated noise to gradients and clipping per-record contributions prior to aggregation, and may report privacy budget usage in the snapshot manifest. The module 17B may redact or hash sensitive tokens during logging, may throttle sampling when resource limits may be reached, and may record audit trails of major actions including sampler starts and stops, inducing-point updates, and snapshot activations.

[0309] In some embodiments, a sensitivity scorer 18B may accept, as inputs, per-token hidden representations emitted by a transformer encoder 30B, a sequence-level feature vector, attention masks, and outputs from a probabilistic head that may include latent means, latent variances, and hyperparameter samples from a Gaussian process layer 32B. The output sensitivity score may be a single number that indicates, roughly “how much would the model’s answer change if this part of the input changed a little?” The system may compute one score for each input token or for groups of tokens (like a sentence, a field in a form, or a section of code). Scores near “low” may mean the output would barely move if that part changed; scores near “high” may mean that part is influential.

[0310] To compute the score in some embodiments, the sensitivity scorer 18B takes inputs it already has during inference: the token representations coming out of the transformer, the model’s current predicted probabilities, and the uncertainty signals from the Gaussian-process head. Then it runs a quick test to estimate influence. That test can be done a few ways: tiny “what-if’ nudges to the token vectors and measuring how the predicted probabilities shift; using the model’s gradients as a shortcut for those nudges; or reading uncertainty directly from the probabilistic head and attributing it back to tokens or groups. The raw influences may scaled into comparable scores, smoothed over neighboring tokens, and collected into per-token and per-group numbers the rest of the system 12B can use.

[0311] These scores are expected useful because they tell the system where to spend effort. High-sensitivity regions can be routed to a more careful path (for example, do extra sampling before deciding), while low-sensitivity regions can take the faster path. The scores may also power simple explanations (e.g., heatmaps over the text showing which parts mattered most) and they-97- 078474-0586924may help with data curation by flagging inputs where the model seems touchy or uncertain so those can be reviewed or prioritized for labeling.

[0312] The sensitivity scorer 18B may also accept a target specification that may identify which output quantity to attribute, such as a selected class probability, a vector of class probabilities, a scalar uncertainty summary, or a composite that may combine both probability and uncertainty terms. The sensitivity scorer 18B may construct a working copy of the forward state for the current request, may select the target quantity, and may initialize buffers sized to the token length or to predefined groups of tokens supplied by configuration or by a grouping service.

[0313] In some embodiments, a first class of procedures may estimate per-token influence by applying small perturbations to token-level inputs and measuring the corresponding change in the target quantity. The sensitivity scorer 18B may generate a perturbation plan that may identify which tokens to nudge, the magnitude of each nudge expressed relative to the scale of the token embeddings, and whether perturbations may be applied as additive noise, as masked substitutions with a neutral token, or as controlled rewrites drawn from a small synonym table. The sensitivity scorer 18B may apply each perturbation while holding all other tokens fixed, may re-run the forward path through the transformer encoder 30B and the probabilistic head, and may record the change in the target quantity relative to the unperturbed run. The sensitivity scorer 18B may repeat these steps for each token and may optionally apply bidirectional nudges to improve symmetry before aggregating the recorded changes into a raw sensitivity value per token.

[0314] In some embodiments, a second class of procedures may estimate influence by reading gradients of the target quantity with respect to intermediate representations, which may reduce the number of forward evaluations. The sensitivity scorer 18B may mark the target quantity for differentiation, may back-propagate through the probabilistic head and the transformer encoder 30B to obtain a gradient tensor aligned to the token sequence, and may compress the gradient at each position into a scalar by applying a norm over channels or by taking a signed projection onto the corresponding token embedding. The sensitivity scorer 18B may combine gradient information with the original token representation by computing a path-based accumulation that may sample intermediate points between a baseline representation and the current representation and may average the per-sample gradients before scalar reduction. The sensitivity scorer 18B may-98- 078474-0586924normalize the resulting scalars across positions and may record them as per-token sensitivity values.

[0315] In some embodiments, a third class of procedures may attribute uncertainty returned by the probabilistic head back to tokens or groups by decomposing variance across controlled experiments. The sensitivity scorer 18B may hold the transformer encoder 30B state fixed, may request multiple draws of latent quantities or hyperparameters from the Gaussian process layer 32B, and may compute for each draw the target uncertainty quantity. The sensitivity scorer 18B may then apply token-level masks that may neutralize or clamp a subset of token contributions, may recompute the uncertainty quantity under those masks, and may record the change as the contribution of the masked subset. The sensitivity scorer 18B may enumerate single-token masks and multi-token masks based on a sampling plan and may aggregate recorded changes into per- token and per-group uncertainty attributions that may sum, up to approximation error, to the total uncertainty.

[0316] In some embodiments, multi-level feature grouping may be applied before scoring so that related tokens may share a single sensitivity index. The sensitivity scorer 18B may receive grouping directives that may define groups such as linguistic constructs, semantic clusters, code blocks, or form fields, and may roll up token-level representations into group descriptors by averaging or by applying a small attention pooling per group. The sensitivity scorer 18B may run any of the perturbation-based, gradient-based, or uncertainty-decomposition procedures on the group descriptors by muting or nudging entire groups at once, and may record group scores alongside token scores. The sensitivity scorer 18B may maintain a mapping between tokens and groups so that group scores may be broadcast back to tokens for display or for routing decisions.

[0317] In some embodiments, the sensitivity scorer 18B may implement variance-based sensitivity analysis using low-discrepancy sampling to reduce the number of model evaluations. The sensitivity scorer 18B may define an input subspace formed by token-level or group-level factors, may sample factor settings using a sequence that may evenly cover the subspace, and may evaluate the target quantity under each sampled setting while reusing cached intermediate results where allowed. The sensitivity scorer 18B may accumulate contributions that correspond to main effects and interaction effects by combining evaluations that share factor settings, and may scale-99- 078474-0586924the contributions so that the accumulated effects approximate the variance of the target quantity across the sampled subspace. The sensitivity scorer 18B may limit the subspace to the most influential factors based on preliminary gradients or perturbation magnitudes to control evaluation cost.

[0318] In some embodiments, attention-informed procedures may propagate importance through the transformer encoder 30B’s attention structure to derive sensitivity signals without additional forward passes. The sensitivity scorer 18B may read stored attention weights for each head and layer, may collapse the weights across heads with a head-importance weighting, and may multiply the collapsed weights across layers to compute how much information from each source position may reach a sink position associated with the sequence-level feature vector. The sensitivity scorer 18B may combine this propagation result with a sink-side gradient or with a change in the target quantity measured at the sink to form a token-level importance value. The sensitivity scorer 18B may clamp or rescale contributions according to masks that reflect restricted attention patterns so that scores remain consistent with causal or segment boundaries.[00319J In some embodiments, the sensitivity scorer 18B may perform topological aggregation before or after computing raw scores to expose neighborhoods of related tokens. The sensitivity scorer 18B may construct a graph whose nodes may correspond to tokens or groups, with edges that may reflect semantic similarity, co-attention strength, or proximity, and may cluster the graph into neighborhoods using a selected clustering routine. The sensitivity scorer 18B may sum or average raw scores within each neighborhood and may attribute interaction terms to edges proportional to measures of interdependence observed during perturbation or sampling. The sensitivity scorer 18B may output both per-node scores and per-neighborhood aggregates together with edge annotations that may record inter-neighborhood influence.

[0320] In some embodiments, the sensitivity scorer 18B may operate in a streaming mode that may compute preliminary scores as soon as the transformer encoder 30B emits early-layer states, and may refine the scores as deeper-layer states become available. The sensitivity scorer 18B may budget computation by first emitting a coarse gradient-based score, may request a small number of perturbation evaluations for positions whose coarse scores exceed a threshold, and may defer uncertainty-decomposition runs to a later phase if the controller 15B directs additional processing.-100- 078474-0586924The sensitivity scorer 18B may track stability by measuring how much scores change across refinement steps and may increase or decrease budgets accordingly under a policy maintained by the controller 15B.

[0321] In some embodiments, the sensitivity scorer 18B may include calibration and normalization steps that may prepare scores for downstream consumption. The sensitivity scorer 18B may apply token-length normalization so that longer sequences do not systematically receive lower per-token scores, may scale scores into a configured numeric range, and may smooth scores across neighboring tokens with a short window or with a learned filter to reduce isolated spikes. The sensitivity scorer 18B may clip extreme values to a configured bound, may record the clipping rate, and may write per-request calibration metadata such as running means and variances used for normalization so that scores may be compared across requests.

[0322] In some embodiments, the sensitivity scorer 18B may support alternative formulations that may not require gradients or repeated full forward passes. The sensitivity scorer 18B may approximate influence using a local linear model fit at the sequence-level feature vector by sampling a small number of synthetic feature perturbations and regressing the target quantity onto those perturbations; the resulting coefficients may be projected back to tokens using the pooling weights or by solving a small reconstruction problem. The sensitivity scorer 18B may read internal gates or masks from mixture-of-experts components or structured-sparse attention components and may treat those gates as importance weights that may be combined with token activations to form a score without additional evaluations. The sensitivity scorer 18B may also approximate sensitivity by computing per-token contribution to a loss surrogate built from cached logits and uncertainty summaries and by comparing the surrogate under neutralized and active token states.

[0323] In some embodiments, the sensitivity scorer 18B may emit a structured output record that may include per-token scores, per-group scores, optional neighborhood aggregates, and diagnostics for the procedure used. The record may list the scoring mode, the number of perturbations or draws performed, gradient ranges, normalization parameters, and any masks applied. The sensitivity scorer 18B may attach provenance fields that may reference the model version, the transformer encoder 30B block indices used for attention-informed procedures, and the Gaussian process layer 32B identifiers used for uncertainty-decomposition procedures. The-101- 078474-0586924record may be written to storage, may be passed to a mode selector 20B, and may be provided to a user interface module 22B for later rendering without restricting how the scores may be used by other components.

[0324] In some embodiments, the sensitivity scorer 18B may implement safeguards and resource controls. The sensitivity scorer 18B may bound the number of perturbation evaluations per request, may share cached intermediate tensors across token perturbations to reduce repeated work, and may parallelize independent evaluations on a graphics processing unit device or across worker processes. The sensitivity scorer 18B may redact or obfuscate token content when writing logs, may respect tenant-specific policies that restrict which procedures may be applied, and may add calibrated noise to scores when a privacy mode may be configured. The sensitivity scorer 18B may monitor runtime metrics, may back off to gradient-only procedures when resource limits may be reached, and may resume higher-cost procedures when budgets may be reset by the controller 15B.

[0325] In some embodiments, the sensitivity scorer 18B may emit a structured record that may include per-token scores aligned to the original sequence, optional group scores, and diagnostics; for example, given tokens [CLS], “reset”, “my”, “password”, “, ”, “please”, [SEP], the scorer 18B may return per-token sensitivities such as [0.00, 0.62, 0.08, 0.71, 0.03, 0.21, 0.00] on a zero-to- one scale where [CLS] and [SEP] may be fixed at zero, together with group scores such as {“intent_terms”: 0.74 for {“reset”, “password”}, “politeness_markers”: 0.21 for {“, ”, “please”}} and an uncertainty-attribution vector such as [0.00, 0.38, 0.04, 0.42, 0.01, 0.09, 0.00] that may represent the proportion of predictive variance attributed to each token under the selected procedure; the record may carry the attention mask [1, 1, 1, 1, 1, 1, 1], the scoring mode identifier (for example, “gradients+perturbation”), the number of perturbations or latent draws performed (for example, 16), normalization parameters used for scaling, and provenance fields such as model version and transformer block indices consulted, and may be serialized for routing to the mode selector 20B and for rendering by the user interface module 22B.

[0326] In some embodiments, a mode selector 20B may receive, for each request, a compact context record assembled by the controller 15B that may include preliminary class probabilities from the likelihood and marginalization module 34B when available, scalar or vector uncertainty-102- 078474-0586924summaries derived from outputs of the Gaussian process layer 32B, per-token and per-group sensitivity scores from the sensitivity scorer 18B, and operational constraints such as latency budget, maximum sampling budget, and tenant policy flags. The mode selector 20B, in some embodiments, can be thought of as a traffic cop for compute. It may look at quick signals (like the model’s current confidence, the uncertainty from the Gaussian-process head, and the sensitivity scores over the input) and decide whether to take a fast lane or a careful lane. Inputs may include: the preliminary class probabilities, an uncertainty number (how unsure the model is), token / group sensitivity scores, and simple context like request size or latency budget. Using a rules or thresholds, selector 20B may output a directive such as “use the deterministic path” (e.g., one clean pass with fixed settings) or “use the Bayesian path” (e.g., do extra sampling and averaging). In some embodiments, selector 20B sends that directive back to the controller 15B, which then runs the Al model 16B in the chosen mode and forwards the final results to the user interface module 22B.

[0327] An expected advantage is that, in some embodiments, the system spends effort where it matters. If the input looks familiar and low-risk, the mode selector 20B keeps things fast. If the input looks unusual or important, selector 20B may ask the system to slow down and gather more evidence before deciding. Over time, selector 20B may also update its thresholds based on feedback (e.g., if many “fast” cases later turn out to be tricky, it will become more cautious for similar inputs). In short: the mode selector 20B in some embodiments reads quick signals from elsewhere in the Al system 12B, chooses the evaluation style, and helps balance speed and reliability without changing the underlying model.

[0328] To these ends or others, the mode selector 20B may validate the context record, may impute defaults for missing fields, and may normalize inputs to reference scales recorded with the active model version. The mode selector 20B may then compute feature values used for decision making, which may include aggregates over sensitivity scores, dispersion statistics over candidate class probabilities, and simple counters such as sequence length or proportion of masked tokens. The mode selector 20B may apply guard rules that may immediately direct a deterministic path when inputs violate resource limits, may direct a higher-fidelity Bayesian path when any safety flag may be present, or may defer to a learned policy otherwise.-103- 078474-0586924

[0329] In some embodiments, a rules-based policy may be implemented as a sequence of threshold comparisons and branching operations. The mode selector 20B may compare a predictive entropy against a configured boundary, may evaluate whether the maximum class probability falls below a confidence threshold, and may inspect whether any group sensitivity score exceeds a per-tenant ceiling that may indicate the presence of influential content. The mode selector 20B may compute a routing score as a weighted combination of these features, where the weights may be loaded from configuration, and may select among modes such as deterministic evaluation, low-budget sampling, or high-budget sampling based on score intervals. The mode selector 20B may record the feature vector, thresholds used, and the final directive in a decision log, may attach a monotonic decision identifier to the request, and may emit the directive to the controller 15B together with numeric budgets such as the number of latent draws, the number of hyperparameter draws, and any limits on structured sparse attention reconfiguration.

[0330] In some embodiments, a learned policy may be used in place of fixed rules. The mode selector 20B may load a compact model such as a gradient-boosted tree, a small multilayer perceptron, or a linear classifier trained over historical features and outcomes. The learned policy may accept the same feature vector described above and may output a mode label and budgets. The mode selector 20B may calibrate the learned policy scores against holdout data by applying a monotone mapping stored in configuration so that a policy score may be interpreted consistently across model versions. The mode selector 20B may support bandit-style exploration by randomizing among near-tie actions at a configured low rate, and may write action and outcome tuples to a buffered log that the controller 15B may export for offline policy retraining. The mode selector 20B may also maintain per-tenant overlays so that a default global policy may be adjusted by tenant-specific constraints, including caps on compute or stricter routing to deterministic paths for certain request classes.

[0331] In some embodiments, the mode selector 20B may support multi-stage decisions. A preliminary decision may be made after the transformer encoder 30B emits early-layer summaries, which may authorize a provisional deterministic path or a low-budget sampling pass, and a final decision may be made after the Gaussian process layer 32B returns updated uncertainty metrics, which may escalate the budget when a stability check fails. The mode selector 20B may implement stability checks by comparing partial aggregates from the likelihood and marginalization module-104- 078474-058692434B across successive batches of samples and may increase or decrease budgets to meet a target tolerance recorded with the run configuration. The mode selector 20B may also request refinement of sensitivity scores at selected spans before committing to a high-budget path by instructing the sensitivity scorer 18B to run a small set of perturbation evaluations on tokens whose gradientbased scores cross a boundary.

[0332] In some embodiments, the mode selector 20B may enforce resource governance. The mode selector 20B may maintain per-tenant and global counters for sampled draws, GPU-seconds, memory reservations, and concurrent Bayesian jobs. Before emitting a directive, the mode selector 20B may check these counters and may downgrade the requested budgets when limits are near exhaustion, while marking the decision with a resource-constrained flag for audit. The mode selector 20B may coordinate with the controller 15B to queue deferred high-budget actions for later execution or to split them into partial passes with intermediate results returned to the user interface module 22B. The mode selector 20B may periodically refresh limits and policy parameters from a configuration store and may roll over counters at time windows defined by tenant contracts.

[0333] In some embodiments, alternative implementations may delegate the decision to a rules engine maintained outside the runtime. The mode selector 20B may serialize the feature vector and policy context to a declarative representation, may call an external policy evaluation service, and may receive a decision and budgets encoded as a compact payload. In another variant, the mode selector 20B may embed the decision inside the likelihood and marginalization module 34B so that sampling budgets may adapt internally as partial results arrive, and the mode selector 20B may act as a recorder that publishes the final chosen budgets and any escalations performed. In yet another variant, the mode selector 20B may be integrated with the sensitivity scorer 18B so that token- or group-level routing may be supported; for example, the mode selector 20B may instruct structured sparse attention in selected transformer blocks to switch to higher-capacity experts for spans whose sensitivity exceeds a threshold while leaving other spans in a low-capacity path, and may propagate these choices as per-layer masks back to the controller 15B.

[0334] In some embodiments, the mode selector 20B may implement feedback and calibration procedures. After the controller 15B finalizes a response, the mode selector 20B may receive-105- 078474-0586924outcome summaries such as latency consumed, sample counts used, calibration error on held-out traces when available, and user override signals collected by the user interface module 22B. The mode selector 20B may adjust thresholds by small increments based on moving averages, may update exploration rates within allowed bands, and may write a compact state record that the controller 15B may checkpoint with the model version so that a deployment may be rolled back with policy state preserved. The mode selector 20B may expose a dry-run mode in which it computes and logs what it would have chosen while deferring to a fixed baseline directive, which may support A / B comparisons orchestrated by the controller 15B without affecting live routing.

[0335] In some embodiments, a user interface (UI) module 22B may execute as a set of services and client libraries that may prepare, serialize, and present outputs produced by the Al system 12B to user devices 14. The UI module 22B may accept response records from the controller 15B that may include predicted class probabilities, uncertainty values, sensitivity scores, and provenance metadata such as model version, configuration identifiers, and sampling budgets actually consumed. The UI module 22B may construct view models by transforming raw arrays into typed objects, may compute derived values such as normalized scales and percentile ranks, and may attach presentation hints that may specify color ramps, threshold markers, and annotation labels. The UI module 22B may render these view models to one or more front ends, which may include a standards-compliant web application running in a browser, native applications, and an API client that may request pre-rendered images or structured data for embedding into third-party dashboards or use in other logic.

[0336] In some embodiments, the UI module 22B may present a classification view that may display predicted class probabilities as stacked bars or sorted lists, with each class row showing a probability, a confidence band derived from predictive dispersion, and a compact badge indicating routing mode selected by the mode selector 20B. The view may include an uncertainty panel that may present predictive entropy as a single scalar, a mutual-information-style value when available, and a trend sparkline across successive requests for recurring inputs. A token-level explanation view may apply a heatmap overlay to the original text, where per-token sensitivity scores may be mapped to an opacity or color scale, and hovering or selecting a span may display exact numeric values and the procedure used to compute the scores. A variance-attribution view may display grouped sensitivities for linguistic constructs or field groups as treemaps or bar clusters and may-106- 078474-0586924include controls to expand or collapse groups and to switch the attribution target among a selected class probability, a vector of class probabilities, or a scalar uncertainty summary.

[0337] In some embodiments, the UI module 22B may present topological variance visualizations and diagnostic graphs. A graph view may render feature groups as nodes positioned by a force-directed layout, may draw edges whose thickness may represent interaction strength, and may color nodes by variance attribution level; selecting a node may fdter the token heatmap to the tokens associated with that group and may reveal the set of inducing points from the Gaussian process layer 32B that contributed the most to the current prediction. A timeline view may show partial aggregates from the likelihood and marginalization module 34B as sampling progresses, with bands narrowing as additional draws may be incorporated. The view may allow pausing, resuming, and stepping to inspect intermediate probability vectors. A calibration view may plot predicted probabilities against observed outcomes for labeled validation sets when provided, and may display temperature or link-function parameters active for a tenant in the current snapshot.[00338J In some embodiments, the UI module 22B may support interactive workflows for review and what-if analysis. A reviewer may select a token span and request a counterfactual evaluation; the UI module 22B may submit a perturbation plan to the controller 15B, may display the resulting change in predicted probabilities and uncertainty, and may annotate the difference on the heatmap. A user may switch inference modes for a single request by instructing the controller 15B to re-run with a higher budget. The UI module 22B may display both the original and re-run outputs side-by-side with a diff of probabilities and a change log of sampling counts. For multitenant deployments, the UI module 22B may allow a user to switch tenant context, which may adjust class taxonomies, masking policies, and visualization defaults. The module 22B may apply tenant-scoped themes and may restrict access to artifacts and logs according to tenant identifiers. For automated clients, the UI module 22B may provide endpoints that may return the same view models in JSON or binary form along with signed URLs (uniform resource locators) for prerendered heatmaps and graphs.

[0339] In some embodiments, the UI module 22B may manage rendering pipelines and performance controls. The UI module 22B may downsample long sequences for initial display and-107- 078474-0586924may fetch high-resolution segments on demand as a user scrolls or focuses on a region. The UI module 22B may compress numeric arrays with quantization and run-length encoding before transmission, may apply client-side decompression, and may cache immutable artifacts by content hash to avoid redundant downloads. The UI module 22B may stream partial results for long- running evaluations by emitting incremental probability vectors and uncertainty updates framed with sequence numbers, and the front end may animate transitions while preserving axis scales. The UI module 22B may record interaction telemetry such as fdter selections, drill-downs, and mode overrides, and may write that telemetry to storage where the controller 15B may aggregate it for later policy adjustments by the mode selector 20B.

[0340] Some embodiments may implement a process 50B illustrated in figure 2B, for instance with the above described Al system 12B or with other implementations. Some embodiments include parsing a sequence of tokens from natural language text (or other unstructured inputs), as indicated by block 52B. Some embodiments include computing with a transformer encoder of a neural network a feature vector of the sequence of tokens, as indicated by block 54B. Some embodiments input the feature vector into a probabilistic head of the neural network, as indicated by block 56B. Embodiments may determine both a latent mean and a latent variance for each of a plurality of candidate output classes, as indicated by block 58B. Some embodiments may compute predictive class probabilities from the latent means and latent variances, as indicated by block 60B. Embodiments may select one of the candidate output classes based on the predictive class probabilities, as indicated by block 62B. Some embodiments determine an uncertainty of the selection based on the latent variances, as indicated by block 64B. Some embodiments store the selection and the uncertainty in memory, as indicated by block 66B, before presenting those values to a user device requesting the inference or prediction.

[0341] CREATING CONTEXT-SPECIFIC, VERSATILE EXPERT Al PERSONAS

[0342] Some artificial intelligence models implement a technique called style transfer. Style transfer in visual generative Al (e.g., in diffusion models) often involves applying the visual style of one image (like a painting) to the content of another image (like a photograph), blending them to create a unique output. This process, in some cases, uses neural networks, such as convolutional neural networks (CNNs), to separate "style" and "content" elements in images. During style-108- 078474-0586924transfer, in some cases, the model extracts style features such as color, texture, and brushstroke patterns from a reference style image, while preserving the layout and structure (content) of the target image. Style loss, in some cases, is quantified with a Gram matrix, which captures the correlation between different feature maps at various layers, providing a way to measure texture and color distribution, and that loss is minimized during training.

[0343] Similarly, text style transfer in natural language processing (NLP) often modifies the tone, formality, or sentiment of text while retaining its other semantic content. One approach involves attribute-controlled pretrained models, where large language models are fine-tuned on style-labeled datasets. Latent space manipulation is another approach, where sentences are encoded into vectors that can be shifted to emphasize stylistic attributes like sentiment. Conditional generative models, including Conditional Variational Autoencoders (CVAE) and Conditional GANs (cGAN), are also used for NLP, as they can be trained to produce text in a specified style by conditioning on style labels. In some approaches, reinforcement learning algorithms drive models to prioritize specific stylistic features by rewarding outputs that match the target style, maintaining meaning while adjusting tone.

[0344] Many existing style transfer algorithms cannot accommodate useful sources of training data in multiple modalities and are relatively brittle once trained or otherwise configured. Such existing approaches often do not work well with multimodal inputs, for example, spanning images, video, voice, and text. Moreover, many of the existing approaches to style transfer afford relatively limited control to the user to shape the resulting output. For example, indicating when particular variants of certain styles should be applied, either at configuration or training, or at run-time generation. Further, many existing approaches are not well suited to adapt to inputs at runtime and fail to appropriately tailor the style to the inputs at hand, making such approaches brittle and less suitable for higher-stakes, more-dynamic use cases.

[0345] Indeed, many current Al solutions often lack the ability to project personalized, stylistically unique outputs that reflect a user's (e.g., company’s) individuality in varied professional contexts. Existing systems often fail to account for the nuanced personalization needs in business settings where experts need to adjust their communication tone and style across a wide spectrum of client types. These limitations hinder experts’ ability to demonstrate both professional-109- 078474-0586924adaptability and personal branding through Al-driven content generation, thus impacting effective communication.

[0346] None of the preceding should be read to imply that any approach is disclaimed or disavowed, and this clarification should not be read to imply that any other material is disclaimed or disavowed herein where no such clarification is provided. Further, the discussion of various issues with other approaches herein should not be read to imply that embodiments are limited to systems that fully solve, or even mitigate, all of these issues or any of these issues, which is not to imply that any other description is limiting.

[0347] Some embodiments accommodate training inputs across heterogenous modalities with multi-modal embeddings created using cross-attentional networks or the like. Some embodiments cluster the resulting embedding vectors with unsupervised topological learning algorithms or hierarchical clustering to determine various clusters corresponding to different styles, or other forms of personas. Some embodiments then afford a user interface by which users may label and configure those personas to shape their application. In some embodiments, a dual-encoder model is then used for run-time personal selection and mixing, and some embodiments implement context-specific learning and evolution of personas, e.g., with reinforcement learning. A collection of styles and parameters that affect the systems propensity to apply those styles (e.g., in combination with different weights affecting the strength of each style’s contribution in a given scenario) is referred to as a persona.

[0348] Some embodiments may be used in enterprise environments where showcasing individuality is helpful. Embodiments may help companies to show their own differentiation in a world that is increasingly becoming homogenized - every company has access to the same publicfacing Al tools, and everyone is starting to sound the same. Some embodiments allow companies and individuals to take advantage of their uniqueness, and leverage those embodiments to differentiate from others who are just all going to sound similar, using similar tools. Some embodiments also allow people who generate more unique and differentiated content to generate more unique content, and have a multiplicative effect.

[0349] Example embodiments may mitigate some or all of these problems or other problems and have the following features.-110- 078474-0586924

[0350] In some embodiments, a biologically inspired adaptive framework may be executed on a computer system to allow users to create multiple personalized "Al Twins" that reflect distinct, context-specific personas. These Al Twins may be adaptable across a range of output media, including text, audio, images, gestures, video, and others, utilizing advanced learning and customization processes. In some embodiments, such a framework may incorporate features allowing dynamic style extraction, multi-modal embedding, and adaptive clustering to enable versatile persona generation and real-time adaptation. Outputs, in some embodiments, may be in the form of any of the types of inputs described. Generative models that create these outputs may be configured with the techniques described herein.

[0351] In some embodiments, a multi-layer style extraction process may be employed, utilizing transformer-based architectures, such as fine-tuned versions of models like Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), LLaMa, or Vision Language Models (VLM), to isolate stylistic characteristics from input sources. These sources may include user-provided text, video transcripts, or image metadata. Other examples include various channels of signals in robotics, like sensor data, control data, and the like. By leveraging natural language processing models trained on extensive, stylistically annotated datasets, the system may identify attributes such as tone, formality, linguistic complexity, and rhetorical features across multimodal data.

[0352] In some embodiments, the framework may analyze stylistic features across non-text media by generating multi-modal embeddings through cross-attentional networks. Such embeddings may facilitate the alignment of stylistic elements across data types, including images, audio, and visual data, enabling the system to interpret nuanced stylistic features consistently across various mediums. For instance, in handling images or audio, cross-attentional networks may align visual or auditory cues (like those in a Mel spectrogram and text transcribed with a speech- to-text model) with textual stylistic markers, creating a coherent representation of the user’s style across all formats.

[0353] Following style extraction, automated clustering and classification may be performed to group extracted styles into clusters through unsupervised topological learning algorithms or hierarchical clustering processes. These algorithms may assign stylistic tags to each cluster,-111- 078474-0586924forming the basis of each Al Twin’s persona. As users interact with the system, it may continuously (e g., periodically, like daily, or weekly, or in response to new input, and these example schedules may be applied in each reference to something happening continuously herein) refine these clusters, potentially affording iterative personalization that adapts over time.

[0354] This adaptive framework may facilitate a flexible application of stylistic elements across diverse media, allowing users to create dynamically responsive personas with stylistic coherence in various formats. By incorporating multi-layer, multi-modal analysis, this framework is expected to handle complex stylistic nuances, supporting consistent representation of brand voice or individual style across multiple channels and enhancing content creation across varied output formats.

[0355] In some embodiments, a tagging and prioritization mechanism may be provided (e.g., exposed via a user interface or application program interface (API)), allowing users to label and rank the stylistic elements of their Al Twins to enhance personalization. A user-driven style tagging interface may be used, helping users to manually assign intuitive tags, such as “Formal Client Report,” “Casual Team Update,” “Detailed,” “Curt,” “Aggressive,” or “Non- confrontational,” to various styles. Users may also rank these tags according to importance in a persona, establishing a feedback loop whereby the backend system may reweight style features in response to these preferences.

[0356] In some embodiments, a prioritization scoring system may be applied to assign weights to tags based on their frequency of use and recent relevance, which may be determined through adaptive Bayesian updating or reinforcement learning techniques. These techniques may facilitate context-sensitive prioritization of tags, allowing each Al Twin to adapt dynamically to different contexts and thereby increase the flexibility and depth of personalization available for each persona. The system may adjust the importance of tags as contextual factors shift, enhancing the ability of each Al Twin to reflect updated user preferences or situational requirements.

[0357] A dynamic re-weighting algorithm may continuously incorporate real-time updates from user interactions, adjusting the priority of tags in response to the selection patterns for various outputs. This dynamic approach to prioritization may favor frequently used or contextually-112- 078474-0586924significant personas, helping high-priority personas to take precedence when generating output, while deprioritizing less relevant styles.

[0358] By embedding tagging and prioritization within the learning loop, this mechanism, in some embodiments, allows for active user control over the style hierarchy, helping afford on-the- fly customization of output style for specific contexts. This flexible control may allow users, including companies, to tailor stylistic output across different regions or cultural contexts, where varying stylistic approaches may be appropriate to different business needs.

[0359] In some embodiments, a real-time persona selection and mixing mechanism may be implemented to facilitate dynamic and context-specific blending of personas for diverse output formats. In certain embodiments, contextual persona blending may be achieved through a dualencoder model, where one encoder processes user data while the other encodes context-specific parameters. These encodings may be represented as tensors and combined using an attention mechanism to yield a blended persona. The system may apply a combination of zero-shot and fewshot learning techniques to approximate the appropriate persona based on user inputs in real time, drawing on models that have been trained or fine-tuned with the relevant persona attributes.

[0360] In some embodiments, the framework may include a modular persona selection approach, allowing users (e g., via a user interface (UI) or application program interface (API)) to choose multiple Al Twins (or other types of personas) for particular outputs. Modular embeddings may then be employed to integrate characteristics from each selected persona. The user interface may provide intuitive controls, such as adjustable sliders, to allow users to mix and balance styles according to their preferred proportions. This interface may help users to fine-tune the blending of multiple personas, creating a customized output style that reflects the user’s desired combination of traits.

[0361] In some embodiments, run-time adaptive learning may be used to generate a new, approximate persona when a novel or unclassified style is required. For example, if a user specifies a blend of “Formal” and “Friendly” for a particular scenario, a meta-leaming layer may analyze context cues to generate a persona that interpolates between similar existing personas. This realtime persona adaptation may allow the model to create a new Al Twin that reflects the specified-113- 078474-0586924blend, dynamically adjusting embeddings to produce output that aligns with the unique stylistic requirements of the situation.

[0362] This real-time (e.g., within 5 seconds, 1 second, or 100 ms of receiving an input to which it is applied) persona blending capability may facilitate flexible, adaptive content generation across multiple forms of output. By supporting on-the-fly persona mixing, the system may allow for responsive, contextually appropriate adjustments to Al-generated content, helping it to maintain a natural, adaptive quality that responds fluidly to evolving user requirements.

[0363] In some embodiments, a context-specific learning and evolution mechanism may be employed to support the continuous adaptation and refinement of Al Twin personas based on situational feedback. In certain embodiments, the system may incorporate a memory-augmented neural network that stores “persona memories,” inspired by biological memory processes. This memory module may record interactions with the user and store specific stylistic adjustments applied during these interactions. When similar situations arise, the system may retrieve relevant memories to autonomously apply previously learned adaptations, providing contextually appropriate outputs based on prior experiences.

[0364] In some embodiments, long-term persona development may be implemented through reinforcement learning embedded in the persona framework. This approach may allow each Al Twin to optimize and evolve its stylistic attributes over time. The system may gradually refine these personas by drawing insights from a variety of context-specific interactions, such as customer communications, formal presentations, or internal team updates, thereby building a sophisticated representation of user preferences that continuously adapts as interactions progress.

[0365] To handle unclassified or out-of-training-set-distribution situations, some embodiments may deploy a meta-learning layer capable of generating a new Al Twin that is responsive to unique contextual factors. By identifying contextual gaps and inferring stylistic cues from these, the system may synthesize a new persona that aligns with unfamiliar circumstances. This self-adapting mechanism, in some embodiments, functions similarly to biological evolution, helping the system to respond appropriately in unfamiliar contexts by adjusting its stylistic responses based on inferred cues.-114- 078474-0586924

[0366] This adaptive framework, in some embodiments, may help an Al Twin (or other personas) to develop enhanced situational awareness over time. The evolving nature of these personas is expected to yield outputs that are progressively refined and contextually aligned with the user’s evolving needs, delivering an increasingly personalized experience that adapts dynamically to changing contexts.

[0367] In some embodiments, resilience and experience accumulation over time may be implemented to support Al Twins (or other personas) in adapting and improving through repeated interactions. An experience-driven adaptation mechanism, in some embodiments, may be used, whereby each Al Twin includes an experience tracker that records contexts of interactions, styles used, and user feedback. This data, in some embodiments, may be input into a reinforcement learning loop, where context-specific memory embeddings guide future responses. For instance, if a particular tone proves effective with a specific client type, the system may prioritize this tone in similar future scenarios, reinforcing successful communication patterns and adapting less effective ones.

[0368] Some embodiments may further include feedback-informed behavior refinement, leveraging user feedback on output effectiveness to enhance response strategies. In this process, Q-learning (a form of reinforcement learning) may be applied, in some embodiments, to adjust response characteristics, such as tone or complexity. Positive feedback may strengthen the associated stylistic traits, while negative feedback may prompt adaptive learning, allowing the system to modify its approach and avoid repeating less effective styles in future interactions.

[0369] In some embodiments, dynamic situational recall may be incorporated. This memorybased recall mechanism, in some embodiments, may allow an Al Twin (or other personas) to retrieve relevant prior interactions when encountering similar contexts, helping afford rapid adaptation without needing to relearn familiar scenarios. This situational recall is expected to improve the Al Twin’s resilience, fostering a sense of familiarity with recurring communication challenges.

[0370] As the Al Twin engages in new or increasingly complex interactions, it may undergo progressive persona evolution, continuously refining its stylistic and behavioral nuances. This memory-augmented development allows each Al Twin to grow more precise and resilient over-115- 078474-0586924time, yielding outputs with greater contextual accuracy and sophistication as interactions accumulate.

[0371] This approach in some embodiments, which builds resilience through memory-based learning, may help each Al Twin (or other personasO to adapt, refine, and expand its stylistic range across interactions. By affording continuous experience-driven adaptation, in some embodiments, Al Twins may become highly responsive, capable of achieving a natural progression of expertise suitable for varied business situations. This continuous learning process is expected to support users in deploying highly refined, reliable Al Twins that respond consistently and intelligently ...

Claims

CLAIMSWhat is claimed is:

1. A tangible, non-transitory, machine-readable medium storing instructions that, when executed, effectuate operations comprising: obtaining, with a computer system, an objective for a multi-agent artificial intelligence (Al) platform to generate a software application; determining, with the computer system, a domain to which the objective applies; accessing, with the computer system, information in the domain; using, with the computer system, the information in the domain, with a reasoning Al model, decomposing the objective into sub-objectives to complete the objective; determining, with the computer system, with the Al platform, user interface (UI) components of the software application; orchestrating, with the computer system, a plurality of Al agents of the Al platform to determine how to configure at least some of the UI components and how to respond to input from at least some of the UI components; and storing, with the computer system, a first version of the software application in memory.

2. The medium of claim 1, the operations comprising: determining a spatial layout of at least some of the UI components with the Al platform.

3. The medium of claim 2, wherein determining the spatial layout comprises generating content negotiation code by which UI components are sized or positioned based on screen dimensions of a client computing device.

4. The medium of claim 1, wherein at least some of the sub-objectives comprise: uploading a document from which rules applied by the software application are extracted; applying the rules to an evaluation document to form evaluation results; and causing the extracted rules and evaluation results to be presented to a user providing the objective.-181- 078474-05869245. The medium of claim 1, wherein determining at least some of the UI components comprises generating code of respective event handlers responsive to interaction with the respective UI components.

6. The medium of claim 1, wherein the Al platform comprises more than one thousand Al agents responsive to an orchestrator.

7. The medium of claim 6, wherein at least some of the Al agents are specific to the domain and are selected by the orchestrator in response to the orchestrator determining those Al agents are specific to the domain.

8. The medium of claim 6, wherein at least some of the Al agents are specific to respective tasks.

9. The medium of claim 1, comprising: receiving feedback from a user who provided the objective on the first version of the software application; and in response to the feedback, changing at least some of the UI components to generate a second version of the software application.

10. The medium of claim 9, wherein the feedback is obtained during a use session with the first version of the software application and the second version of the software application is substituted for the user during the session with session state matching that of the first version of the software application at the time of the substitution.

11. The medium of claim 1, wherein: the information in the domain is accessed in a private enterprise network; and the software application is deployed in the private enterprise network.

12. The medium of claim 1, wherein:-182- 078474-0586924the Al platform comprises more than 100 Al agents and at least some of the more than 100 Al agents are task specific Al agents and at least some of the more than 100 Al agents are domain specific Al agents.

13. The medium of claim 1, wherein: determining the UI components of the software application is performed with a UI planner Al agent of the Al platform; and at least some of the UI components comprise: a clustered graph; a time series graph; a force-directed graph; or a chord diagram.

14. The medium of claim 1, wherein: determining the UI components of the software application comprises: interpreting at least some of the sub-objectives, decomposing tasks, and selecting UI components and layout with a first Al agent of the Al platform; and generating executable code by which at least some of the UI components are rendered and updating the executable code with a second Al agent of the Al platform. the software application is configured to: ingest a document expressing compliance requirements in natural language text and images; extract the requirements based on both the natural language text and the images; and cause a visualization of a processing pipeline of the document to be displayed, the visualization showing relationships between documents, pages, and extracted rules; and the software application is configured to: ingest another document and evaluate compliance of the other document with the extracted requirements; and present, with at least some of the UI components, a visual association between content of the other document and determinations of compliance or non-compliance.-183- 078474-058692415. A method, comprising: obtaining, with a computer system, an objective for a multi-agent artificial intelligence (Al) platform to generate a software application; determining, with the computer system, a domain to which the objective applies; accessing, with the computer system, information in the domain; using, with the computer system, the information in the domain, with a reasoning Al model, decomposing the objective into sub-objectives to complete the objective; determining, with the computer system, with the Al platform, user interface (UI) components of the software application; orchestrating, with the computer system, a plurality of Al agents of the Al platform to determine how to configure at least some of the UI components and how to respond to input from at least some of the UI components; and storing, with the computer system, a first version of the software application in memory.-184- 078474-0586924

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