Virtual neuron system for enhanced reasoning in neural network-based generative models

Abstract

A system and method for enhancing reasoning in neural network-based generative models through software-defined Virtual Neurons—active processing units with configurable activation functions and weighted connections—instantiated during inference without modifying pretrained parameters. Virtual Neurons maintain state vectors in dedicated in- memory data structures managed by a cognitive overlay layer, distinct from external storage and context-token buffers used by prior art. Organization follows hierarchical tiers scaled by α* = (γCs / βCf)^(1 / (β-γ)); for γ ≈ 0.8, β ≈ 1.5, α* ≈ √2, providing optimal path length and cache locality. Cognitively-isolated sandboxes with data-structure-level barriers enable Virtual Double hypothesis exploration by cloning internal cognitive state graphs (not token sequences), with O(1) rollback. A multilayer introspective feedback system evaluates internal cognitive structure (not output scores) and triggers structural modifications. Combined interaction produces runtime adaptation, speculative branching, and self-correction not achievable by components alone.

Description

[0001] PCT Application — Virtual Neuron System v5.3

[0002] INTERNATIONAL PATENT APPLICATION under the Patent Cooperation Treaty (PCT)

[0003] VIRTUAL NEURON SYSTEM FOR ENHANCED REASONING IN NEURAL NETWORK-BASED GENERATIVE MODELS

[0004] Inventor and Applicant:

[0005] Thierry Marechai Montreal, Quebec, Canada

[0006] PCT Application — Virtual Neuron System v5.3

[0007] PRIORITY CLAIMS

[0008] This application claims priority to:

[0009] • U.S. Provisional Application No. 63 / 727,035

[0010] Filed: December 2, 2024

[0011] Title: Artificial Neural Framework for Virtual Neurons and Cognitive Module Integration

[0012] • U.S. Provisional Application No. 63 / 726,496

[0013] Filed: November 30, 2024

[0014] Title: Dynamic Multilayered Feedback Loops for Mathematical Introspection in

[0015] Artificial Intelligence Systems

[0016] • U.S. Provisional Application No. 63 / 727,023

[0017] Filed: December 2, 2024

[0018] Title: Dynamic Virtual Neural Networks for Autonomous Al Adaptation

[0019] • U.S. Provisional Application No. 63 / 726,974

[0020] Filed: December 2, 2024

[0021] Title: Virtual Doubles for Enhanced Al Problem Solving and Creative Exploration

[0022] • U.S. Provisional Application No. 63 / 727,014

[0023] Filed: December 2, 2024

[0024] Title: Virtual Neural Network Framework for Advanced Al Problem Solving

[0025] • U.S. Provisional Application No. 63 / 727,055

[0026] Filed: December 2, 2024

[0027] Title: Virtual Neural Networks for Multi Layer Introspective Problem Solving

[0028] PCT Application — Virtual Neuron System v5.3

[0029] ABSTRACT

[0030] A computer-implemented system and method for enhancing reasoning capabilities in neural network-based generative models, including transformer-based language models, state-space models, and hybrid architectures. The system instantiates software-defined Virtual Neurons within the computational context of a host model during inference, without modifying pretrained parameters. Each Virtual Neuron is an active processing unit with configurable activation functions and weighted connections, maintaining a state vector in a dedicated inmemory data structure managed by the cognitive overlay layer — distinct from external storage systems and context-token buffers used by prior art memory-augmented systems. Virtual Neurons organize into hierarchical tiers scaled by an optimal geometric ratio a derived from variational energy minimization: a* = (yCs / pCf)A(l / (P-y)), where typical values y ~ 0.8 and P ~ 1.5 yield a* ~ 2 ~ 1.414, providing reduced average path length and improved cache locality. The system includes cognitively-isolated sandbox contexts enabling parallel hypothesis exploration with reduced overhead. A multilayer introspective feedback system evaluates internal cognitive structure — not merely output scores — and triggers structural corrective actions. The combined interaction produces emergent capabilities including runtime structural adaptation, safe speculative branching with rollback, and structural self-correction.

[0031] PCT Application — Virtual Neuron System v5.3

[0032] FIELD OF THE INVENTION

[0033] The present invention relates to systems and methods for enhancing reasoning capabilities in neural network-based generative models through runtime-instantiated cognitive structures. The invention is applicable to transformer-based language models, state-space models (Mamba, RWKV), recurrent architectures, and hybrid neural-symbolic systems. The invention provides technical improvements in contextual memory efficiency, coordination latency, and processing organization.

[0034] BACKGROUND OF THE INVENTION

[0035] Technical Problem

[0036] Neural network-based generative models suffer from technical limitations: (1) context window saturation requiring O(n2) attention computation, (2) absence of structured intermediate state persistence, (3) single-path execution without efficient parallel exploration, and (4) lack of introspection mechanisms operating on computational structure rather than output text.

[0037] Critical Distinctions from Prior Art

[0038] Distinction from Chain-of-Thought Prompting

[0039] Chain-of-Thought generates reasoning as serialized text tokens consuming context capacity. The present invention maintains state in dedicated memory structures or reserved token slots, reducing token count in the primary context and attention computation overhead.

[0040] Distinction from Memory-Augmented Systems (MemGPT)

[0041] Memory-augmented systems use text-based memory commands and passive memory vectors requiring parsing overhead. The present invention implements memory as native data structures with direct programmatic access, avoiding serialization. Virtual Neurons are active processing units with configurable activation functions and weighted connections — not passive storage like RAG vectors or MemGPT memory slots.

[0042] Distinction from Multi- Agent Frameworks (AutoGen, LangGraph)

[0043] Multi-agent frameworks coordinate via inter-process communication with serialization overhead. Virtual Neurons operate within a single process sharing memory space, achieving lower coordination latency. Multi -agent systems coordinate few discrete agents; Virtual Neuron systems instantiate many units with finer granularity.

[0044] Distinction from Tree-of-Thoughts

[0045] Tree-of-Thoughts generates alternative text continuations. Virtual Doubles are computational state duplicates with explicit memory isolation. State is preserved in typed data structures, not text. Memory barriers prevent cross-contamination.

[0046] Distinction from Speculative Decoding

[0047] Speculative decoding generates token predictions for verification. Virtual Doubles explore reasoning hypotheses with different parameter configurations. Critical distinction: Virtual Doubles clone the internal cognitive state graph (activation vectors, adjacency structures, introspection markers), not the output token sequence. The speculative branch operates on cognitive structure, not text trajectories. PCT Application — Virtual Neuron System v5.3

[0048] Distinction from Mixture-of-Experts

[0049] MoE routes to pre-trained permanent experts fixed at training time. Virtual Neurons are instantiated at inference time with runtime-configured parameters. Virtual Neuron count adapts to task; MoE experts cannot observe each other; Virtual Neurons share unified state space.

[0050] Distinction from Dynamic Computation Graphs

[0051] Dynamic graphs create nodes for gradient computation. Virtual Neurons are semantic processing units with cognitive state (activation, confidence, role, history). Purpose: automatic differentiation versus cognitive organization.

[0052] Distinction from Prefix-Tuning and Soft Prompts

[0053] Prefix-Tuning uses reserved tokens as learnable embeddings treated as free-form text. The present invention's reserved context-token slots encode state vectors as structured numerical data, and the cognitive overlay layer performs arithmetic operations (vector addition, element-wise multiplication) on said slots to update Virtual Neuron states.

[0054] PCT Application — Virtual Neuron System v5.3

[0055] TECHNICAL IMPROVEMENTS

[0056] 1. Reduced Context Window Utilization: State vectors in dedicated memory or reserved token slots reduce context consumption compared to text-based reasoning, reducing attention computation overhead.

[0057] 2. Lower Coordination Latency: Shared memory access avoids serialization, transport, and deserialization overhead inherent in IPC-based coordination.

[0058] 3. Optimal Hierarchical Organization: Tier scaling by a* ~ 2 (derived from variational minimization) reduces average cognitive path length, improves memory locality for overlay operations, and provides predictable multi-scale resolution.

[0059] 4. Efficient State Exploration: Data-structure-level memory barriers enable parallel exploration with lower overhead than process-based isolation, with rollback via reference management achieving 0(1) time complexity.

[0060] SUMMARY OF THE INVENTION

[0061] 1. Virtual Neurons (VNs): Software-defined active processing units materialized as explicitly instantiated objects. Each comprises: (a) state vector {activation, threshold, history, confidence, timestamp}; (b) configurable activation function (ReLU, sigmoid, tanh); (c) weighted connections in adjacency format; (d) semantic role tag {INPUT, DETECTOR, INTEGRATOR, DECISION, OUTPUT, INHIBITOR, COORDINATOR, META}. Unlike passive memory vectors, Virtual Neurons process inputs through activation functions and propagate signals through weighted connections.

[0062] 2. Virtual Neural Networks (VNNs): Organized assemblies with hierarchical tiers scaled by a* = (yCs / pCf)A(l / (P-y)). For typical values y ~ 0.8 (spectral cost exponent) and P ~ 1.5 (structural maintenance exponent), a* ~ 2 ~ 1.414. This ratio reduces average path length and improves cache locality.

[0063] 3. Dynamic Virtual Neural Networks (DVNNs): VNNs with autonomous restructuring. Controller monitors metrics {utilization, latency, accuracy, coherence}, detects triggers, executes modifications {expand, contract, rewire, reassign}.

[0064] 4. Virtual Doubles (VDs): Computational duplicates (full or partial clones) in cognitively- isolated sandbox contexts with copy-on-write semantics, write barrier flags, and rollback via reference swap achieving 0(1) time complexity.

[0065] 5. Multilayer Introspective Feedback Loop (MIFL): Evaluation system comprising at least local consistency evaluation and global alignment evaluation. Unlike critic networks evaluating output scores, MIFL evaluates internal cognitive structure (activation topology, branch lineage, sandbox provenance) and triggers structural modifications (restructuring tiers, correcting roles, merging / discarding Virtual Doubles).

[0066] Synergistic Interactions (Non-Obviousness)

[0067] The combined effect of Virtual Neurons, hierarchical Virtual Neural Networks, dynamic restructuring, Virtual Doubles, and Multilayer Introspective Feedback is not mere aggregation: these components interact to provide runtime structural adaptation, safe speculative branching with rollback, and structural self-correction. No prior architecture provides this joint capability within a single cognitive overlay layer operating on immutable pretrained models. Specifically: (a) VDs enable hypothesis exploration that feeds MIFL evaluation; (b) MIFL triggers DVNN restructuring based on VD outcomes; (c) hierarchical PCT Application — Virtual Neuron System v5.3 organization enables efficient routing for both VD branching and MIFL evaluation; (d) sandbox isolation ensures VD exploration cannot corrupt main reasoning state.

[0068] PCT Application — Virtual Neuron System v5.3

[0069] DETAILED DESCRIPTION

[0070] 1. Mathematical Foundation for Optimal Scaling

[0071] The scaling ratio a* is derived from variational optimization of energy functional E(a) = Cs aAy + Cf aAP where Cs represents spectral processing cost, Cf represents structural maintenance cost, y < P ensures complexity grows faster than benefit for large a. Taking dE / da = 0 yields a* = (yCs / pCf)A(l / (P-y)). For typical cognitive processing systems with y ~ 0.8 (spectral cost exponent) and P ~ 1.5 (structural maintenance exponent), and balanced cost coefficients, a* ~ 2. This value provides: reduced average path length, improved memory locality for overlay operations, stabilized activation patterns, and predictable multi-scale cognitive resolution. Note: The optimization is not claimed as an abstract mathematical method but as a configuration step for organizing memory access patterns and cognitive routing. The ratio a* is one of several possible implementations within the claimed range [1.3, 2.0],

[0072] 2. Cognitive Sandbox Architecture

[0073] OS Sandbox: Isolates system resources via kernel -enforced boundaries. Involves process creation, context switches, IPC.

[0074] Cognitive Sandbox: Isolates cognitive state via data structure boundaries within single process. Implementation: copy-on-write cloning (e.g., Python's copy. deepcopy with shared references for unchanged data), write barrier flags enforced via custom memory manager intercepting write operations to sandboxed regions, private registry copies, rollback via reference pointer swap achieving 0(1) time complexity. Unlike operating system sandboxes (e.g., Docker containers) that isolate system resources via kernel enforcement, the cognitive sandbox operates at the data structure level within a single process, avoiding context switch overhead.

[0075] 3. MIFL vs Critic Networks

[0076] Unlike critic networks or reward-model evaluators operating on output-level scalar scores, MIFL evaluates internal cognitive structure: activation graph topology, reasoning branch lineage, sandbox provenance, structural coherence between modules, multi-step causal consistency. MIFL performs structural modifications (restructuring tiers, correcting roles, merging / discarding Virtual Doubles) rather than merely assigning reward scores. Layer 1 evaluates local consistency by: (a) detecting oscillation if activation values alternate between two states for >3 consecutive cycles; (b) detecting stuck states if activation remains unchanged for >5 cycles; (c) detecting range violations if activation exceeds [0, 1] bounds. Corrective actions include adjusting activation thresholds by A = 0.1 (1 - confidence score) or pruning connections with weights below r = 0.05 mean_connection_weight.

[0077] 4. Alternative Embodiment: Latent Virtual Neurons

[0078] In alternative embodiments, Virtual Neurons may be realized as clusters, sub-manifolds, or activation neighborhoods within representational space, identified via attention-based algorithms: spectral clustering applied to attention maps of the host model, thresholding of attention weights to detect coherent activation neighborhoods with coherence score >0.7, or vector quantization of embedding spaces to identify sub-manifolds. Each latent VN has derived state vector comprising centroid embedding (mean of constituent token embeddings) and coherence score (cosine similarity between centroid and task objective embedding). Integration: Once identified, latent Virtual Neurons are incorporated into the same PCT Application — Virtual Neuron System v5.3 processing pipeline as explicit VNs: their derived state vectors are used for activation evaluation, connection updates, introspective scoring, and DVNN restructuring using identical logic.

[0079] 5. Alternative Embodiment: Reserved Context-Token Slots

[0080] In alternative embodiments, Virtual Neuron state may be stored in reserved context regions or special-purpose token slots treated as logical state carriers, while still managed by the cognitive overlay layer. These reserved slots encode state vectors as structured numerical data (floating-point arrays), and the cognitive overlay layer performs arithmetic operations (vector addition, element-wise multiplication) on said slots to update Virtual Neuron states — unlike prior art methods that treat reserved tokens as free-form text.

[0081] 6. Virtual Doubles: Cognitive State Cloning

[0082] Virtual Doubles differ from speculative decoding because they clone the internal cognitive state graph (activation vectors, adjacency structures, introspection markers), not the output token sequence. The speculative branch operates on cognitive structure, not text. The sandbox context is specifically configured for cognitive hypothesis exploration in neural network-based reasoning, enabling rollback of reasoning states without affecting the host model's parameters. The consensus mechanism for merging Virtual Doubles comprises a weighted average of state vectors: merged_state = X(confidence_i x state_i) / S(confidence i).

[0083] 7. Glossary of Terms

[0084] Cognitive Overlay Layer: Software middleware that intercepts tokens from the host model, routes computations via Virtual Neurons, and manages their state without modifying pretrained parameters.

[0085] Coherence Score: Cosine similarity between the centroid embedding of a Virtual Neuron (latent or explicit) and the task objective embedding, where task objective embedding is derived from the input query.

[0086] Consensus Mechanism: Algorithm for fusion of Virtual Double states using weighted average by confidence scores: merged_state = X(confidence_i x state_i) / X(confidence i).

[0087] Structural Corrective Action: Modification to VNN topology including: threshold adjustment (A = 0.1 (1 - confidence)), connection pruning (weights below T = 0.05 mean_weight), or semantic role reassignment.

Claims

PCT Application — Virtual Neuron System v5.3CLAIMSWhat is claimed is:

1. A computer-implemented method for enhancing reasoning in a neural network-based generative model having fixed pretrained parameters, the method comprising: (a) receiving an input query at a computing system hosting said neural network-based generative model; (b) during inference and without modifying any pretrained parameter of said model, instantiating one or more software-defined Virtual Neurons, wherein each Virtual Neuron is an active processing unit comprising: (i) a state vector stored in a dedicated in-memory data structure managed by the cognitive overlay layer, wherein said data structure is distinct from external storage systems and context-token buffers used by prior art memory-augmented systems; (ii) a configurable activation function selected from ReLU, sigmoid, or tanh; (iii) weighted connections to other Virtual Neurons maintained in a connection data structure; and (iv) a semantic role designation; (c) routing at least a portion of inference computation through said Virtual Neurons via a cognitive overlay layer that manages Virtual Neuron state while preserving parameter immutability of the pretrained model; and (d) generating an output based at least in part on activation states of said Virtual Neurons; wherein storing state in said dedicated in-memory data structure reduces context window utilization compared to text-based reasoning approaches; and wherein each Virtual Neuron actively processes inputs through its activation function and propagates signals through its weighted connections, unlike passive memory vectors in prior art systems.

2. A computer-implemented method for organizing cognitive processing structures in hierarchical tiers, the method comprising: (a) instantiating a plurality of processing units within a computational context; (b) organizing said processing units into hierarchical tiers wherein a base tier contains N processing units and each subsequent tier k contains approximately N / aAk processing units; (c) selecting the geometric scaling ratio a by minimizing an energy functional E(a) = Cs of y + CfaAP where Cs and Cf are positive constants representing spectral and structural costs, y and P are positive exponents with P > y, yielding optimal ratio a* = (yCs / p Cf)A(l / (P-y)); and (d) processing information through said hierarchical tiers; wherein for typical values y ~ 0.8 and P ~ 1.5 with balanced cost coefficients, a* ~ 2 ~ 1.414; and wherein said hierarchical organization reduces average cognitive path length and improves memory locality for overlay operations; wherein said optimization is a configuration step for organizing memory access patterns and cognitive routing, not an abstract mathematical method.

3. The method of claim 1, further comprising: (a) capturing a state snapshot including state vectors and connection data structures; (b) allocating a cognitively-isolated sandbox context specifically configured for cognitive hypothesis exploration, comprising: (i) a private copy via copy-on-write semantics; (ii) write barrier flags preventing cross-boundary writes; (iii) said sandbox operating within a single process at data structure level, avoiding context switch overhead; (c) instantiating a Virtual Double (VD) within said sandbox, said Virtual Double being a full or partial clone of source processing structures that clones the internal cognitive state graph (activation vectors, adjacency structures, introspection markers) rather than output token sequences; (d) executing said Virtual Double with modified cognitive parameters to explore an alternative reasoning hypothesis; and (e) merging results via consensusPCT Application — Virtual Neuron System v5.3 mechanism comprising weighted average of state vectors proportional to confidence scores, with rollback capability via reference pointer swap achieving 0(1) time complexity.

4. The method of claim 1, further comprising evaluating Virtual Neurons via a multilayer introspective feedback system comprising at least: (a) local consistency evaluation detecting: oscillation if activation values alternate between two states for >3 consecutive cycles, stuck states if activation remains unchanged for >5 cycles, and range violations if activation exceeds [0, 1] bounds; (b) global alignment evaluation measuring correspondence between output and task objectives via cosine similarity; wherein said feedback system evaluates internal cognitive structure including activation graph topology, branch lineage, and structural coherence, rather than merely evaluating output-level scalar scores; and wherein threshold violations trigger structural corrective actions including at least one of: adjusting activation threshold by A = 0.1 (1 - confidence score), pruning connections with weights below T =0.05 mean_connection_weight, and structural role reassignment.

5. A system for enhancing reasoning in neural network-based generative models, the system comprising: (a) a host neural network-based generative model with pretrained parameters maintained as read-only, said model comprising at least one of: transformer-based architecture, state-space model architecture, recurrent architecture, or hybrid architecture; (b) a cognitive overlay layer configured to instantiate and manage Virtual Neurons as active processing units while preserving parameter immutability; (c) a connection registry maintaining weighted connections; (d) a sandbox allocator creating cognitively-isolated contexts with data-structure-level memory barriers for cognitive hypothesis exploration; (e) an introspective feedback engine evaluating internal cognitive structure; and (f) a tier organization module arranging Virtual Neurons in hierarchical tiers scaled by geometric ratio a* derived from energy functional minimization.

6. The method of claim 1, wherein the dedicated in-memory data structure storing Virtual Neuron state vectors comprises memory separate from the model's context window and distinct from external databases, files, or persistent storage.

7. The method of claim 1, wherein Virtual Neuron state is alternatively stored in reserved context-token slots encoding state vectors as structured numerical data (floating-point arrays), wherein the cognitive overlay layer performs arithmetic operations (vector addition, element-wise multiplication) on said slots to update Virtual Neuron states, unlike prior art methods treating reserved tokens as free-form text.

8. The method of claim 1, wherein in alternative embodiments, Virtual Neurons are realized as clusters or activation neighborhoods within a representational space, identified via attention-based algorithms including: spectral clustering applied to attention maps of the host model, or thresholding of attention weights to detect coherent activation neighborhoods with coherence score >0.7; wherein each latent Virtual Neuron has a derived state vector comprising centroid embedding computed as mean of constituent token embeddings and coherence score defined as cosine similarity between said centroid and task objective embedding; and wherein once identified, latent Virtual Neurons are incorporated into the same processing pipelinePCT Application — Virtual Neuron System v5.3 as explicit VNs using identical logic for activation evaluation, connection updates, and introspective scoring.

9. The method of claim 1, wherein Virtual Neurons are organized according to the hierarchical tier structure of claim 2 with geometric scaling ratio a ~ 2.

10. The method of claim 1, further comprising: monitoring performance metrics including utilization, latency, accuracy, and coherence; evaluating against threshold functions; and upon violation, autonomously restructuring by at least one of: adding Virtual Neurons, removing Virtual Neurons, modifying connection weights, and reassigning semantic roles; wherein restructuring occurs during inference.

11. The method of claim 3, further comprising instantiating multiple Virtual Doubles concurrently in separate sandbox contexts, each exploring different cognitive hypotheses by cloning internal state graphs, and merging results via weighted consensus based on confidence scores.

12. The method of claim 1, wherein the connection data structure supports connection types comprising: excitatory connections, inhibitory connections, modulatory connections, and gating connections.

13. The method of claim 1, wherein the neural network-based generative model comprises at least one of: a state-space model, a recurrent neural network, a hybrid transformer-recurrent architecture, or a neural-symbolic hybrid system.

14. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 1.

15. The method of claim 1, applied in a non -therapeutic, non-diagnostic manner to at least one of: mathematical reasoning, autonomous planning, scientific hypothesis generation, document analysis, risk assessment, and educational tutoring.