Cognitive Inference Logging for Context-Aware Static LLMs

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Solution Overview

Problem

Large Language Models (LLMs) face limitations in reliability, accountability, and reasoning transparency due to their static nature, failing to provide user-specific responses and lacking long-term contextual understanding.

Innovation Solution

A system integrating a processor, memory, and a Socratic engine to generate cognitive inference units with confidence-falsifiability deltas, which are appended to a cognitive inference log, enabling dynamic and personalized responses through a blockchain-based audit trail.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a static LLM is used to process user requests, then the system structure is simple, but the model cannot adapt dynamically to individual user interactions or retain long-term contextual understanding

Engineering Contradiction:

Improvedynamic adaptation to user interactionsVSAvoidsystem structure

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

An integration layer is introduced as an intermediary component between the static LLM and the user interface. This integration layer includes a cognitive inference engine that generates CI units from user requests and queries the CI log for contextual information, enabling dynamic adaptation without modifying the underlying static LLM architecture.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system is segmented into distinct functional components: the static LLM, the integration layer, the cognitive inference engine, and the CI log storage. This segmentation allows the static LLM to remain simple while the integration layer handles the complexity of dynamic adaptation and contextual retention separately.

Inventive Principle:

Principle #1Segmentation

2Reliability

If cognitive inference units and confidence-falsifiability deltas are generated and stored in a CI log, then reasoning transparency and accountability are improved, but the system complexity and processing time increase

Engineering Contradiction:

Improvereasoning transparencyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The cognitive inference engine generates CI units and assigns confidence-falsifiability (CF) deltas in advance during user interactions. These inferences are stored in the CI log before they are needed for subsequent queries, allowing the system to retrieve pre-processed contextual information rather than generating inferences in real-time during each interaction.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates structured copies of inference data (CI units with CF deltas) and stores them in the CI log. These copied representations can be efficiently queried and reused across multiple interactions, reducing the need to regenerate the same inferences repeatedly while maintaining full reasoning transparency.

Inventive Principle:

Principle #26Copying

3Adaptability or versatility

If the integration layer queries the CI log for each subsequent user interaction, then user-specific context is maintained, but the processing time and computational overhead increase

Engineering Contradiction:

Improveuser-specific context retentionVSAvoidprocessing time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The CI log stores pre-generated cognitive inference units and CF deltas from previous user interactions. During subsequent interactions, the integration layer queries this pre-processed data rather than generating new inferences from scratch, significantly reducing processing time while maintaining user-specific context.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system retrieves only the specific CI units relevant to the current user interaction from the CI log, rather than processing or retrieving all stored inferences. This selective querying approach minimizes computational overhead while ensuring the appropriate user-specific context is applied.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS12555008B1Systems and methods for cognitive inferencing for large language models

Publication Date: 2026.02.17 DNAI SYSTEMS CO

AI summary

A system for cognitive inferencing for large language models (LLMs), the system including: a integration layer communicatively connected to a static LLM, wherein the integration layer includes a processor configured to receive one or more user requests from a user, wherein the one or more user requests include a user interaction with the static LLM, generate one or more cognitive inference (CI) units from the one or more user requests, assign a confidence-falsifiability (CF) delta for each of the one or more CI units using a Socratic engine and append the one or more CI units and the CF delta to a cognitive inference (CI) log associated with the user, wherein the CI log serves as an inference engine for the static LLM, wherein the integration layer is configured to query the CI log upon a subsequent user interaction with the static LLM.