A system for continuously validating the premises behind AI decisions. Not how confident the model is, but whether the inputs it relied on are still true.
System and Method for Cognitive Validity Assurance and Reasoning Integrity in Autonomous AI Agents
A system for maintaining the cognitive validity of autonomous agents is disclosed. The architecture utilises a Causal Substrate to model logical dependencies between a hypothesis and its supporting premises, distinct from statistical probability. An Integrity Audit engine continuously calculates a vector-based Semantic Distance between an initial Epistemic Snapshot and the current Operational Context. If this distance exceeds a threshold defined by an Epistemic Gravity score, a software interrupt or real-time control event is generated to inhibit agent execution, preventing action on stale or invalid logic. The system further comprises a Deception Spectrum Analyser for entropy-based signal filtering, a Stratified Trust Policy for source governance, and an Autonomous Logic Repair module configured to hot-swap invalid premises via a hierarchy of fallback positions. An Open-Ended State Machine provides fail-safe adaptation for unknown events through sandboxed process generation, ensuring resilient operation in dynamic environments.
Filed: December 2025 (Singapore) | Status: Patent pending
AI confidence scores tell you how sure a model is about its output. They don't tell you whether the inputs that led to that output are still valid. A model can be 99% confident in a conclusion built on data that went stale seconds ago.
Consider a trading algorithm that recommends a position based on market conditions observed at 9:01am. By 9:02am, a central bank announcement has changed the underlying dynamics. The model's statistical confidence in its recommendation hasn't changed, but the premises supporting it have collapsed. The model doesn't know this because it measures its own certainty, not the health of its inputs.
In financial services, healthcare, and critical infrastructure, this gap between statistical confidence and premise validity can have severe consequences. Decisions proceed with high confidence on foundations that no longer hold.
The Cognitive Causality Architecture models the logical dependencies behind every decision and continuously checks whether those dependencies are still valid.
Every decision is modelled as a graph of logical dependencies: which facts support which conclusions. Hard facts are linked to verifiable data sources. Assumptions are linked to probabilistic models or qualitative assessments. Predictions are linked to supporting evidence with time-locked validation. When one conclusion becomes a premise for another, the dependency chain is tracked recursively.
Each fact and assumption is continuously checked against its source. Has the market price changed? Has the regulation been updated? Is the sensor reading still current? The system uses a tiered architecture: critical premises (those whose failure would invalidate the entire conclusion) are checked in milliseconds against primary data sources. Less critical premises are checked in seconds or minutes against secondary sources. Every premise is scored across eight data quality dimensions: accuracy, completeness, timeliness, consistency, validity, uniqueness, reliability, and relevance.
The system produces a Validity Warrant: a scored, cryptographically signed attestation of how healthy every premise is right now. The warrant weighs each premise by how structurally important it is to the conclusion. A premise whose failure would collapse the entire reasoning chain is weighted far more heavily than a contextual detail.
The warrant does not block decisions. It documents the exact state of every premise, including any flaws, so there is a complete evidence record. When a decision is made, the warrant accompanies it as an auditable attestation of what was true at that moment.
When premises degrade beyond configurable thresholds, the system can signal warnings or escalate to human review. In safety-critical deployments, a deterministic kill switch can prevent execution when critical premises fail. In advisory deployments, the same degradation is recorded in the warrant as a drift metric, enabling downstream systems to factor it into their own decision logic.
When a premise fails, the system does not simply halt. It follows a structured fallback sequence: first attempting to swap in a validated alternative source, then searching for semantically equivalent data, then falling back to a conservative operating mode. Only when all repair attempts fail does the system lock out. Even crisis decisions made under degraded conditions are fully documented in the warrant.
Existing systems measure output confidence, not input validity. The Cognitive Causality Architecture separates these two concepts and provides continuous, real-time premise validation with cryptographic evidence of what was true when each decision was made.
Statistical confidence and premise validity are tracked independently. A model can be highly confident on invalid premises, and the system makes that visible.
Premises are checked continuously against their sources, not just at the moment a decision is made. Degradation is detected in real time.
Every Validity Warrant is cryptographically signed and tamper-evident. The evidence record of what was true when a decision was made cannot be altered after the fact.
A built-in signal analysis system classifies incoming data for signs of noise injection and manipulation, filtering adversarial inputs before they can affect premise integrity.
Trading decisions. A trading algorithm recommends a position based on market data, volatility models, and macroeconomic assumptions. The system continuously validates each premise against live market feeds. When a central bank announcement invalidates the macroeconomic assumptions, the Validity Warrant immediately reflects the degradation, documenting exactly which premises changed and when.
Clinical decisions. A diagnostic AI reaches a conclusion based on patient history, lab results, and imaging. The system tracks whether the underlying data is current. When new lab results arrive that contradict earlier values, the system flags the affected premises and documents the shift in the warrant, enabling clinicians to reassess with full awareness of what changed.
Supply chain optimisation. An AI agent recommends sourcing decisions based on supplier status, pricing, and logistics data. The system detects when a supplier's operating status changes or when shipping route conditions shift, surfacing the impact on downstream decisions before they execute on outdated assumptions.
Several existing technologies address aspects of AI confidence and data quality. None provides continuous, real-time validation of the premises underlying AI decisions.
| Technology / Approach | What It Does | Gap |
|---|---|---|
| Probabilistic AI / LLMs | Generate outputs based on statistical likelihood | Cannot maintain epistemic integrity over time in open-ended environments. |
| Retrieval-Augmented Generation (RAG) | Retrieve context via semantic similarity | Vector proximity does not equate to temporal validity. A relevant document may be factually obsolete. |
| Truth Maintenance Systems | Maintain consistency in symbolic logic databases | Operate in a closed, boolean world. Cannot handle probabilistic validity (0.0 to 1.0). |
| Finite State Machines | Enforce rigorous state transition logic | Fixed state set cannot accommodate dynamic premise validation. No probabilistic confidence weighting. |
| Model Explainability (SHAP, LIME) | Post-hoc feature attribution | Explains what the model did, not whether the premises remain valid. Static at prediction time. |
The core terminology of Reasoning Validity and the Cognitive Causality Architecture.
The Contextual Confidence Score separates model confidence from premise validity. A high model confidence on invalid premises produces a low overall score.
The Validity Warrant score is computed as a gravity-weighted average of premise validities, penalised by drift from initial conditions.
The system addresses data quality, continuous monitoring, transparency, and documentation requirements across major regulatory frameworks.
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