Cognition · Software component
Reasoning Verifier
Software componentCognitionCognition & MemoryVariation point (abstract)arc:ReasoningVerifier
An abstract external verification component that judges the correctness of an agent's reasoning steps or chain, independently of the model that produced them.
Responsibility. Judges whether agent reasoning steps are valid.
Also known as: Verification model, Reasoning judge, Step-level verifier, Process verifier
Variants
| Variant | When to choose |
|---|---|
| Business Rule Logic Verifier | Choose in regulated industries where business rules and domain constraints can be encoded as formal logic and compliance must be verifiably met. |
| Circuit Reasoning Verifier | Choose when model internals are accessible and verification must reflect what actually happened inside the model rather than its verbal explanation; requires per-domain calibration. |
| Fine-Tuned Step Verifier | Choose when domain-specific labeled correct/incorrect reasoning steps (human-annotated or synthetic) are available and higher in-domain accuracy is required, e.g., compliance, legal or policy reasoning. |
| Formal Proof Verifier | Choose for mathematical, symbolic or other formally specified domains where errors have severe consequences (financial calculation, cryptographic protocols, safety-critical systems) and reasoning can be translated into formal statements. |
| Symbolic Math Verifier | Choose when reasoning contains arithmetic or algebraic manipulations that can be extracted and independently re-computed with a symbolic mathematics library. |
| Zero-Shot Step Verifier | Choose when labeled reasoning-correctness data is unavailable and moderate agreement with human judgment suffices, using a properly prompted general LLM. |
Relationships
is configured by structural
is invoked by dependency
escalates to dynamic
is triggered by dynamic
evaluates assurance
Design guidance
- SHOULD run on every step in high-throughput low-stakes use, flag suspicious steps for human review in high-stakes use, and run only on final chains when resource-constrained.
- SHOULD evaluate each step in the context of prior steps and re-evaluate all dependent steps when an earlier step is flagged.
- MUST NOT be relied upon as a single uncorroborated verifier for high-stakes decisions; combine with other verifiers or human review.
- SHOULD check both logical form and premise truth, and assess reasoning quality independently of final-answer correctness.
Classification
- Patterns
- Step-level inspectionTask-level inspectionDefense-in-depth verification
- Quality attributes
- Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)Interaction capability (ISO/IEC 25010)
- Risks mitigated
- Logical fallaciesMathematical errorsScope confusionCategory confusionCascading step errorsUnfaithful CoT explanationsHallucinated claims in medical CoTOmission of critical input facts
Sources
- Ch3.6: T. Nguyen, "Trace Analysis and Execution Debugging," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.6. ISBN: 9798244538229.
- Ch5.1: T. Nguyen, "Chain-of-Thought (CoT) Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.1. ISBN: 9798244538229.