Cognition · Software component

Fine-Tuned Step Verifier

Software componentCognitionCognition & Memoryarc:FineTunedStepVerifier

A reasoning verifier backed by a model fine-tuned on labeled correct and incorrect reasoning steps of a specific domain, including fallacy-detection training.

Responsibility. Scores reasoning steps using a domain-trained verification model.

Also known as: Domain-specific verifier, Fallacy detector, Trained verifier

Variant of Reasoning Verifier abstract

When to choose. 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.

Direct neighbourhood (hover for relationship types)

Relationships

invokes dependency

Inference Server Ch3.6

alternative to variability

Design guidance

SHOULD be trained on diverse examples of each fallacy type; generalization to new domains is limited.

Quantitative guidance

As stated by the sources; verify before use.

Fine-tuned domain verifiers typically achieve 85-95% accuracy on in-domain verification tasks (Ch3.6).
Compliance case: affirming-the-consequent fallacy appeared in 18% of traces; after verifier and prompt fixes risk detection accuracy +34%, false negatives -67% (Ch3.6).
Customer-service case: policy application consistency improved from 71% to 94% (Ch3.6).

Classification

Patterns: Fallacy-specific detection
Quality attributes: Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
Risks mitigated: Affirming the consequentNecessary/sufficient condition confusionKeyword-matching policy application

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.