When Small Models Are Right for Wrong Reasons: Process Verification for Trustworthy Agents

• URL: http://arxiv.org/abs/2601.00513v1
• Date: Thu, 01 Jan 2026 23:54:15 GMT
• Title: When Small Models Are Right for Wrong Reasons: Process Verification for Trustworthy Agents
• Authors: Laksh Advani,
• Abstract summary: We reveal a critical reliability crisis: 50-69% of correct answers from small language models contain fundamentally flawed reasoning.<n>We introduce the Reasoning Integrity Score (RIS), a process-based metric validated with substantial inter-rater agreement.<n>We show RAG succeeds by grounding calculations in external evidence, reducing errors by 7.6%, while meta-cognition amplifies confusion without sufficient model capacity.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying small language models (7-9B parameters) as autonomous agents requires trust in their reasoning, not just their outputs. We reveal a critical reliability crisis: 50-69\% of correct answers from these models contain fundamentally flawed reasoning -- a ``Right-for-Wrong-Reasons'' phenomenon invisible to standard accuracy metrics. Through analysis of 10,734 reasoning traces across three models and diverse tasks, we introduce the Reasoning Integrity Score (RIS), a process-based metric validated with substantial inter-rater agreement ($κ=0.657$). Conventional practices are challenged by our findings: while retrieval-augmented generation (RAG) significantly improves reasoning integrity (Cohen's $d=0.23$--$0.93$), meta-cognitive interventions like self-critique often harm performance ($d=-0.14$ to $-0.33$) in small models on the evaluated tasks. Mechanistic analysis reveals RAG succeeds by grounding calculations in external evidence, reducing errors by 7.6\%, while meta-cognition amplifies confusion without sufficient model capacity. To enable deployment, verification capabilities are distilled into a neural classifier achieving 0.86 F1-score with 100$\times$ speedup. These results underscore the necessity of process-based verification for trustworthy agents: accuracy alone is dangerously insufficient when models can be right for entirely wrong reasons.

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