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

TL;DR

This paper exposes a reliability crisis in small language models, where many correct answers are based on flawed reasoning, and introduces a process verification metric to improve trustworthiness.

Contribution

It introduces the Reasoning Integrity Score (RIS), a process-based metric validated across diverse tasks, and analyzes the effects of retrieval augmentation and meta-cognition on reasoning quality.

Findings

50-69% of correct answers contain flawed reasoning

Retrieval-augmented generation improves reasoning integrity significantly

Meta-cognitive interventions can harm performance in small models

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 ($\kappa=0.657$). 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.