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Research questionHow can we evaluate LLM reasoning quality beyond final-answer accuracy across deployment contexts?Final-answer correctness can conceal inconsistent, fragile, locally incoherent, or inefficient reasoning. These hidden differences make it difficult to choose models reliably for particular deployment contexts.
AI
Evaluation & Benchmarks
Machine Learning
Natural Language Processing
Reasoning
Latest papersRecent research connected to this question, newest first.Measuring Reasoning Quality in LLMs: A Multi-Dimensional Behavioral FrameworkThe framework evaluates multiple LLMs and benchmarks using behavioral dimensions including correctness, consistency, robustness, local logical coherence, efficiency, and stability, with deployment-aware aggregation. Its evidence is experimental and domain-specific validation remains open.research paper · Sep 2, 2026
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