Measuring Reasoning Quality in LLMs: A Multi-Dimensional Behavioral Framework
Despite remarkable progress on reasoning benchmarks, current LLM evaluation practice remains anchored to final-answer correctness, providing limited insight into how models reason, how reliably they behave under contextual variation, or how efficiently they reach conclusions. This paper proposes a unified multi-dimensional framework for measuring LLM reasoning quality from a behavioral perspective, operationalizing six theoretically grounded dimensions rooted in cognitive science: Correctness (CQ), Consistency (CS), Robustness (RS), Local Logical Coherence (LS), Efficiency (ES), and Stability (SS). The framework introduces deployment-aware aggregation, enabling context-specific model sele...
| Authors | Ali Şenol, Garima Agrawal, Huan Liu |
|---|---|
| Primary category | cs.AI |
| Published | 23 May 2026 |
| Last updated at source | 2 September 2026 |
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