arXiv:2605.24661v1 Announce Type: cross Abstract: LLMs have achieved remarkable success in complex reasoning tasks, yet current evaluation approaches predominantly rely on final-answer correctness, offering limited insight into the underlying reasoning processes that produce those answers. To address this gap, this study proposes a unified multi-dimensional framework for measuring reasoning quality in LLMs from a behavioral perspective, operationalizing six theoretically grounded dimensions: Correctness (CQ), Consistency (CS), Robustness (RS), Logical Coherence (LS), Efficiency (ES), and Stabi
The rapid advancement of LLMs necessitates more sophisticated evaluation methods beyond just final answer correctness to understand and improve their underlying reasoning processes.
A multi-dimensional framework for measuring LLM reasoning quality is crucial for better model development, reliable deployment, and deeper scientific understanding of advanced AI capabilities.
The focus of LLM evaluation shifts from mere output correctness to a nuanced understanding of behavioral dimensions like consistency, robustness, and logical coherence, influencing future model architectures and training paradigms.
- · AI researchers
- · LLM developers
- · AI safety and ethics organizations
- · Enterprise AI adopters
- · Developers relying solely on superficial LLM benchmarks
- · Companies with poorly understood LLM deployments
Improved understanding of LLM reasoning will lead to more robust and trustworthy AI systems.
New evaluation protocols could become industry standards, influencing competitive advantage and regulatory frameworks for AI.
Deeper insights into AI reasoning may accelerate the development of truly agentic and generally intelligent AI systems.
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Read at arXiv cs.CL