arXiv:2602.08498v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) increasingly rely on reasoning traces with complex internal structures. However, existing work lacks a unified answer to three fundamental questions: (1) what defines high-quality reasoning, (2) how to reliably evaluate long, implicitly structured reasoning traces, and (3) how to use such evaluation signals for reasoning optimization. To address these challenges, we provide a unified perspective. (1) We introduce the ME$^2$ principle to characterize reasoning quality along macro- and micro-level concerning effici
The proliferation of complex AI models creates an urgent need for robust evaluation and optimization methods, a challenge this paper directly addresses.
Improved characterization and optimization of AI reasoning directly impacts the reliability, capability, and autonomy of future AI systems, particularly agents.
Our understanding and methods for defining, measuring, and enhancing reasoning quality in advanced AI models are being refined, enabling more robust development.
- · AI model developers
- · AI researchers
- · Developers of AI agents
- · Companies relying on opaque AI performance
- · Unstructured AI evaluation methods
More reliable and efficient large reasoning models emerge, accelerating AI development cycles.
The ability to accurately evaluate and optimize reasoning leads to more capable and trustworthy autonomous AI agents.
Increased reliability and complexity of AI agents begins to fundamentally restructure industries by automating cognitive tasks at scale.
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