arXiv:2603.05290v2 Announce Type: replace Abstract: Large language models (LLMs) achieve promising performance, yet their ability to reason remains poorly understood. Existing evaluations largely emphasize task-level accuracy, often conflating pattern matching with reasoning capability. We present X-RAY, an explainable reasoning analysis system that maps the LLM reasoning capability using calibrated, formally verified probes. We model reasoning capability as a function of extractable \textit{structure}, operationalized through formal properties such as constraint interaction, reasoning depth,
The accelerating pace of LLM development and deployment necessitates a deeper, more rigorous understanding of their true capabilities beyond superficial task performance.
A strategic reader needs to understand the fundamental reasoning capabilities of LLMs to accurately assess their potential, limitations, and areas for strategic investment or caution.
The ability to formally map and calibrate LLM reasoning capability introduces a more precise and less task-centric method for evaluating and comparing advanced AI models.
- · AI researchers
- · LLM developers
- · AI ethics and safety organizations
- · LLM evaluators relying solely on task accuracy
- · Companies overstating LLM reasoning abilities
This research provides a more sophisticated toolkit for understanding and benchmarking the actual reasoning prowess of large language models.
Improved diagnostics will accelerate the development of LLMs with genuinely strong reasoning capabilities, distinguishing them from those adept at pattern matching.
A clearer understanding of LLM reasoning could inform regulatory frameworks and responsible AI deployment strategies at a national and international level.
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Read at arXiv cs.AI