arXiv:2605.28006v1 Announce Type: cross Abstract: Understanding how LLMs reason is hindered by a practical asymmetry: while their generated outputs are observable, the underlying reasoning patterns remain opaque. Relying on single probes, such as Mutual Information Peak (MIP) or Deep-Thinking Ratio (DTR), risks underestimating the genuine inferential structure. To response this deficiency, we present an Integrated, cross-Architecture Reasoning (IAR) framework, designed to provide a unified approach to LLM reasoning interpretability. Specifically, we first propose to use bandwidth-calibrated MI
The rapid advancement and widespread deployment of large language models are creating an urgent need for greater transparency and interpretability of their reasoning processes.
Understanding LLM reasoning is crucial for trust, regulation, and improving model capabilities, moving beyond opaque outputs to genuinely comprehend their inferential structures.
This framework offers a more unified and robust method for interpreting LLM reasoning, potentially leading to more reliable and controllable AI systems.
- · AI developers
- · AI researchers
- · Regulators
- · Ethical AI practitioners
- · Proponents of opaque AI
- · Black-box model industries
Improved interpretability tools facilitate better debugging and auditing of complex LLMs.
Increased trust in AI systems could accelerate adoption in sensitive or critical applications.
Standardized interpretability frameworks may become a regulatory requirement for AI deployment, fostering responsible AI development.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI