ODAR: Principled Adaptive Routing for LLM Reasoning via Active Inference

arXiv:2602.23681v1 Announce Type: new Abstract: The paradigm of large language model (LLM) reasoning is shifting from parameter scaling to test-time compute scaling, yet many existing approaches still rely on uniform brute-force sampling (for example, fixed best-of-N or self-consistency) that is costly, hard to attribute, and can trigger overthinking with diminishing returns. We propose ODAR-Exper