State of Thought Enables Endogenous Reasoning

Test-time compute has emerged as a major approach to improving the capabilities of Large Language Models (LLMs). However, existing test-time reasoning paradigms rely heavily on externally imposed control, either through fixed reasoning programs or through costly expansion in constrained search spaces, limiting both generalization and efficiency. We propose State of Thought (SoT), a new reasoning paradigm that enables endogenous reasoning in LLMs, with the model's internal reasoning state governing how reasoning unfolds. Concretely, SoT extracts a compact dynamics-geometric state from the model's internal information transfer and selectively activates historical reasoning support useful under the current reasoning state, framing reasoning as a state-conditioned process over evidence rather than an externally prescribed token chain. Across quantitative (1.29x), general (1.62x), symbolic and code (1.72x), long-context (2.63x), and multimodal (1.08x) reasoning tasks on 4 models with 20 datasets, SoT consistently improves task accuracy while reducing tokens by 69.0% and latency by 48.7%, supporting endogenous state-driven reasoning as a more generalizable and efficient alternative.