LoTR: Logic-of-Thought Routing for Plug-and-Play Reasoning of LLMs

Reasoning has become a primary means of improving the problem-solving capabilities of Large Language Models (LLMs), with paradigms prescribing external reasoning strategies such as step-by-step, iterative revision, and search-and-sampling. However, while these paradigms guide how the model reasons, they do not consider whether the model's internal computation pathway is well-matched to the dynamic reasoning logic state under the prescribed strategy, limiting the potential under this strategy. We propose Logic-of-Thought Routing (LoTR), a plug-and-play reasoning module that strengthens existing reasoning paradigms through logic-conditioned internal pathway routing. Concretely, LoTR identifies dynamic logic states from the model's internal information transfer, couples them with attention-head pathway patterns, and uses a lightweight probe to infer the current logic-state mixture and softly route the corresponding pathway. This enables the model to better realize the prescribed reasoning strategy through an internal pathway matched to the current reasoning logic. Across 3 backbones, 10 benchmarks, and 8 reasoning paradigms, LoTR improves matched accuracy by 3.60% on average, reaching 5.37% on Llama with only +0.15% completion tokens and lower latency on 2 backbones. It also outperforms the mean of 4 inference-time intervention baselines on all 3 backbones.