[2510.05069] SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs

Computer Science > Computation and Language arXiv:2510.05069 (cs) [Submitted on 6 Oct 2025 (v1), last revised 1 Mar 2026 (this version, v3)] Title:SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs Authors:Dachuan Shi, Abedelkadir Asi, Keying Li, Xiangchi Yuan, Leyan Pan, Wenke Lee, Wen Xiao View a PDF of the paper titled SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs, by Dachuan Shi and 6 other authors View PDF Abstract:Recent work shows that, beyond discrete reasoning through explicit chain-of-thought steps, which are limited by the boundaries of natural languages, large language models (LLMs) can also reason continuously in latent space, allowing richer information per step and thereby improving token efficiency. Despite this promise, latent reasoning still faces two challenges, especially in training-free settings: 1) purely latent reasoning broadens the search distribution by maintaining multiple implicit paths, which diffuses probability mass, introduces noise, and impedes convergence to a single high-confidence solution, thereby hurting accuracy; and 2) overthinking persists even without explicit text, wasting tokens and degrading efficiency. To address these issues, we introduce SwiReasoning, a training-free framework for LLM reasoning which features two key innovations: 1) SwiReasoning dynamically switches between explicit and latent reasoning, guided by block-wise confidence estimated f...