Beyond Chain-of-Thought: The Rise of Multiplex Thinking in LLMs
Автор: AI Paper Review
Загружено: 2026-01-21
Просмотров: 28
Introducing a new mechanism called Multiplex Thinking proposed to improve the complex reasoning ability of large-scale language models. The traditional chain thinking (CoT) method is expensive because it requires the creation of a long, discrete token sequence, but this method samples K candidate tokens at each stage and compresses them into a single continuous multiplex token. This approach allows a superposition state that maintains multiple inference paths at the same time, allowing the model to process information efficiently even in uncertain situations. In particular, the multiplex trajectory maintains probabilistic characteristics, so it has the advantage of being possible to directly optimize through **enhanced learning (RL)**. As a result of the experiment, this method has demonstrated excellent performance, recording high accuracy even in shorter sentences than conventional methods in mathematical reasoning benchmarks. As a result, Multiplex Thinking is evaluated as an innovative methodology that maximizes the efficiency of reasoning and the ability to explore at the same time.
https://arxiv.org/pdf/2601.08808
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