Selective Latent Thinking: Adaptive Compression of LLM Reasoning Chains 事件

Selective Latent Thinking: Adaptive Compression of LLM Reasoning Chains arXiv:2605.25745v1 Announce Type: new Abstract: Explicit chain-of-thought (CoT) reasoning substantially improves the reasoning ability of large language models (LLMs), but incurs high inference cost due to lengthy autoregressive traces. Existing latent reasoning methods offer a promising alternative, yet they often treat reasoning as uniformly compressible, causing precision-critical intermediate steps to be overly compress