Imbuing Large Language Models with Bidirectional Logic for Robust Chain Repair 事件

Imbuing Large Language Models with Bidirectional Logic for Robust Chain Repair arXiv:2606.05030v1 Announce Type: new Abstract: Autoregressive chain-of-thought (CoT) reasoning in large language models (LLMs) is fundamentally forward-directed: each step conditions only on prior tokens. This unidirectional inductive bias renders even capable models susceptible to error snowballing, wherein a single logical or arithmetic mistake in an early step irreversibly corrupts the entire reasoning chain. We