SeDT: Sentence-Transformer Decision-Transformer Conditioning for Multi-Turn Conversation Reliability 事件

SeDT: Sentence-Transformer Decision-Transformer Conditioning for Multi-Turn Conversation Reliability arXiv:2605.26788v1 Announce Type: new Abstract: Large language models (LLMs) achieve impressive performance when a task is fully specified in a single turn, yet the same models lose up to 39% of that performance when the identical task is revealed incrementally across multiple turns, a phenomenon documented at scale as Lost in Conversation. Crucially, this collapse is almost entirely a reliabili