AI 在对话中跟不上你变卦
你和一个 AI 聊天交代任务，中途改了主意，它大概率会懵。研究者把静态任务改造成多轮对话——用户意图会逐步透露、修正甚至转向——结果发现，在静态测试里表现很好的模型，一旦面对意图变化，性能大幅跳水。这不是某个模型的毛病，而是当前所有大模型的通病：它们擅长一次说清的任务，但跟不上人类边聊边改的真实习惯。这篇论文不是教你明天怎么用，而是揭示了一个被忽视的鸿沟——未来的协作 AI 必须学会跟踪你变来变去的想法，而现在的模型还做不到。
📄 原文摘要(英文)
As LLMs become more capable, they are increasingly deployed as collaborative agents, taking on user-delegated tasks through iterative interaction. Yet genuine interaction is inherently dynamic: users rarely specify their intent upfront, instead disclosing, revising, and reshaping it as the conversation unfolds. Despite this, LLMs are still predominantly evaluated or trained in single-turn, fully-specified settings, leaving open a fundamental question: how well do LLMs track and act on user intent as it evolves over the course of a conversation? To study this, we introduce a framework that transforms static, single-turn tasks into dynamic multi-turn conversations in which the user's intent evolves across turns--incrementally revealed, revised, and at times redirected mid-conversation--while preserving each task's original evaluation protocol, enabling existing benchmarks to be reused as controlled testbeds without new annotation. Across multiple tasks, we surface a consistent phenomenon: strong static-setting performance does not transfer to the evolving-intent setting, with substantial drops across model families. Our findings point to a fundamental gap: today's LLMs do not yet faithfully track and act on the user's evolving intent, a capability invisible to static evaluation yet critical for future collaborative agents.