{"id":1179724,"date":"2026-07-23T22:22:36","date_gmt":"2026-07-24T05:22:36","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=1179724"},"modified":"2026-07-23T22:28:08","modified_gmt":"2026-07-24T05:28:08","slug":"llms-get-lost-in-evolving-user-intent","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/llms-get-lost-in-evolving-user-intent\/","title":{"rendered":"LLMs Get Lost in Evolving User Intent"},"content":{"rendered":"\n\n\n
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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Jihoon Tack","user_id":"44058"},{"type":"user_nicename","value":"Philippe Laban","user_id":"43662"},{"type":"user_nicename","value":"Jennifer 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