LLMs Get Lost in Evolving User Intent

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 inten

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ethics.ai (21 July 2026), “LLMs Get Lost in Evolving User Intent,” evidence record 13013, https://ethics.ai/record/13013 (originally published by HuggingFace Daily Papers).

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