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IntentDelta: A Human-Constructed Benchmark for Evolving User Intent

2026-10-24 17:10:48
Jieyu Zhou, Zijian Wang, Yun Wang
arXiv_CL
     
Abstract

Language-model agents must adapt as users revise their requirements and priorities during complex tasks. We introduce IntentDelta, a human-constructed multi-turn benchmark with 638 trajectories and 3,766 turns across WebShop and TravelPlanner. Each turn pairs a user utterance with a cumulative intention state and human reference actions. The trajectories are cross-verified and cover feasibility- and preference-driven changes in constraints and priorities. We evaluate eleven agents on intention recovery and action quality. Average Intention Recovery is 33.1\% in TravelPlanner and 44.7\% in WebShop, while average Action Score is 31.6% and 41.0%, respectively. Agents struggle to preserve earlier requirements and distinguish soft preferences from must-have constraints. Correct intention recovery also does not guarantee successful execution, highlighting challenges in translating evolving user intentions into actions.

Abstract (translated)

URL

https://arxiv.org/abs/2610.33806

PDF

https://arxiv.org/pdf/2610.33806.pdf

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