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New IntentDelta benchmark reveals AI agent struggles with evolving user needs

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced IntentDelta, a new benchmark designed to evaluate how well language model agents can adapt to evolving user intentions during complex tasks. The benchmark, comprising 638 trajectories and 3,766 turns across WebShop and TravelPlanner environments, assesses agents on their ability to recover user intentions and execute appropriate actions. Current agents demonstrate an average intention recovery rate of 33.1% in TravelPlanner and 44.7% in WebShop, with significant challenges in distinguishing between hard constraints and soft preferences, and in translating recovered intentions into successful task execution. AI

IMPACT Highlights critical limitations in current AI agents' ability to adapt to dynamic user requirements, impacting the development of more responsive and user-friendly AI systems.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New IntentDelta benchmark reveals AI agent struggles with evolving user needs

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The cluster contains a research paper introducing a new benchmark for evaluating AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Jieyu Zhou, Zijian Wang, Yun Wang ·

    IntentDelta: A Human-Constructed Benchmark for Evolving User Intent

    arXiv:2610.33806v1 Announce Type: new 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 Tra…

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