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arXiv Computation and Language By Bin Wu, Guanyun Zou, Bingbing Wang, Huan Zhao, Chuan Shi

Ask Now, Use Later: Benchmarking the Proactivity Gap in Long-Lived LLM Agents

Read the original on arXiv Computation and Language →

The paper introduces ATRBench, a benchmark that measures the proactivity gap in long‑lived LLM agents by evaluating their ability to ask for user preferences that are not needed immediately but may be useful in future sessions. It defines the Ask‑to‑Remember (ATR) task, where agents must decide whether to request a reusable preference now, and shows that current state‑of‑the‑art agents perform significantly below an oracle. The study identifies preference acquisition as the main bottleneck and provides a diagnostic framework for improving agent proactivity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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