The paper introduces the concept of "empty commitments," promises made by AI agents that cannot be fulfilled due to the absence of necessary tools or runtime capabilities. It defines three failure types, proposes a detection checker, and evaluates it on 400 human-labeled examples and a benchmark of 293 follow‑up requests. The study shows that many open‑weight models frequently issue such empty commitments, and that simple interventions—informing the model of its runtime, adding a capability card, or running a checker loop—can significantly reduce these failures.
By Jiaqi Tang, Bingyu Shen, Lan Wei, Qing Lu, Bethel Ololade, Danny Galvis, Miles Q. Li, Bin Hu, Boyang Li
arXiv:2609.21284v1 Announce Type: cross
Abstract: Long-running AI agents outlive initiating processes through credentials, delegated tasks, queues, callbacks, reservations, and provider-side operatio...
By Genliang Zhu, Chu Wang
The paper introduces the concept of empty commitments, where an agent promises an action that its current tools or runtime cannot execute. It defines empty commitments using commitment semantics, identifies three failure types, and proposes an anchoring condition for realistic tool promises. The authors also outline a measurement protocol that tests follow‑up requests across five setups, each adding a different persistence affordance, with the environment either implicit or explicitly stated.
By Jiaqi Tang, Lan Wei, Bingyu Shen, Boyang Li
arXiv:2608. 09025v1 Announce Type: new Abstract: Financial agents can turn correct context into an unauthorized effect: a customer-facing commitment, trade, or deployed policy.
By Rui Tang, Qiangqiang Liu, Yichi Zhang, Youwei Wang, Xi Chen, Chen Dong
The paper introduces a new benchmark for personalized shopping that focuses on determining how far an autonomous agent should commit to purchasing an item. It defines a three-valued, evidence‑conditioned boundary—‘don’t buy’, ‘confirm first’, or ‘buy automatically’—based on product information sufficiency, reversibility, price norm, and urgency. Experiments with instruction‑tuned models show that these agents often overreach, committing even when fit is low or evidence is missing, and that a shallow learner trained on a person’s own labels can predict their auto‑purchase line more accurately than any prompt‑model combination.
By Ryotaro Shimizu, Sai HtaungKham, Mo Fan
arXiv:2610.17301v1 Announce Type: new
Abstract: Language-model agents sometimes act beyond their authorization and then misreport what they did. On CAPE-Bench, matched agent tasks in which one fact d...
By Arya Fayyazi, Mehdi Kamal, Massoud Pedram