arXiv:2609.07741v1 Announce Type: new
Abstract: Persistent AI assistants are intended to extend human attention, memory, and coordination across changing digital and physical environments. To be trul...
By Jo\~ao Dias Ferreira
arXiv:2609.37501v1 Announce Type: cross
Abstract: We propose RegLLM, a diagnostic harness for bounded autonomy in regulated agentic workflows. It instruments six trustworthiness signals: citation val...
By Dipankar Sarkar
The paper introduces a new multi‑agent micro‑benchmark called Delay‑of‑Gratification, modeled after the Stanford marshmallow experiment, to evaluate large language models (LLMs) in long‑horizon, multi‑turn interactions. In the benchmark, ReAct agents use a per‑step “raise a question” tool under various constraints—social context (broadcast vs. isolated), persona traits (age, hedonic drive), and tool‑use policy (mandatory vs. optional). Across 19,200 trajectories, the study finds that most agents exhibit an early impulse to “eat,” only 75.9% persist to the end, and factors such as isolation and hedonic drive significantly influence survival and questioning behavior, with ablations showing that removing hedonic drive and age can improve completion rates.
By Olga Manakina, Igor Bogdanov, Chung-Horng Lung
arXiv:2606. 23991v1 Announce Type: new Abstract: What is an agent?
By Eric Xing, Mingkai Deng, Jinyu Hou
arXiv:2607. 15883v1 Announce Type: cross Abstract: Large language models are broadly capable, yet in sustained one-to-one conversation they still read as flat: competent, responsive, and somehow not quite the presence of a mind.
By Sebastian Cochinescu
arXiv:2606. 02965v1 Announce Type: new Abstract: Benchmarks for autonomous agents measure whether agents complete tasks, yet this framing is systematically blind to whether an agent should have proceeded at all.
By Victor Ojewale, Suresh Venkatasubramanian