arXiv:2609.37588v1 Announce Type: new
Abstract: Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the...
By T. Duy Nguyen-Hien, Yee Whye Teh, Wee Sun Lee, Tan Zhi-Xuan
Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the request --- risking misalignment with the user --...
arXiv:2607. 02686v1 Announce Type: new Abstract: Reinforcement learning agents operating under partial observability must act on incomplete information, making them natural candidates for guidance from small language models (SLMs) that carry broad reasoning priors.
By Juarez Monteiro, Nathan Gavenski, Guilherme Lima, Francisco Galuppo, Odinaldo Rodrigues, Adriano Veloso
arXiv:2606. 03135v1 Announce Type: new Abstract: Large Language Model (LLM) agents often operate under underspecified user instructions, where latent uncertainty over user intent leads to erroneous tool actions.
By Mengyi Deng, Zhiwei Li, Xin Li, Tingyu Zhu, Ying Zhao, Zhijiang Guo, Wei Wang
arXiv:2609.37236v1 Announce Type: new
Abstract: An agent that uses tools typically responds to what the user explicitly asks, yet completing the task may require information the user never requested....
By Ido Levy, Asaf Yehudai, Segev Shlomov, Asaf Adi, Leshem Choshen
arXiv:2607. 12267v1 Announce Type: cross Abstract: Language agents that interleave reasoning and tool use degrade sharply as reasoning chains lengthen, even when each individual step is easy.
By Ning Liu