arXiv:2610.27620v1 Announce Type: cross
Abstract: Autonomous LLM agents act through tools under per-action authorization: a local guard screens each tool call in isolation. We show that local authori...
By Anmol Pandey, Aditya Jain, Liang Chen, Carsten Maple
arXiv:2608.23224v1 Announce Type: cross
Abstract: Retrieval can efficiently and effectively augment a frozen vision--language--action (VLA) policy without retraining, yet retrieved text becomes a con...
By Zhiruo Zhou, Zelin Li, Xiwen Chen, Jiazhuo Li, Chenwei Wang, Huiming Chen, Xiaojun Zhu
arXiv:2606. 10385v1 Announce Type: cross Abstract: On-policy distillation (OPD) has demonstrated strong empirical gains in enhancing complex reasoning in LLMs by aligning a student model with a teacher's predictive distribution over the student's own trajectories.
By Wenhao Zhang
arXiv:2607. 25364v1 Announce Type: new Abstract: Tool-using agents expose structured calls but commonly attach free-form rationales.
By Genliang Zhu (Accentrust, Georgia Institute of Technology), Chu Wang (Accentrust, University of Illinois Urbana-Champaign)
arXiv:2606. 03089v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) has emerged as an efficient post-training paradigm by using a teacher conditioned on privileged information to provide dense token-level supervision.
By Ming Wen, Yuxuan Liu, Kun Yang, Yunhao Feng, Zhuoer Xu, Yuhao Sun, Shiwen Cui, Xiang Zheng, Xingjun Ma, Yu-Gang Jiang
The paper introduces the Event Reasoning System (ERS), a framework that represents reasoning as an explicit event graph and uses hierarchical rule composition to manage dependencies and revisions. ERS employs semantic features to nominate candidate objects and rule bindings, while low‑level rules provide reusable transformations and high‑level control rules organize subgoals, ensuring that only admissible operations enter accepted reasoning paths. Empirical results on structured‑event graphs show ERS can build long dependency chains, eliminate irrelevant operations, and maintain correctness across numerous test cases, while reducing inference time by over 50% compared to single‑level control.
By Cheng Lyu, Xueqi Wang