arXiv:2606. 14778v1 Announce Type: cross Abstract: Long-term action anticipation (LTA) aims to predict an ordered sequence of future verb-noun actions from a partially observed video.
By Rui Cao, Jiannong Cao, Bo Yuan, Zhiyuan Wen, Mingjin Zhang
arXiv:2608. 05729v1 Announce Type: new Abstract: As capabilities rapidly increase, AI agents can move from running inside one app to acting across a user's devices over time.
By Xinshuang Liu, Runfa Blark Li, Shaoxiu Wei, Xin Lin, Truong Nguyen
The paper introduces OODA-Tool, a typed closed‑loop policy that separates state preservation from action generation to reduce state‑action competition in multi‑turn tool use. It follows Boyd’s Observe‑Orient‑Decide‑Act cycle, reconstructing task state, deciding on execution, forming admissible actions, and then realizing outputs. Experiments with Qwen3 models show OODA‑Tool consistently improves task success, especially for smaller models and tasks requiring accumulated information.
By Rongfeng Guo, Yinxuan Huang, Yusen Wu, Maoqing Zhong, Yunlu Chen, Meng Tang, Teng Long, Vincent Tao Hu
arXiv:2605.30152v2 Announce Type: replace-cross
Abstract: Proactive assistants continuously decide when to intervene and what context should support the intervention. Large language model (LLM) pipel...
By Xiaoze Liu, Ruowang Zhang, Amir H. Abdi, Michel Galley, Zhikai Chen, Siheng Xiong, Xiaoqian Wang, Jing Gao
arXiv:2610.07578v1 Announce Type: new
Abstract: In cooperative Multi-Agent Reinforcement Learning (MARL), agents are often trained under concurrent participation, while in many tasks some agents act...
By Jianglin Qiao, Siyi Hu, Thien Hoang Nguyen, Zehong Cao, Salah Sukkarieh
arXiv:2606. 27136v1 Announce Type: new Abstract: For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience.
By Shicheng Ye, Chao Yu