arXiv:2606.00981v2 Announce Type: replace
Abstract: LLMs can plan by generating schedules directly as a Planner, or by translating a task into a formal specification for a solver as a Formalizer. Mos...
By Jiayi Zhang, Jianing Yin, Ben Zhou, Li Zhang
arXiv:2609.38108v1 Announce Type: new
Abstract: Large language models (LLMs) enable agents to solve long-horizon tasks by generating a plan and then executing it in an environment. However, successfu...
By Subba Reddy Oota, Francisco Herrera, Jordi Cabot Sagrera, Marcos L\'opez de Prado, Shadab Khan
Translating natural-language planning intent into verified plans is a longstanding challenge: people communicate goals in language, while classical planners require formal PDDL specifications. Recent agentic frameworks bridge this gap by orchestrating a pool of specialized repair agents inside a verifier-checked refinement loop, but the orchestrator at the centre is itself a prompted frontier LLM, paying a frontier-LLM API call at every refinement step.
arXiv:2611.07461v1 Announce Type: cross
Abstract: How can an agent handle a real-world task end to end? Frontier models with their own harnesses still fall short on real-world benchmarks: on Terminal...
By Haibo Jin, Peng Kuang, Xiaopeng Yuan, Lucheng Fu, Jerry Wang, Xucheng Yu, Yaoning Yu, Dehao Wu, Runguo Li, Colin Su, Jingxuan Sun, Xuyang Liu, Haojing Luo, Haohan Wang
Plan-and-Patch is a plan‑and‑act framework that uses a diffusion language model to generate structured, program‑like plans and then repairs them by filling in selected regions while keeping the surrounding steps fixed. The approach avoids regenerating entire plans, reducing unnecessary changes. Experiments show that on the Natural Plan benchmark, diffusion planners achieve a 53.7% plan‑repair success rate versus 27.0% for autoregressive planners, and after task‑specific training on ALFWorld and TextCraft, diffusion planners match autoregressive planners in plan generation success while cutting mean plan‑generation latency by 39–46%.
By Syamantak Kumar, Jiang Guo, Hassan Hamad, Hideo Kobayashi, Yi Xiang, Yezhou Yang, Yanjun Qi, Daniele Bonadiman, Jiarong Jiang
arXiv:2608.23035v1 Announce Type: new
Abstract: As on-device LLM agents evolve into personal copilots, the mobile operating system has become a key testbed for this paradigm, making rigorous capabili...
By Yi Zhu, Xiongwei Wu, Qiyi Wang, Tingyu Qu, Jiajun Liu, Sihan Cao, Long Chen, Weigao Sun, Feida Zhu, Yiran Zhong, Steven Hoi