Kairos is a planning system that orchestrates five heterogeneous planning engines—ground heuristic search, compressed and snap-action temporal search, time-step hybrid dynamics, and lifted search—based on a semantic profile of each task. By scheduling these engines within a shared time and memory budget and validating every candidate plan with an independent validator, Kairos achieves broader PDDL coverage (from STRIPS to PDDL+) and outperforms the strongest specialized planners on 1,756 tasks, especially in numeric, temporal, PDDL3, and PDDL+ domains. The system also handles all seventeen PDDL features tested, with key contributions from compressed temporal search and a novelty fallback mechanism.
By Francisco Mart\'in Rico, Esther Aguado Gonz\'alez, Francisco Miguel Moreno Olivo, Rodrigo P\'erez Rodr\'iguez, Jos\'e Miguel Guerrero Hern\'andez, Francisco J. Rodr\'iguez Lera
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
The paper introduces SpecAgent, a protocol that lets a strong language model act as a reviewer rather than a generator for tool‑using agents. A weaker model drafts steps, executing read‑only tool calls to ground its proposals, while the strong model verifies the draft in a single prefill pass, committing the longest valid prefix and repairing any rejected step. Experiments on benchmarks show SpecAgent achieves near‑strong‑model performance while cutting memory traffic, wall time, and token generation.
By Zilin Wang, Yinhong Liu, Chao Chen, Qun Liu, Zhijiang Guo
arXiv:2608. 05144v2 Announce Type: replace Abstract: Long-horizon reasoning requires an agentic runtime that can persist when evidence supports its current approach and pivot when measurements reveal failure, hidden constraints, or a misspecified objective.
By Boxiu Li, Zimo Wen, Yijia Fan, Chuan Wen, Fan Yang, Hangxi Guo, Jiaao Wu, Jiachen Zhang, Junxiang Lei, Mukai Li, Ruize Tang, Runjing Gu, Shibo Hu, Sihan Chen, Sufeng Guo, Wanbo Zhang, Xian Zhang, Xiaoyu Chen, Xuanhe Zhou, Xuyao Huang, Yifei Gao, Yifei Shen, Yilin Chen, Yuheng Wu, Yuzhe Zhang, Zelong Zhao, Zhijie Deng
arXiv:2606. 29700v1 Announce Type: new Abstract: Planning often requires symbolic specifications that are both executable and verifiable.
By Jiamei Jiang, Jiajing Zhang, Feifei Mo, Linjing Li, Daniel Zeng
Capability‑Grounded Agentic Planning (CGAP) is a method that transforms a natural‑language request into a deterministic PDDL model by compiling a catalog of tools, then uses a classical planner to generate an abstract plan. An LLM interprets the request as goals and, during execution, adds missing entity‑specific calls while a verifier ensures each result satisfies the original request. Experiments on ToolSandbox, ComplexFuncBench, BFCL multi‑turn, and τ²‑bench retail show CGAP can outperform standard tool‑calling agents by up to 28.9 points.
By Shikha Shrestha, William Yeoh, Ning Zhang, Yevgeniy Vorobeychik
arXiv:2608. 05144v1 Announce Type: new Abstract: Long-horizon reasoning requires an agentic runtime that can persist when evidence supports its current approach and pivot when measurements reveal failure, hidden constraints, or a misspecified objective.
By Boxiu Li, Zimo Wen, Yijia Fan, Junxiang Lei, Sufeng Guo, Jiaao Wu, Ruize Tang, Mukai Li, Yifei Shen, Xiaoyu Chen, Wanbo Zhang, Runjing Gu, Yifei Gao, Yuheng Wu, Xuyao Huang, Zelong Zhao, Jiachen Zhang, Shibo Hu, Hangxi Guo, Yilin Chen, Yuzhe Zhang, Fan Yang, Chuan Wen, Xian Zhang, Xuanhe Zhou, Zhijie Deng
arXiv:2508. 02721v2 Announce Type: replace-cross Abstract: While powerful, the inherent non-determinism of large language model (LLM) agents limits their application in structured operational environments where procedural fidelity and predictable execution are strict requirements.
By Libin Qiu, Yuhang Ye, Zhirong Gao, Xide Zou, Junfu Chen, Ziming Gui, Weizhi Huang, Xiaobo Xue, Wenkai Qiu, Kun Zhao
arXiv:2607. 01942v1 Announce Type: new Abstract: LLM-based agents have shown strong potential for solving complex multi-step tasks, yet existing performance improvements often rely on either scaling to larger backbone models or task-specific fine-tuning.
By Yue Zhang, Sihan Chen, Ziwen Huang, Hanyun Cui, Kangye Ji, Zhi Wang
MemPlan is a memory‑conditioned PDDL planning framework designed for partially observable interactive text environments. It keeps the PDDL domain fixed and reconstructs the problem at each step using parsed observations, candidate hypotheses, and interaction memory, employing positive graph memory to assign relevance‑based action costs and negative memory to record refuted assumptions as exclusion constraints. Experiments on TextWorld, ALFWorld, and Robotouille demonstrate that MemPlan improves task completion and step efficiency over language‑only and PDDL‑aware baselines while significantly reducing online token cost compared with LLM‑planning baselines such as ReAct, and its fine‑tuned interface generalizes across task types without task‑specific fine‑tuning.
By Tengwei Song, Robert Hoehndorf
The paper introduces Consistent Plan-Act (ConPAct), a method that addresses coordination failures between high-level planners and low-level actors in long-horizon agentic tasks. By prompting both agents to produce structured state assertions and programmatically detecting contradictions, the authors identify a systematic planner-actor state mismatch. ConPAct feeds these detected contradictions back to both agents, fine‑tunes them on consistent interactions, and achieves notable performance gains, such as raising MiniGrid success rates from 38.6% to 54.4% with GPT‑5.6‑sol/terra.
By Heng-Zhuang Li, Yi-Kai Zhang, Yu Wang, Yueqing Sun, Jiayuan Zhang, Qi Gu, Han-Jia Ye
arXiv:2608. 04265v1 Announce Type: cross Abstract: Evaluations of LLM planning agents largely ask whether a task succeeds or a declared plan is followed.
By J. de Curt\`o, I. de Zarz\`a