arXiv AI
Oct 31

Kairos: Semantics-Aware Orchestration of Planning Engines across Classical, Numeric, Temporal and Hybrid PDDL

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 AI
Oct 16

Trajectory-Level Speculation with Semantic Verification for Tool-Using Agents

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 AI
Aug 11

Argus: A General-Purpose Agentic Reasoning Runtime for Long-Horizon Tasks

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 Computation and Language
5d ago

Capability-Grounded Agentic Planning

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