Robust Asynchronous Planning via Auto-Formalization
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
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.
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...
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.
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.
arXiv:2606. 29700v1 Announce Type: new Abstract: Planning often requires symbolic specifications that are both executable and verifiable.
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.