arXiv:2605. 29649v2 Announce Type: replace Abstract: Heuristic search is the dominant paradigm in symbolic AI planning, and the strongest heuristics are the result of decades of work by planning researchers.
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arXiv:2606. 02438v1 Announce Type: new Abstract: Learned heuristics have recently become a competitive alternative to traditional domain-independent heuristics for satisficing planning.
arXiv:2501. 18784v5 Announce Type: replace Abstract: Heuristics are a central component of deterministic planning, particularly in domain-independent settings where general applicability is prioritized over task-specific tuning.
The paper introduces ARTEMIS, a no-code evolutionary optimization platform that automatically tunes large language model (LLM) agents by jointly optimizing prompts, tool descriptions, and parameters using semantically-aware genetic operators. Starting from a benchmark script and natural language goals, ARTEMIS discovers configurable components, extracts performance signals from execution logs, and evolves configurations without architectural changes. Experiments on four agent systems show significant gains: a 13.6% increase in acceptance rate for the ALE Agent, a 10.1% performance boost for the Mini‑SWE Agent, a 36.9% token‑reduction for the CrewAI Agent, and a 22% accuracy improvement for the MathTales‑Teacher Agent using a smaller open‑source model.
arXiv:2606. 14202v1 Announce Type: cross Abstract: Large Language Models (LLMs) have advanced Automatic Heuristic Design (AHD) by enabling heuristic generation through reasoning and code synthesis.
OR-Agent is a multi‑agent research framework that automates heuristic design for optimization problems by structuring heuristic search as a tree‑based workflow with explicit hypothesis generation and systematic backtracking. It introduces a hierarchical, optimization‑inspired reflection system that uses short‑term reflections as verbal gradients, long‑term reflections as verbal momentum, and memory compression as semantic weight decay to guide research dynamics. Experiments on classical combinatorial optimization tasks and simulation‑based cooperative driving scenarios show that OR‑Agent outperforms strong evolutionary search baselines, with all code and data publicly available.
arXiv:2606. 29082v1 Announce Type: cross Abstract: Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture?