arXiv:2605.29649v1 Announce Type: new 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. Recent work has shown that large language models (LLMs) can design heuristics for individual planning domains, but no LLM-generated heuristic has so far worked on arbitrary planning tasks. In this paper, we use evolutionary search to produce the first LLM-generated domain-independent heuristics that exceed the hand-engineered state of the art. We let an LLM mutate parent heuristics written in C
The proliferation of powerful LLMs and advances in evolutionary search algorithms are converging, enabling new breakthroughs in AI planning that were previously inaccessible.
This development suggests LLMs can now create foundational, domain-independent AI tooling that surpasses human expertise, potentially accelerating AI capabilities across numerous applications.
The reliance on hand-engineered heuristics for symbolic AI planning is diminishing as LLMs demonstrate the ability to evolve superior, universally applicable solutions.
- · AI research institutions
- · Robotics companies
- · Logistics and optimization software providers
- · AI tool developers
- · Traditional symbolic AI planning researchers
- · Specialized AI planning consultancies
- · Companies reliant on bespoke, domain-specific AI solutions
LLMs will be increasingly used to generate and optimize core AI algorithms and heuristics.
The development cycle for new AI applications will shorten significantly as LLMs automate foundational problem-solving.
This could lead to a 'recursive self-improvement' loop for AI, where AI systems design better AI systems, accelerating technological progress beyond human comprehension.
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Read at arXiv cs.AI