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LLM-generated solvers fall into 'heuristic trap' on combinatorial problems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new benchmark, CP-SynC-XL, comprising 100 combinatorial problems to evaluate how Large Language Models (LLMs) synthesize executable solvers. Their findings indicate that using LLMs to formalize problems for existing solvers like OR-Tools in Python yields higher correctness than declarative modeling in MiniZinc. Prompting LLMs to also optimize search strategies resulted in only minor speed-ups and a significant drop in correctness for many problems, attributed to a "heuristic trap" where LLMs replace complete search with approximations or introduce over-constraining machinery. AI

IMPACT Highlights the risks of using LLMs for direct optimization in solver generation, suggesting a focus on formalization for verified solvers.

RANK_REASON Academic paper introducing a new benchmark and evaluating LLM-generated solvers. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM-generated solvers fall into 'heuristic trap' on combinatorial problems

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Academic paper introducing a new benchmark and evaluating LLM-generated solvers. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Dan Roth ·

    Formalize, Don't Optimize: The Heuristic Trap in LLM-Generated Combinatorial Solvers

    Large Language Models (LLMs) struggle to solve complex combinatorial problems through direct reasoning, so recent neuro-symbolic systems increasingly use them to synthesize executable solvers. A central design question is how the LLM should represent the solver, and whether it sh…

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