HeuriGen is a benchmark and agentic evaluation framework designed to rigorously assess Large Language Models (LLMs) on combinatorial optimization (CO) problems — a domain where success requires more than pattern recognition: it demands creative algorithm design, multi-step planning, tool use, and adaptive reasoning.

🧠 Motivation

While LLMs have shown impressive capabilities in coding and open-ended reasoning, existing benchmarks fall short:

Objective benchmarks (e.g., HumanEval, AIME) are prone to saturation and fail to test creativity or multi-step reasoning.
Subjective evaluations (e.g., Chatbot Arena) allow diverse outputs but often rely on noisy or superficial feedback.

To bridge this gap, HeuriGen introduces real-world CO tasks that:

Feature well-defined objectives with expansive solution spaces.
Require heuristic design, not just memorized answers.
Enable quantitative and automated evaluation through code execution.

Problem Set

Problem	Domain
Operator Scheduling	Electronic Design Automation
E-Graph Extraction	Compilers
Pickup and Delivery w/ Time Windoes	Logistics
Technology Mapping	Electronic Design Automation
Global Routing	Electronic Design Automation
Protein Sequence Design	Computational Biology
Airline Crew Pairing	Logistics
Pedigree	Computational Biology
Intra-Op Parallelism	Compilers

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