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--- |
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license: cc-by-4.0 |
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language: |
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- en |
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tags: |
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- code |
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size_categories: |
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- 10M<n<100M |
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--- |
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# HeuriGen |
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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. |
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## 🧠 Motivation |
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While LLMs have shown impressive capabilities in coding and open-ended reasoning, existing benchmarks fall short: |
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- Objective benchmarks (e.g., HumanEval, AIME) are prone to saturation and fail to test creativity or multi-step reasoning. |
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- Subjective evaluations (e.g., Chatbot Arena) allow diverse outputs but often rely on noisy or superficial feedback. |
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To bridge this gap, HeuriGen introduces real-world CO tasks that: |
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- Feature well-defined objectives with expansive solution spaces. |
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- Require heuristic design, not just memorized answers. |
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- Enable quantitative and automated evaluation through code execution. |
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## Problem Set |
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| Problem | Domain | |
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| :--: | :--: | |
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| [Operator Scheduling]() | Electronic Design Automation | |
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| [E-Graph Extraction]() | Compilers | |
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| [Pickup and Delivery w/ Time Windoes]() | Logistics | |
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| [Technology Mapping]() | Electronic Design Automation | |
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| [Global Routing]() | Electronic Design Automation | |
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| [Protein Sequence Design]() | Computational Biology | |
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| [Airline Crew Pairing]() | Logistics | |
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| [Pedigree]() | Computational Biology | |
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| [Intra-Op Parallelism]() | Compilers | |
