A$^{2}$utoLPBench: An Auto-Generated, Agent-Friendly LP Benchmark via Inverse-KKT Construction
arXiv:2607.02141v1 Announce Type: new Abstract: Most LP-from-text benchmarks are static datasets of word problems written and labeled by hand. Once such a dataset is released, its size is fixed, its difficulty is fixed, and every problem can leak into the training data of future LLMs. We present \textbf{A$^{2}$utoLPBench}, a benchmark for testing LLM-driven agents on linear programming problems written in plain text. We first pick a feasible point and dual, then write down a problem for which that point is optimal and the objective value is known. The answer is known by construction, with no s
The proliferation of large language models and the increasing focus on their autonomous capabilities necessitate more robust and leakage-proof benchmarks to measure their effectiveness in complex problem-solving domains.
This development is crucial for accurately assessing the true problem-solving abilities of AI agents on linear programming tasks, moving beyond static datasets susceptible to data leakage and offering a path to more reliable evaluation.
The shift from human-generated, static benchmarks to auto-generated, dynamically created ones ensures a continuous and novel supply of evaluation problems for LLM-driven agents.
- · AI agent developers
- · Organizations deploying AI for optimization
- · AI ethicists and evaluators
- · Developers relying on easy-to-exploit benchmarks
- · Companies with weak AI problem-solving capabilities
Improved evaluation leads to more capable AI agents for operational tasks.
Greater trust in AI agents for complex business optimization problems emerges across industries.
The development of similar auto-generated benchmarks extends to other difficult problem domains, accelerating widespread AI agent adoption.
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Read at arXiv cs.AI