HARDEN: Constrained Evolutionary Search for Harder, Answer-Preserving Evaluation Cases

Abstract:Language models are often evaluated on curated benchmarks that underrepresent the complexity of enterprise deployments. We introduce HARDEN, a constrained evolutionary search method to adapt the input of existing evaluation cases into more challenging variants while keeping their expected outputs fixed. HARDEN searches along generated domain-specific complexity axes while enforcing feasibility constraints such as preserving task semantics, realism, and execution validity. Across FinQA, PubMedQA, and ContractNLI and three Qwen3.5 model scales (35B-A3B, 122B-A10B, and 397B-A17B), HARDEN reduces task-model accuracy by 22.7% on average and by up to 49.9% relative to single-pass baselines using the same feasibility checks. These results show that evolutionary search can produce substantially harder valid evaluation cases.

Artificial Intelligence (cs.AI)

arXiv:2609.30571 [cs.AI]

(or arXiv:2609.30571v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.30571 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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