arXiv:2602. 16763v2 Announce Type: replace Abstract: Artificial intelligence benchmarks are an important mechanism for measuring model progress and guiding deployment decisions.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2501. 10711v5 Announce Type: replace-cross Abstract: Code-related benchmarks play a critical role in evaluating large language models (LLMs), yet their quality fundamentally shapes how the community interprets model capabilities.
arXiv:2605. 08678v3 Announce Type: replace Abstract: Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes.
arXiv:2606. 26158v1 Announce Type: new Abstract: When a benchmark's accuracy saturates, it is often retired and replaced with a more challenging version.
arXiv:2605. 28508v2 Announce Type: replace Abstract: Existing AI evaluation practices often fail to capture how systems actually perform in low-resource environments, where operational constraints shape usability as much as model quality.
arXiv:2607. 19386v1 Announce Type: new Abstract: Cross-paper comparison of sparse autoencoder (SAE) interpretability often relies on autointerpretability scores.
HARDEN is a constrained evolutionary search method that transforms existing evaluation cases into more challenging variants while preserving their expected outputs. It operates along domain‑specific complexity axes and enforces feasibility constraints such as task semantics, realism, and execution validity. Experiments on FinQA, PubMedQA, and ContractNLI with Qwen3.5 models show that HARDEN can reduce task‑model accuracy by an average of 22.7% and up to 49.9% compared to single‑pass baselines.