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arXiv AI By Haoxiang Wang, Da Yu, Huishuai Zhang

Beyond Fixed Benchmarks and Worst-Case Attacks: Dynamic Boundary Evaluation for Language Models

Read the original on arXiv AI →

The paper introduces Dynamic Boundary Evaluation (DBE), a method that moves beyond static benchmarks by locating the per‑prompt pass probability near 0.5 for large language models. DBE provides a calibrated item bank with difficulty labels, a Skill‑Guided Boundary Search algorithm that works with API‑level access, and an adaptive evaluation protocol that places new models on a unified ability scale. The approach is applied to safety, capability, and truthfulness tasks, expanding coverage without saturation and remaining compatible with existing datasets.

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.

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