arXiv:2607. 11981v1 Announce Type: cross Abstract: Aggregate reliability estimates can obscure heterogeneity in measurement-design burden across response conditions, so a single G- or D-study may mischaracterize a design's adequacy for particular strata.
By Yi Gui
arXiv:2603. 00077v3 Announce Type: replace-cross Abstract: Rubric-based LLM judges have become indispensable for evaluating and optimizing systems on non-verifiable tasks, where success cannot be reduced to exact programmatic checks.
By Delip Rao, Chris Callison-Burch
arXiv:2608. 00794v2 Announce Type: replace Abstract: Agentic AI evaluation pipelines produce benchmark scores that justify deployment decisions, safety certifications, and regulatory compliance claims.
By William Caban
Complexity measured from generated code is failure-dependent: a difficult prompt can yield a short failing program and be assigned low output complexity. We introduce a six-dimension prompt-side struc...
arXiv:2608. 08822v1 Announce Type: new Abstract: Cognitive decision-making research depends on diverse scenarios with carefully controlled complexity, yet manual production is slow, inconsistent, and biased.
By Abdalla Doleh, Toni Somers, Ratna Babu Chinnam
The paper introduces a six‑dimension prompt‑side structural‑complexity index to assess code‑generation reliability before a model generates output. Using 5,000 Python prompts and 21 large language models, the authors find that pass rates exhibit a non‑monotonic breakpoint around a composite score of 13.75, with task‑type and construction‑frame adjustments shifting this threshold. The study also reports high inter‑rater reliability (ICC = 0.872) and demonstrates that the index can predict failure likelihood without relying on output correctness.
By Michael Hernandez, Tian Zhao