arXiv Machine Learning
Oct 16

Sharp and Stable Label-Free Calibration Certificates from Logical Coherence

The paper presents a framework for deriving sharp, label‑free calibration certificates for query‑conditioned probabilistic predictors. It establishes minimax lower bounds on calibration residuals under various betting restrictions, shows that these bounds can be discontinuous and that Lipschitz stakes yield stable, sharp bounds. The authors also demonstrate practical algorithms—projected online gradient ascent and anytime‑valid betting—for generating fixed‑horizon and anytime‑valid certificates, and validate the approach on a preregistered study of four predictors across two datasets.

By Takahiro Kato (NP-hard, Inc.)
arXiv AI
Sep 24

Ask Which, Not How Good: Sizing Benchmarks Scored by an LLM

The study analyzes 373,019 judgments from LLM‑scored benchmarks, decomposing variance into system, item, judge, and interaction components via generalizability theory. It finds that with a single judge, generalizability converges to a ceiling determined by the system‑by‑judge variance, which is substantially lower in pairwise preference settings, allowing one judge to suffice. The research also reveals significant biases in presentation order and highlights that many published win‑rate claims fall below the measured floor of the benchmarks.

By Atul Anand