arXiv (All)
open access
Rank Confidence Sequences:Anytime-valid Leaderboards
Leaderboards rank models by their average scores on benchmark items, and they are consulted repeatedly while the evaluation is still running. Existing confidence intervals for a model's rank control their error rate only if they are computed once, after a number of items chosen in advance. If they a
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Statistical Gems in AI: Toward Reliable and Efficient Intelligence#263689
- Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees#1117197
- Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf#985224
- Statistical Robustness and Follow-up Completeness of Survival Outcomes in Phase III Breast Cancer Trials#669766
- CRAMER: Control via Request-Aware Masking for Editing Recommenders#1084336
Record · ID 1116542
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive