Test-Time Coverage: Test-Conditioned Data Curation for Deployment-Aware Learning
Deployed AI systems are often trained from broad candidate data pools, necessitating data curation towards the deployment test distribution. However, standard data curation methods score training-side criteria rather than directly optimizing deployment match. We introduce TTCov (Test-Time Coverage), a data-level test-conditioned curation method that uses test-side information before training instead of updating model weights at inference. TTCov decomposes deployment-conditioned curation into coverage and distribution. To represent coverage, it builds a task Atlas, a collection of LLM-based atomic propositions (APs) describing deployment-relevant concepts, seeded from open task knowledge a...
| Authors | Nadine Chang, Maying Shen, Shizhe Diao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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