A Matched-Budget Audit Framework for Recaptioned Image-Text Supervision Distributions

arXiv:2610.00952 · cs.CV, cs.AI · Submitted 2026-10-01 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-10-01

Updated: 2026-10-01

Comments: initial commit

Code: https://github.com/BootsofLagrangian/audit-recap-t2i

License: http://creativecommons.org/licenses/by/4.0/

The gist: Recaptioned image-text corpora are now standard for text-to-image (T2I) training, with vision--language model (VLM) captioners replacing sparse alt-text by dense descriptions.

Terminology

Abstract

Recaptioned image-text corpora are now standard for text-to-image (T2I) training, with vision--language model (VLM) captioners replacing sparse alt-text by dense descriptions. A recaptioned corpus is a supervision distribution induced by a documented captioning policy (π), captioner (V c), and source corpus (C). Length-correlated proxies miss caption-register artifacts and downstream T2I benchmarks entangle the corpus with training choices, so this distribution is hard to audit at corpus scale. We introduce a reusable matched-budget audit framework for recaptioned supervision distributions D π,V c,C: at a fixed text budget of B = 64 it reports a five-axis profile spanning prompt-side coverage, image-conditioned faithfulness, and caption-surface health, with claimed controllable basic units (CBU) as the common claim unit. We instantiate the framework on seven paired comparisons over five public source corpora. Across the four cross-corpus pairs, the released surface raises supported CBU per caption by +3.39 to +6.36 under both Qwen and Gemma Judges, and on CC12M the same framework exposes a long-vs-dense frontier that is consistent across both judges and four budgets. We release the audited multi-source recap corpus (about 490M) together with the audit-artifact bundle.

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