High-stakes deployment of vision-language models (VLMs) requires selective prediction, where systems abstain when uncertain rather than risk costly errors. We investigate whether confidence-based abstention provides reliable control over error rates in video question answering, and whether that control remains robust under distribution shift. Using NExT-QA and Gemini 2.0 Flash, we establish two findings. First, confidence thresholding provides mechanistic control in-distribution. Sweeping threshold epsilon produces smooth risk-coverage tradeoffs, reducing error rates f
Cite
@article{arxiv.2601.00138,
title = {Explicit Abstention Knobs for Predictable Reliability in Video Question Answering},
author = {Jorge Ortiz},
journal= {arXiv preprint arXiv:2601.00138},
year = {2026}
}
Comments
Preprint. Diagnostic study of confidence-based abstention under evidence truncation