{
  "id": 16131,
  "url": "https://arxiv.org/abs/2608.01336v1",
  "title": "Asleep at the Wheel: JEPA's Limitations in Evaluating Novel Driving Data",
  "summary": "Modern autonomous-driving fleets record far more video than human reviewers can inspect. This motivates the need for an automatic clip triage mechanism, to surface rare and review-worthy clips, so that driving models can be fine-tuned to better handle unideal circumstances. We test a label-free approach that scores clips by the prediction-error \"novelty\" of a self-supervised joint-embedding predictive architecture (JEPA); a frozen V-JEPA video encoder is paired with a lightweight predictor head",
  "authors": "Advait Pavuluri, Shamik Karkhanis, Uzma Mushtaque",
  "category": "research",
  "topics": null,
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-02T15:57:40.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/16131",
  "original_url": "https://arxiv.org/abs/2608.01336v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}