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arXiv Computation and Language By Pengyiang Liu, Junbo Niu, Xiaoyang Hu, Zhongyue Shi, Zitian Wang, Linjiang Huang, Si Liu

TRACE: Temporal Retrieval with Anchored and Convergent Evidence for Long-Horizon Video Understanding

Read the original on arXiv Computation and Language →

The paper introduces TRACE, a training‑free agent for long‑video understanding that grounds answers in raw visual clips and builds an evidence bundle until the answer stabilises. It also presents VES‑Bench, a 600‑question benchmark over 348 public long videos that audits whether decoded frames cover all necessary evidence intervals at three strictness levels. TRACE achieves high accuracy on VES‑Bench (63.5% audit accuracy) and remains competitive on other video‑understanding benchmarks while using far fewer frames than uniform decoding.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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