TRACE

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

Pengyiang Liu^1,3,*, Junbo Niu^2,3,*, Xiaoyang Hu¹, Zhongyue Shi¹, Zitian Wang¹, Linjiang Huang¹, Si Liu^1,†

¹Beihang University  ²Peking University  ³Shanghai AI Laboratory

^*Equal contribution  ^†Corresponding author

EMNLP 2026 Main Conference

Project Page · VES-Bench on Hugging Face

Overview

A long-video answer is evidence-supported only when the frames decoded from the video cover every event the answer depends on. Existing evaluations score final-answer correctness or predicted evidence intervals, but the frames a method decodes before answering are rarely audited, so correct answers can still rest on incomplete observation.

We introduce VES-Bench, a 600-question benchmark of Temporal Ordering and Event Counting items over 348 public long videos. Each item carries a jointly necessary set of evidence intervals, letting us audit at three strictness levels whether a method's decoded frames cover every one of them.

We also propose TRACE, a training-free agent that grounds answers in raw visual clips, builds an evidence bundle round by round, and stops only when the answer stabilises as the bundle grows and a final pass over the same clips returns the same answer.

VES-Bench

VES-Bench is hosted on Hugging Face at buaaplay/VES-Bench.

hf download buaaplay/VES-Bench --repo-type dataset --local-dir ./VES-Bench

The release contains VES-Bench.parquet with the 600 audited questions and videos/ with the source long videos.

Method

Coming soon.

Citation

@inproceedings{liu2026trace,
  title     = {{TRACE}: Temporal Retrieval with Anchored and Convergent Evidence for Long-Horizon Video Understanding},
  author    = {Liu, Pengyiang and Niu, Junbo and Hu, Xiaoyang and Shi, Zhongyue and Wang, Zitian and Huang, Linjiang and Liu, Si},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
  year      = {2026}
}