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    <abstract>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. Under a same-backbone audit, TRACE answers 50.7% of questions correctly with at least two decoded frames inside every evidence interval, at 98.7 frames per question: over 10 points above uniform decoding at 128 frames (40.2%), and within 2.6 points of uniform decoding at 256 frames at 0.39× its frame cost, while reaching the highest answer accuracy in the audit (63.5%). TRACE also stays competitive on Video-MME (86.1), LVBench (75.6), and LongVideoBench (75.1).</abstract>
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%0 Conference Proceedings
%T TRACE: Temporal Retrieval with Anchored and Convergent Evidence for Long-Horizon Video Understanding
%A Liu, Pengyiang
%A Niu, Junbo
%A Hu, Xiaoyang
%A Shi, Zhongyue
%A Wang, Zitian
%A Huang, Linjiang
%A Liu, Si
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-trace-temporal
%X 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. Under a same-backbone audit, TRACE answers 50.7% of questions correctly with at least two decoded frames inside every evidence interval, at 98.7 frames per question: over 10 points above uniform decoding at 128 frames (40.2%), and within 2.6 points of uniform decoding at 256 frames at 0.39× its frame cost, while reaching the highest answer accuracy in the audit (63.5%). TRACE also stays competitive on Video-MME (86.1), LVBench (75.6), and LongVideoBench (75.1).
%U https://aclanthology.org/2026.emnlp-main.2626/
%P 59693-59708

Markdown (Informal)

[TRACE: Temporal Retrieval with Anchored and Convergent Evidence for Long-Horizon Video Understanding](https://aclanthology.org/2026.emnlp-main.2626/) (Liu et al., EMNLP 2026)

• TRACE: Temporal Retrieval with Anchored and Convergent Evidence for Long-Horizon Video Understanding (Liu et al., EMNLP 2026)

ACL

• Pengyiang Liu, Junbo Niu, Xiaoyang Hu, Zhongyue Shi, Zitian Wang, Linjiang Huang, and Si Liu. 2026. TRACE: Temporal Retrieval with Anchored and Convergent Evidence for Long-Horizon Video Understanding. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 59693–59708, Budapest, Hungary. Association for Computational Linguistics.