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

2026-08-23

AI Summary

论文提出TRACE方法，通过构建证据包来解决长视频理解中答案基于不完全观察的问题，提高了视频理解和答案准确性。

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 0.39x 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).

Problem

• long-video understanding
• evidence intervals
• frame decoding

Innovation

• Temporal Retrieval
• Anchored and Convergent Evidence
• Long-Horizon Video Understanding
• VES-Bench
• TRACE