Program-Guided Temporal Reasoning over Routed Video Evidence for TimeLogicQA

A temporal video question may turn on a brief transition, a repeated action, or the boundary between two events. Temporal Evidence Routing approaches these questions by first finding the observations needed to check the requested relation. It parses the events, options, and temporal condition, then supplies ordered frames for short clips or event-focused windows for longer videos. A multimodal model records event occurrences and their intervals. Temporal checks use these records to produce a candidate answer, which is combined with a base prediction through evidence and confidence checks. The system operates with fixed pretrained weights. On TimeLogicQA, it achieves 81.8 AvgAcc against 62.50 for direct Gemini prompting, a gain of 19.30 percentage points.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-04
DOI
https://doi.org/10.5281/zenodo.23953939
Primary Topic
Multimodal Machine Learning Applications
Type
preprint
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preprint

Program-Guided Temporal Reasoning over Routed Video Evidence for TimeLogicQA

Wenwu He, Yuxia Chen, Yu Zhang, Yuyang Sun et al.
Zenodo (CERN European Organization for Nuclear Research)
Multimodal Machine Learning Applications
preprint

Program-Guided Temporal Reasoning over Routed Video Evidence for TimeLogicQA

Wenwu He, Yuxia Chen, Yu Zhang, Yuyang Sun, Xiaogang Wang
preprint en

Abstract

A temporal video question may turn on a brief transition, a repeated action, or the boundary between two events. Temporal Evidence Routing approaches these questions by first finding the observations needed to check the requested relation. It parses the events, options, and temporal condition, then supplies ordered frames for short clips or event-focused windows for longer videos. A multimodal model records event occurrences and their intervals. Temporal checks use these records to produce a candidate answer, which is combined with a base prediction through evidence and confidence checks. The system operates with fixed pretrained weights. On TimeLogicQA, it achieves 81.8 AvgAcc against 62.50 for direct Gemini prompting, a gain of 19.30 percentage points.

Zenodo (CERN European Organization for Nuclear Research)
Southwest University (CN), University of Electronic Science and Technology of China (CN), Freedom House (US), Southeast University (CN)
Multimodal Machine Learning Applications
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