@inproceedings{wang-etal-2026-routing,
title = "Routing Before Looking: Query-Adaptive Evidence Acquisition for Long-form Video Understanding",
author = "Wang, Tianyue and
Wu, Xuying and
Ma, Yuxiang and
Liang, Ruiming and
Kang, Jiaxuan and
Hao, Yanchao and
Wei, Zheng and
Qu, Leigang and
Guo, Haiyun and
Wang, Jinqiao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1984/",
doi = "10.18653/2026.findings-emnlp.1984",
pages = "42290--42308",
ISBN = "979-8-89176-462-0",
abstract = "Long-form video understanding remains challenging for video agents due to the mismatch between query demands and evidence acquisition strategies. Although recent planning-before-perception methods outperform query-agnostic pipelines, they often rely on a single dominant strategy, either generation-based strategy or retrieval-based strategy, limiting their ability to handle diverse query demands. We propose **Route2Look**, a lightweight and model-agnostic framework for query-adaptive evidence acquisition in long-form video understanding. Route2Look operates in a Route-Look-Memorize loop with three tools: Global Browse for holistic context, Temporal Ground for explicit temporal cues, and Semantic Retrieve for semantic search. The core component is a routing policy that dynamically selects evidence acquisition tools based on the query. To build this policy, Route2Look adopts a two-stage design: first distilling the routing skill from differential contrastive analysis between generation-based and retrieval-based trajectories, and then applying the distilled skill with hard routing rules and continue-or-stop criteria during inference. Experiments on challenging long-video benchmarks show that Route2Look achieves state-of-the-art performance while maintaining strong frame efficiency across datasets and query types. Oracle routing analysis further reveals the potential of query-adaptive evidence acquisition for future long-form video understanding."
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<abstract>Long-form video understanding remains challenging for video agents due to the mismatch between query demands and evidence acquisition strategies. Although recent planning-before-perception methods outperform query-agnostic pipelines, they often rely on a single dominant strategy, either generation-based strategy or retrieval-based strategy, limiting their ability to handle diverse query demands. We propose **Route2Look**, a lightweight and model-agnostic framework for query-adaptive evidence acquisition in long-form video understanding. Route2Look operates in a Route-Look-Memorize loop with three tools: Global Browse for holistic context, Temporal Ground for explicit temporal cues, and Semantic Retrieve for semantic search. The core component is a routing policy that dynamically selects evidence acquisition tools based on the query. To build this policy, Route2Look adopts a two-stage design: first distilling the routing skill from differential contrastive analysis between generation-based and retrieval-based trajectories, and then applying the distilled skill with hard routing rules and continue-or-stop criteria during inference. Experiments on challenging long-video benchmarks show that Route2Look achieves state-of-the-art performance while maintaining strong frame efficiency across datasets and query types. Oracle routing analysis further reveals the potential of query-adaptive evidence acquisition for future long-form video understanding.</abstract>
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%0 Conference Proceedings
%T Routing Before Looking: Query-Adaptive Evidence Acquisition for Long-form Video Understanding
%A Wang, Tianyue
%A Wu, Xuying
%A Ma, Yuxiang
%A Liang, Ruiming
%A Kang, Jiaxuan
%A Hao, Yanchao
%A Wei, Zheng
%A Qu, Leigang
%A Guo, Haiyun
%A Wang, Jinqiao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-routing
%X Long-form video understanding remains challenging for video agents due to the mismatch between query demands and evidence acquisition strategies. Although recent planning-before-perception methods outperform query-agnostic pipelines, they often rely on a single dominant strategy, either generation-based strategy or retrieval-based strategy, limiting their ability to handle diverse query demands. We propose **Route2Look**, a lightweight and model-agnostic framework for query-adaptive evidence acquisition in long-form video understanding. Route2Look operates in a Route-Look-Memorize loop with three tools: Global Browse for holistic context, Temporal Ground for explicit temporal cues, and Semantic Retrieve for semantic search. The core component is a routing policy that dynamically selects evidence acquisition tools based on the query. To build this policy, Route2Look adopts a two-stage design: first distilling the routing skill from differential contrastive analysis between generation-based and retrieval-based trajectories, and then applying the distilled skill with hard routing rules and continue-or-stop criteria during inference. Experiments on challenging long-video benchmarks show that Route2Look achieves state-of-the-art performance while maintaining strong frame efficiency across datasets and query types. Oracle routing analysis further reveals the potential of query-adaptive evidence acquisition for future long-form video understanding.
%R 10.18653/2026.findings-emnlp.1984
%U https://aclanthology.org/2026.findings-emnlp.1984/
%U https://doi.org/10.18653/2026.findings-emnlp.1984
%P 42290-42308
Markdown (Informal)
[Routing Before Looking: Query-Adaptive Evidence Acquisition for Long-form Video Understanding](https://aclanthology.org/2026.findings-emnlp.1984/) (Wang et al., Findings 2026)
ACL
- Tianyue Wang, Xuying Wu, Yuxiang Ma, Ruiming Liang, Jiaxuan Kang, Yanchao Hao, Zheng Wei, Leigang Qu, Haiyun Guo, and Jinqiao Wang. 2026. Routing Before Looking: Query-Adaptive Evidence Acquisition for Long-form Video Understanding. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 42290–42308, Budapest, Hungary. Association for Computational Linguistics.