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arXiv · 2605.23826

Decomposing Queries into Tool Calls for Long-Video Keyframe Retrieval

Michal Shlapentokh-Rothman·Prachi Garg·Yu-Xiong Wang·Derek Hoiem
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Abstract

Keyframe selection is a direct way to provide verifiable visual evidence for long-video question answering (QA). Queries differ in what they require, and finding the right frames depends on knowing what to look for. Existing keyframe selectors either score every frame against a single query, or decompose the query into a fixed schema evaluated by a single visual tool. We propose ToolMerge, a keyframe retrieval method based on decomposition and merging: an Large Language Model (LLM) based planner decomposes the query into tool calls and specifies how their per-tool rankings are merged using boolean operators. To evaluate retrieval directly, we construct Molmo-2 Moments (M2M), a benchmark in which every question is anchored to a specific time interval by construction. Across QA, question retrieval, and caption retrieval, ToolMerge is competitive with prior keyframe selectors, most notably on caption retrieval, outperforming other methods by 5%. Code and data can be found at https://github.com/michalsr/ToolMerge .

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Michal Shlapentokh-Rothman, Prachi Garg, Yu-Xiong Wang, Derek Hoiem. 2026-07-06. Decomposing Queries into Tool Calls for Long-Video Keyframe Retrieval. https://arxiv.org/abs/2605.23826

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