agent/papers/items/2026-2607-02927-videosearcher-empowering-video-deep-research-with-multi-tool-agentic-reasoning-v.md

Paper: VideoSearcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning

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type: paper title: "VideoSearcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning" authors: Zhenkun Gao, Yicheng Bao, Jinlong Peng, Xueheng Li, Theo Huang, Bangwei Liu, Kunquan Li, Zhenye Gan, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.02927 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-07-03 updated_at: 2026-07-03 status: queued relevance: high topics:

• agent-evaluation
• rag
• reasoning
• tool-use methods:

benchmarks:

models:

datasets:

• cs.CV
• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 16 collection_queries: tool-use

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: tool-use
• inferred topics: agent-evaluation, rag, reasoning, tool-use
• arXiv categories: cs.CV, cs.AI
• collection score: 16

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2607.02927