CAViAR: Critic-Augmented Video Agentic Reasoning

• URL: http://arxiv.org/abs/2509.07680v1
• Date: Tue, 09 Sep 2025 17:59:39 GMT
• Title: CAViAR: Critic-Augmented Video Agentic Reasoning
• Authors: Sachit Menon, Ahmet Iscen, Arsha Nagrani, Tobias Weyand, Carl Vondrick, Cordelia Schmid,
• Abstract summary: We ask: can perception capabilities be leveraged to perform more complex video reasoning?<n>We develop a large language model agent given access to video modules as subagents or tools.<n>We show that the combination of our agent and critic achieve strong performance on datasets.
• Score: 90.48729440775223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video understanding has seen significant progress in recent years, with models' performance on perception from short clips continuing to rise. Yet, multiple recent benchmarks, such as LVBench, Neptune, and ActivityNet-RTL, show performance wanes for tasks requiring complex reasoning on videos as queries grow more complex and videos grow longer. In this work, we ask: can existing perception capabilities be leveraged to successfully perform more complex video reasoning? In particular, we develop a large language model agent given access to video modules as subagents or tools. Rather than following a fixed procedure to solve queries as in previous work such as Visual Programming, ViperGPT, and MoReVQA, the agent uses the results of each call to a module to determine subsequent steps. Inspired by work in the textual reasoning domain, we introduce a critic to distinguish between instances of successful and unsuccessful sequences from the agent. We show that the combination of our agent and critic achieve strong performance on the previously-mentioned datasets.

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