Progressive Video Condensation with MLLM Agent for Long-form Video Understanding

• URL: http://arxiv.org/abs/2604.02891v1
• Date: Fri, 03 Apr 2026 09:00:38 GMT
• Title: Progressive Video Condensation with MLLM Agent for Long-form Video Understanding
• Abstract summary: We propose ProVCA, a progressive video condensation agent that iteratively locates key video frames at multiple granularities.<n>ProVCA state-of-the-art zero-shot accuracies of 69.3% on Ego, 80.5% on NExTQA, and 77.7% on IntentQA.
• Score: 22.571711953808045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding long videos requires extracting query-relevant information from long sequences under tight compute budgets. Existing text-then-LLM pipelines lose fine-grained visual cues, while video-based multimodal large language models (MLLMs) can keep visual details but are too frame-hungry and computationally expensive. In this work, we aim to harness MLLMs for efficient video understanding. We propose ProVCA, a progressive video condensation agent that iteratively locates key video frames at multiple granularities. ProVCA first adopts a segment localization module to identify the video segment relevant to the query, then a snippet selection module to select important snippets based on similarity, and finally a keyframe refinement module to pinpoint specific keyframes in those snippets. By progressively narrowing the scope from coarse segments to fine frames, ProVCA identifies a small set of keyframes for MLLM-based reasoning. ProVCA achieves state-of-the-art zero-shot accuracies of 69.3\% on EgoSchema, 80.5\% on NExT-QA, and 77.7\% on IntentQA, while using fewer frames than previous training-free methods.

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