AdaptToken: Entropy-based Adaptive Token Selection for MLLM Long Video Understanding

• URL: http://arxiv.org/abs/2603.28696v1
• Date: Mon, 30 Mar 2026 17:14:15 GMT
• Title: AdaptToken: Entropy-based Adaptive Token Selection for MLLM Long Video Understanding
• Abstract summary: AdaptToken is a training-free framework that turns an MLLM's self-uncertainty into a global control signal for long-video token selection.<n>It consistently improves accuracy (e.g., +6.7 on average over Qwen2.5-VL 7B) and continues to benefit from extremely long inputs (up to 10K frames)<n>It reduces inference time by about half with comparable performance.
• Score: 81.07348307304547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long video understanding remains challenging for Multi-modal Large Language Models (MLLMs) due to high memory costs and context-length limits. Prior approaches mitigate this by scoring and selecting frames/tokens within short clips, but they lack a principled mechanism to (i) compare relevance across distant video clips and (ii) stop processing once sufficient evidence has been gathered. We propose AdaptToken, a training-free framework that turns an MLLM's self-uncertainty into a global control signal for long-video token selection. AdaptToken splits a video into groups, extracts cross-modal attention to rank tokens within each group, and uses the model's response entropy to estimate each group's prompt relevance. This entropy signal enables a global token budget allocation across groups and further supports early stopping (AdaptToken-Lite), skipping the remaining groups when the model becomes sufficiently certain. Across four long-video benchmarks (VideoMME, LongVideoBench, LVBench, and MLVU) and multiple base MLLMs (7B-72B), AdaptToken consistently improves accuracy (e.g., +6.7 on average over Qwen2.5-VL 7B) and continues to benefit from extremely long inputs (up to 10K frames), while AdaptToken-Lite reduces inference time by about half with comparable performance. Project page: https://haozheqi.github.io/adapt-token

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