Research Models & Releases·arXiv cs.CL·1d ago

VideoMM reduces video MLLM token overhead with adaptive filtering

VideoMM addresses a critical scaling bottleneck in video understanding MLLMs: the explosion of visual tokens that overwhelms context windows and multiplies inference costs. Rather than relying on external token-reduction models, which trade off semantic fidelity against efficiency, the work proposes a two-tier approach that applies coarse filtering first, then fine-grained analysis only on relevant regions. This insight that preliminary filtering doesn't require full visual detail reshapes how practitioners can build practical long-form video systems without sacrificing either speed or comprehension. The technique matters for anyone deploying video MLLMs at scale.

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Explainer

The key insight is that coarse filtering doesn't require visual fidelity. VideoMM shows that preliminary token reduction can work without external models, which means practitioners can build the efficiency layer directly into the MLLM rather than bolting on a separate compression stage.

This connects to the broader pattern across recent research of moving safety and efficiency checks into the model itself rather than relying on external pipelines. The Cascade unlearning framework (September) similarly embeds suppression across multiple internal layers rather than applying a single deletion pass. Both papers reflect a maturation in the field: instead of treating efficiency or safety as a post-hoc wrapper, researchers are redesigning the model's internal pathways. VideoMM applies that same principle to inference cost.

If VideoMM's macro-micro approach maintains quality parity with full-token baselines on standard video QA benchmarks (MSVD, ActivityNet-QA) while reducing tokens by 70%+ and latency by 50%+, the technique has crossed from theoretical to practically deployable. Watch whether major video MLLM implementations (LLaVA-Video, Qwen-VL successors) adopt the two-tier filtering pattern within the next 12 months.

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Cascade: Hierarchical Recoverability Control for Large Language Model Unlearning · arXiv cs.CL

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MentionsVideoMM · Multimodal Large Language Models · MLLMs

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