Light-Omni: Reflex over Reasoning in Agentic Video Understanding with Long-Term Memory
Light-Omni is a multimodal agent framework for reflexive video understanding using dual contextual states, achieving 12.1x speedup and 2.6x GPU memory efficiency over M3-Agent, and serving as a memory system for MLLMs.
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[Submitted on 6 Jul 2026]
Title:Light-Omni: Reflex over Reasoning in Agentic Video Understanding with Long-Term Memory
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Abstract:Agentic video understanding equips models with long-term memory to autonomously process and respond to continuous, long-horizon multimodal streams. However, advanced video agents often rely on ``detective-style'' iterative reasoning for action control (e.g., $\mathtt{search}$) and evidence aggregation, incurring prohibitive costs and latency. We argue that such heavy reasoning primarily compensates for the lack of global context and semantic misalignment in retrieval. This paper introduces Light-Omni, a multimodal agent framework for reflexive and lightweight video understanding. It achieves this through dual contextual states that instantly build the required context in a single forward pass. First, we maintain a global state, a finite-sized multimodal script continuously consolidated from episodic memory, serving as the global context for Light-Omni. Through hierarchical merging, it preserves recent details while summarizing past events. Second, conditioned on this global context, we generate a parametric latent state that directly drives autonomous actions and produces retrieval embeddings, with minimal latency. Benefiting from this coupled design, Light-Omni achieves semantically aligned retrieval and reflexive responses while avoiding iterative reasoning. Extensive experiments validate the effectiveness of Light-Omni across multiple video benchmarks. Notably, it outperforms M3-Agent with an average 2.4% accuracy gain, a 12.1$\times$ speedup, and a 2.6$\times$ improvement in GPU memory efficiency. Furthermore, it serves as a memory system to enhance both the performance and efficiency of existing MLLMs. Project page: this https URL.
Comments: Project Page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.05511 [cs.CV]
(or arXiv:2607.05511v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.05511
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chang Nie [view email] [v1] Mon, 6 Jul 2026 18:00:06 UTC (5,594 KB)
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