arXiv:2607.02588v1 Announce Type: cross Abstract: Multimodal large language models excel on short clips but struggle on hour-long videos in an online setting, where frames are processed incrementally under limited memory. Existing online methods either retain compact visual representations that lack semantic structure, or build higher-level memory stores organized around temporal proximity rather than explicit causal links, leaving multi-hop narrative reasoning to be reconstructed by the LLM at every query. We bridge this gap with \textsc{Homer}, a Hierarchical Online Memory Exploration and Re
The increasing demand for LLMs to process longer, more complex data streams necessitates advancements in memory and reasoning architectures for video understanding.
This research addresses a critical limitation of current multimodal LLMs, enabling them to handle real-world, long-form video content more effectively, which is crucial for numerous applications.
The development of hierarchical memory and agentic reasoning will allow AI systems to perform multi-hop narrative reasoning on extended video sequences, moving beyond short clip analysis.
- · AI developers
- · Video analytics companies
- · Content moderation platforms
- · Surveillance technology providers
- · Companies reliant on short-form video only AI
- · Legacy video processing solutions
Multimodal LLMs gain significantly improved capabilities in understanding complex, long-duration video content.
This advancement enables new applications in areas like long-form content summarization, autonomous system perception, and complex event detection.
Improved video understanding could lead to more sophisticated AI agents capable of continuous, real-time contextual awareness across extended periods.
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Read at arXiv cs.AI