Watch, Remember, Reason: Human-View Video Understanding with MLLMs

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The survey "Watch, Remember, Reason: Human-View Video Understanding with MLLMs" presents a comprehensive analysis of multimodal large language models (MLLMs) for video understanding, moving beyond short clips to long, knowledge-intensive scenarios. It introduces a "human-view" perspective, organizing MLLM capabilities into "watching," "remembering," and "reasoning." The work formulates video understanding systems by their perceptual representations, memory states, reasoning traces, and predictions, identifying challenges in spatio-temporal perception, efficient long-video processing, memory modeling, streaming understanding, and faithful reasoning. It reviews representative methods, application domains like egocentric, sports, instructional, medical, and narrative videos, and covers training datasets (e.g., MTVR-CoT-72K, VideoMarathon) and evaluation benchmarks across various task types and capabilities.

Key takeaway

For AI Architects designing scalable video understanding systems, you should prioritize integrating structured multi-level memory and agentic reasoning components. Focus on developing models that can selectively acquire and explicitly ground evidence, balancing computational efficiency with reasoning faithfulness. Your systems should support verifiable outputs, connecting conclusions to specific spatio-temporal cues to enhance interpretability and reduce hallucination in long-form video analysis.

Key insights

MLLM video understanding benefits from a human-like "watch, remember, reason" functional decomposition.

Principles

• Long video comprehension requires selective perception and context retention.
• Reasoning must be grounded in explicit spatio-temporal evidence.
• Memory mechanisms are crucial for handling long-range dependencies.

Method

The proposed formulation characterizes video understanding systems by their perceptual representations, memory states, reasoning traces, and final predictions, mapping to watching, remembering, and reasoning modules.

In practice

• Implement agentic approaches for complex, multi-step video reasoning.
• Design structured multi-level memory with evidence pointers for hour-scale videos.
• Utilize verifiable RL or preference optimization for grounded reasoning.

Topics

• Multimodal Large Language Models
• Video Understanding
• Video Reasoning
• Long-form Video Processing
• Spatio-temporal Grounding
• Memory-augmented AI

Code references

• marinero4972/Awesome-HumanView-VideoUnderstanding

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Architect

Related on AIssential

• Watch, Remember, Reason: Human-View Video Understanding with MLLMs — The human-view perspective unifies MLLM video understanding through watching, remembering, and reasoning capabilities.
• Flat-Pack Bench: Evaluating Spatio-Temporal Understanding in Large Vision-Language Models through Furniture Assembly — Current LVLMs significantly struggle with fine-grained spatio-temporal reasoning and object interaction in complex video scenarios.
• LongSpace: Exploring Long-Horizon Spatial Memory from Perception to Recall in Video — Long-horizon spatial memory in MLLMs requires integrating 3D structural cues and hierarchical, question-guided memory retrieval.
• MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism — MemDreamer decouples perception and reasoning for long video understanding using hierarchical graph memory and agentic retrieval, achieving SOTA.
• Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding — Long video understanding agents should actively seek query-relevant evidence through iterative, goal-directed observation.
• PyraVid: Hierarchical Multimodal Memory for Long-Horizon Video Reasoning — PyraVid uses hierarchical multimodal memory for long-horizon video reasoning, inspired by cognitive science.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.