Native Active Perception as Reasoning for Omni-Modal Understanding

2026-06-18 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, long

Summary

OmniAgent is introduced as the first native omni-modal agent for long video understanding, addressing the computational cost of "watch-it-all" passive models. It formulates video understanding as a Partially Observable Markov Decision Process (POMDP) using an iterative Observation-Thought-Action cycle, distilling audio-visual cues into persistent textual memory to decouple reasoning complexity from raw video duration. The framework employs a two-stage optimization: Agentic Supervised Fine-Tuning with best-of-N trajectory synthesis and dual-stage quality control, followed by Agentic Reinforcement Learning with TAURA, which uses turn-level entropy for credit assignment. OmniAgent achieves state-of-the-art performance among open-source models across ten benchmarks, including LVBench (50.5%) where its 7B agent outperforms the 10x larger Qwen2.5-VL-72B (47.3%) with 73% fewer frames, and demonstrates positive test-time scaling. It also shows lower wall-clock latency (66.8s vs. 75.1s) on LVBench, requiring only 1 A100 GPU compared to 4.

Key takeaway

For Machine Learning Engineers developing scalable long-form video understanding solutions, OmniAgent presents a compelling alternative to passive models. You should investigate its active perception framework to significantly reduce computational overhead and improve accuracy, especially for hour-long content. Its 7B model's superior performance over 72B alternatives, coupled with single A100 GPU requirements, suggests a highly efficient deployment strategy for your projects.

Key insights

OmniAgent uses active perception and persistent textual memory to decouple video understanding reasoning from raw video duration.

Principles

• Formulate video understanding as a POMDP.
• Employ iterative Observation-Thought-Action cycles.
• Entropy indicates reasoning criticality in multi-turn agents.

Method

OmniAgent's two-stage optimization involves Agentic SFT (best-of-N trajectory synthesis, dual-stage quality control) to bootstrap capabilities, then Agentic RL with TAURA for entropy-steered credit assignment.

In practice

• Distill high-dimensional percepts into textual memory.
• Use dual-stage quality control for SFT data.
• Apply turn-level entropy to steer RL credit assignment.

Topics

• OmniAgent
• Active Perception
• Video Understanding
• Reinforcement Learning
• Large Language Models
• POMDP
• Multi-modal AI

Code references

• harryhsing/OmniAgent

Best for: Research Scientist, Computer Vision Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

Related on AIssential

• Native Active Perception as Reasoning for Omni-Modal Understanding — OmniAgent employs active, iterative perception and reasoning to efficiently understand long videos, decoupling computational cost from video duration.
• VisualThink-VLA: Visual Intermediate Reasoning for Effective and Low-Latency Vision-Language-Action Policies — Visual intermediate reasoning, not textual chain-of-thought, enables effective and low-latency vision-language-action policies.
• 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.
• ESI-Bench: Towards Embodied Spatial Intelligence that Closes the Perception-Action Loop — Embodied spatial intelligence requires active perception-action loops, where models currently exhibit "action blindness" and metacognitive gaps.
• OmniPro: A Comprehensive Benchmark for Omni-Proactive Streaming Video Understanding — OmniPro is a new benchmark for evaluating omni-modal LLMs in proactive streaming video understanding.
• Watch, Remember, Reason: Human-View Video Understanding with MLLMs — MLLM video understanding benefits from a human-like "watch, remember, reason" functional decomposition.

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