OmniReasoner: Thinking with Long Audio-Video via Native Tool Use

2026-07-21 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

OmniReasoner is a novel tool-use post-training framework designed to enhance omnimodal Large Language Models' (LLMs) ability to reason with long audio-video content. Developed by Yidong Wang, Shuman Liu, Biao Liu, Yu Chen, Caorui Li, and others, it tackles the challenge of sparse, cross-modal, and costly high-fidelity evidence in extended media. The system first generates a low-cost global preview of the entire stream. When necessary, it then invokes a "zoom-in" tool, specifying a temporal interval for detailed visual and audio inspection before generating an answer. A key innovation, TimeAnchor, maintains the tool's temporal argument validity and consistency across varying sampling granularities. To facilitate training without expensive manual annotation, OmniReasoner employs a Temporal Augmented Data Engine that synthesizes tool-use trajectories through video editing. Experiments demonstrate improved answer accuracy and temporal grounding on omnimodal and video benchmarks, efficiently focusing high-fidelity computation.

Key takeaway

For AI Scientists developing LLMs for long-form multimedia analysis, OmniReasoner presents a robust strategy to manage sparse, cross-modal evidence efficiently. You should consider adopting a two-stage processing pipeline that combines a low-fidelity global preview with dynamic, high-fidelity "zoom-in" tool calls. This method, coupled with synthetic data generation for training, can significantly improve temporal grounding and answer accuracy while optimizing computational resource allocation for your models.

Key insights

OmniReasoner enables omnimodal LLMs to reason with long audio-video by dynamically zooming into relevant temporal segments.

Principles

• Sparse global preview guides targeted high-fidelity inspection.
• Temporal arguments must be consistent across sampling rates.
• Synthesize training data to avoid costly manual annotation.

Method

OmniReasoner uses supervised fine-tuning and reinforcement learning to teach LLMs to call a zoom-in tool after a global preview, guided by TimeAnchor for temporal consistency, and trained with synthesized trajectories.

In practice

• Implement a two-stage processing for long media.
• Design temporal anchoring for multi-granularity data.
• Automate data generation for tool-use training.

Topics

• Omnimodal LLMs
• Tool Use
• Audio-Video Reasoning
• Temporal Grounding
• Synthetic Data Generation
• Reinforcement Learning

Code references

• RockyChen0205/OmniReasoner

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.