OmniReasoner: Thinking with Long Audio-Video via Native Tool Use
This paper presents a new approach called OmniReasoner that helps models understand long audio and video content better by allowing them to focus on important parts when needed.
This video presentation explains the key concepts from the paper in plain language.
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- 1 OmniReasoner improves how models reason about long audio-video inputs.
- 2 It uses a tool to zoom in on specific segments for better analysis.
- 3 The method balances the need for broad coverage with the need for detailed information.
Introduction
Long audio-video reasoning requires omni-modal LLMs to connect sparse evidence across modalities. The challenge lies in preserving high-fidelity evidence without overwhelming computational resources. Recent methods have shown that selective evidence acquisition can enhance long-video reasoning.
Zoom-in
OmniReasoner utilizes a zoom-in tool to focus on specific audio-video segments when necessary. This allows the model to gather high-fidelity evidence for answering questions, as demonstrated in an example where the model counts fish in a bowl based on narration.
Agentic Tool Use
OmniReasoner emphasizes adaptive inspection of long videos through agentic tool use, allowing the model to decide when and where to retrieve additional evidence for higher-fidelity audio-video analysis.
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OmniReasoner Overview
OmniReasoner processes long audio-video inputs by first creating a global observation and then making decisions on whether to answer or zoom in for more detailed evidence. This method balances temporal coverage with perceptual fidelity.
Long Omni Video Reasoning
Long-form video understanding combines various reasoning aspects, including temporal localization and multi-step reasoning. The challenge is heightened for audio-visual inputs, where decisive evidence may be distributed across both modalities.
Figures Explained
The paper’s visual material highlights the workflow and the main system components.
- Figure 1: An OmniReasoner inference example showing the process of identifying relevant narration and requesting a zoom-in interval.. Illustrates how OmniReasoner utilizes a zoom-in tool to enhance answer accuracy by focusing on specific segments of audio-video content.
- Figure 2: Overview of the OmniReasoner framework and its decision-making process.. Demonstrates the model’s approach to balancing global observation and local evidence retrieval for effective reasoning.
Limitations and Cautions
A useful limitation and caution is that this article summarizes the available paper text and extracted evidence; readers should consult the source paper before treating any interpretation as definitive.
The paper’s conclusions may depend on its source selection, definitions, assumptions, and the scope of its analysis, so follow-up reading is important.
Frequently Asked Questions
This paper presents a new approach called OmniReasoner that helps models understand long audio and video content better by allowing them to focus on important parts when needed.
Long audio-video reasoning requires omni-modal LLMs to connect sparse evidence across modalities. The challenge lies in preserving high-fidelity evidence without overwhelming computational resources. Recent methods have shown that selective evidence acquisition can.
OmniReasoner improves how models reason about long audio-video inputs. It uses a tool to zoom in on specific segments for better analysis. The method balances the need for broad coverage with the need for detailed information.
Yes. PDFDigest can turn this paper into a structured explanation, key takeaways, visual summaries, and a narrated video when available.