OmniReasoner framework enables LLMs to analyze long audio-video content

Researchers have introduced OmniReasoner, a novel framework designed to enhance the capabilities of omnimodal large language models (LLMs) in processing long audio-video content. This framework enables LLMs to intelligently decide when and where to utilize a "zoom-in" tool for higher-fidelity inspection of specific temporal intervals within the audio-video stream. To address the challenge of training this tool-use behavior without extensive manual annotation, a Temporal Augmented Data Engine was developed to synthesize training trajectories. Experiments on various benchmarks demonstrate that OmniReasoner improves answer accuracy and temporal grounding while optimizing computational resources by focusing high-fidelity processing on relevant segments. AI

IMPACT Enhances LLM capabilities for analyzing complex, long-form audio-video data.

RANK_REASON The cluster contains a research paper detailing a new framework for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• OmniReasoner
• ScienceCast
• Temporal Augmented Data Engine
• TimeAnchor

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