Year: 2026
Type: conference-paper
Abstract: Long audio-video reasoning is difficult for omni-modal LLMs because the decisive evidence is often sparse, cross-modal, and too expensive to preserve with uniformly high-fidelity inputs.We introduce OmniReasoner, a tooluse post-training framework for Thinking with Long Audio-Video: omni-modal LLMs learn, via supervised fine-tuning and reinforcement learning, to decide whether and where to call a zoom-in tool before answering.OmniReasoner first builds a low-cost global preview of the full stream and then, when needed, calls the zoom-in tool with a requested temporal interval for higher-fidelity visual and audio inspection before answering.Because the model observes different sampling granularities before and after this call-a sparse global preview and a denser local clip-we introduce TimeAnchor, which keeps the tool's temporal argument valid and round-trip-consistent across these granularities, rather than tied to frame indices from a particular sampling rate.To make this tooluse behavior trainable without expensive manual interval annotation, we build a Temporal Augmented Data Engine that synthesizes tooluse post-training trajectories by video editing and composition.Experiments across omnimodal and video benchmarks show that Om-niReasoner improves both answer accuracy and temporal grounding while concentrating high-fidelity computation on informative regions.Code is available at https://github. com/RockyChen0205/OmniReasoner.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Award: G1362912382
OpenAlex ID: W7170095331