Agentic Active Omni-Modal Perception for Multi-Hop Audio-Visual Reasoning
📰 ArXiv cs.AI
Learn to improve multi-hop audio-visual reasoning with Agentic Active Omni-Modal Perception using MOV-Bench, a new benchmark with 519 curated questions
Action Steps
- Build a multi-hop audio-visual reasoning model using Agentic Active Omni-Modal Perception
- Evaluate the model on MOV-Bench, a benchmark with 519 curated questions
- Configure the model to handle sparse and temporally dispersed evidence
- Test the model's ability to reason across both audio and visual streams
- Apply the model to real-world applications, such as audio-visual question answering
Who Needs to Know This
AI researchers and engineers working on multi-modal models can benefit from this work to improve their models' ability to reason across audio and visual streams
Key Insight
💡 Agentic Active Omni-Modal Perception can improve multi-hop audio-visual reasoning by actively selecting relevant evidence from both audio and visual streams
Share This
🔊👀 Improve multi-hop audio-visual reasoning with Agentic Active Omni-Modal Perception and MOV-Bench! #AI #MultiModal
Key Takeaways
Learn to improve multi-hop audio-visual reasoning with Agentic Active Omni-Modal Perception using MOV-Bench, a new benchmark with 519 curated questions
Full Article
Title: Agentic Active Omni-Modal Perception for Multi-Hop Audio-Visual Reasoning
Abstract:
arXiv:2605.28192v1 Announce Type: new Abstract: Multi-hop audio-visual reasoning remains challenging for Omni-LLMs, as relevant evidence is often sparse, temporally dispersed, and distributed across both audio and visual streams. Existing benchmarks provide limited investigation of this setting, typically involving only a limited number of modalities, relevant temporal segments, or reasoning steps. In this work, we introduce MOV-Bench, a benchmark containing 519 carefully curated questions that
Abstract:
arXiv:2605.28192v1 Announce Type: new Abstract: Multi-hop audio-visual reasoning remains challenging for Omni-LLMs, as relevant evidence is often sparse, temporally dispersed, and distributed across both audio and visual streams. Existing benchmarks provide limited investigation of this setting, typically involving only a limited number of modalities, relevant temporal segments, or reasoning steps. In this work, we introduce MOV-Bench, a benchmark containing 519 carefully curated questions that
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