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 require multi-hop reasoning over temporally dispersed audio-visual evidence. Evaluations on MOV-Benc
The development of sophisticated Omni-LLMs highlights the current limitations in multi-modal reasoning, driving the need for benchmarks that address complex, multi-hop evidence analysis.
This benchmark signifies a crucial step in advancing AI agents' ability to reason across diverse and temporally asynchronous data, which is essential for developing truly autonomous and intelligent systems.
The introduction of MOV-Bench changes how Omni-LLMs will be evaluated and developed, shifting focus towards more complex, human-like reasoning tasks involving distributed audio-visual information.
- · AI research labs
- · AI agent developers
- · Multi-modal AI companies
- · AI models without advanced reasoning capabilities
- · Companies relying solely on single-modal AI solutions
Improved performance of AI agents in tasks requiring complex multi-modal reasoning will become evident.
This will accelerate the deployment of more capable AI agents in various industries, from customer service to operational management.
The enhanced reasoning capabilities of AI agents could lead to new forms of automation, collapsing workflows not previously thought possible.
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Read at arXiv cs.AI