arXiv:2606.07643v1 Announce Type: cross Abstract: Recent advances in Omni-Multimodal Large Language Models (Omni-MLLMs) have enabled strong integration of vision, audio, and language. However, their audio-visual intelligence (AVI) remains insufficiently evaluated due to the lack of systematic and comprehensive benchmarks. We introduce AVI-Bench, a cognitively inspired benchmark that evaluates Omni-MLLMs across three stages, perception, understanding, and reasoning, through cross-modal tasks requiring joint audio-visual interpretation. This design enables fine-grained diagnosis of model capabil
The proliferation of Omni-MLLMs necessitates robust evaluation frameworks to understand their true capabilities and limitations, especially as they integrate more modalities.
A more systematic benchmark for audio-visual intelligence allows for better development and deployment of advanced AI systems, pushing towards more human-like perception and understanding.
The introduction of AVI-Bench provides a standardized, cognitively-inspired method for diagnosing Omni-MLLM performance, moving beyond ad-hoc evaluations to a structured assessment across perception, understanding, and reasoning.
- · Omni-MLLM developers
- · AI researchers
- · AI evaluation firms
- · Robotics
- · Undifferentiated multimodal AI models
Refined benchmarks will accelerate the development of more capable and reliable Omni-MLLMs, particularly in audio-visual tasks.
Improved multimodal AI could lead to more nuanced human-computer interaction and advanced autonomous systems.
The enhanced diagnostic capabilities offered by such benchmarks could guide future AI safety and alignment research by revealing systematic model failures.
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Read at arXiv cs.AI