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Frontier MLLMs exhibit strong local perception but falter on long-horizon audio-visual reasoning, with a single benchmark exposing specific failure modes like identity drift and cross-modal misalignment.
Despite achieving 84.8% accuracy, multimodal models struggle with long-horizon reasoning in electrical circuits, revealing critical gaps in their understanding of physical conventions.
RL fine-tuning LMMs for tool use can collapse structural formats due to strong pretrained tool priors, but a surprisingly simple fix of targeted format rewards and frame-budget randomization can restore stability and boost performance.
Today's visual generation models are often evaluated on the wrong things, leading to inflated performance claims that mask critical failures in spatial reasoning, temporal consistency, and causal understanding.
