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Current MLLM benchmarks are missing the forest for the trees: Agentic-MME reveals that strong final-answer accuracy masks surprisingly poor tool use and planning in complex multimodal tasks.
Forget hand-crafted membership inference attacks - AutoMIA learns better strategies automatically, adapting to different models and eliminating the need for manual feature engineering.
SAM's impressive zero-shot segmentation abilities don't directly translate to medical imaging, but this new fine-tuning approach unlocks its potential for accurate nuclei instance segmentation with minimal added parameters.
LongCat-Next shatters the language-centric paradigm by unifying text, vision, and audio into a single autoregressive model with minimal modality-specific design, finally reconciling understanding and generation in discrete vision modeling.
VLMs' hallucinations aren't just errors, but traceable pathologies in their "cognitive trajectory," diagnosable via geometric anomalies in a learned state space.
