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A unified multimodal agent, OneSearch-VL, achieves state-of-the-art performance on diverse visual research tasks by explicitly modeling and preserving interdependencies within a Visually Grounded Evidence Graph.
CED reveals that VLMs can be trained to prioritize evidence-based reasoning over language shortcuts, leading to more reliable visual understanding.
LLaVA-OV-2's codec-stream tokenization lets it crush existing video-language models, especially in tasks requiring fine-grained temporal understanding of high-frequency motion.
Language models are increasingly doing their real work in the "invisible" latent space, not the tokens we see.
GRPO's struggle with exploration and difficulty adaptation in LLM reasoning stems from a previously unnoticed symmetry in its advantage estimation, which can be overcome by asymmetrically weighting correct vs. incorrect trajectories.
