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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.
Occamy-1.0, a cost-efficient co-work model obtained by further training the post-trained Qwen3.6-35B-A3B checkpoint, is presented and placed at the low-cost knee of the observed cost--performance Pareto frontier.
Achieving a staggering 90.4% reduction in robot-motion error, Hydra-0 redefines how we model and control robotic actions across varied environments.
MLLMs struggle with video temporal-logical reasoning, showing a substantial performance gap compared to human capabilities, especially as complexity increases.
