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Shanghai Artificial Intelligence Laboratory
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Pruning irrelevant visual tokens can boost medical reasoning performance by over 100%, transforming how VLMs approach sparse medical images.
A unified model that seamlessly integrates brain, vision, and language reveals new insights into multimodal neural processing and outperforms traditional methods.
Agents-K1 transforms how we extract and reason about scientific knowledge, achieving superior performance in multi-hop reasoning tasks compared to existing methods.
Current AI agents struggle to reliably rediscover scientific knowledge, with top performers averaging only 21.5 out of a possible score, revealing critical gaps in their research capabilities.
Achieving robust brain decoding across subjects without any retraining could revolutionize how we interpret neural signals in diverse populations.
