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The University of Tokyo, NII LLMC, National Institute of Informatics
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Multimodal LLMs encode chart information but fail to route it effectively for predictions, revealing a critical gap in scientific claim verification.
Adding just one spatial word can lead MLLMs to consistently choose the wrong answer, revealing a critical vulnerability in their reasoning processes.
Clinically-focused NER for prion diseases is now possible with PrionNER, a new dataset that exposes the limitations of existing models in extracting fine-grained, complex information from biomedical literature.
RL models trained with verifiable rewards exhibit a surprising deductive-over-abductive reasoning asymmetry, even in controlled environments, suggesting a fundamental challenge in current RLVR approaches.
