Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核
Advances in Neural Information Processing Systems , volume=
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mllm-shap is an open-source Python platform extending Shapley Value explainability to text-audio Multimodal LLMs via modality-aware masking and phonetic token grouping.
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Mechanisms of Misgeneralization in Physical Sequence Modeling
Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核
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mllm-shap: A Shapley Value Explainability Platform for Text-Audio Multimodal Large Language Models
mllm-shap is an open-source Python platform extending Shapley Value explainability to text-audio Multimodal LLMs via modality-aware masking and phonetic token grouping.
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NeuralSet: A High-Performing Python Package for Neuro-AI
NeuralSet is a scalable Python framework that unifies diverse neural recordings and stimuli with deep learning embeddings via metadata decoupling and lazy data extraction.