数据来源：ArXiv Domain LLM Domain Papers1. The Physical Basis of Prediction: World Model Formation in Neural Organoids via an LLM-Generated CurriculumThe capacity of an embodied agent to understand, predict, and interact with its environment is fundamentally contingent on an internal world model. This paper introduces a novel framework for investigating the formation and adaptation of such world models within a biological substrate: human neural organoids. We present a curriculum of three scalable, ...

数据来源：ArXiv Domain LLM Domain Papers1. The Physical Basis of Prediction: World Model Formation in Neural Organoids via an LLM-Generated CurriculumThe capacity of an embodied agent to understand, predict, and interact with its environment is fundamentally contingent on an internal world model. This paper introduces a novel framework for investigating the formation and adaptation of such world models within a biological substrate: human neural organoids. We present a curriculum of three scalable, ...

数据来源：ArXiv Domain LLM Domain Papers1. Atlas-free Brain Network TransformerCurrent atlas-based approaches to brain network analysis rely heavily on standardized anatomical or connectivity-driven brain atlases. However, these fixed atlases often introduce significant limitations, such as spatial misalignment across individuals, functional heterogeneity within predefined regions, and atlas-selection biases, collectively undermining the reliability and interpretability of the derived brain network ...

数据来源：ArXiv Domain LLM Domain Papers1. Atlas-free Brain Network TransformerCurrent atlas-based approaches to brain network analysis rely heavily on standardized anatomical or connectivity-driven brain atlases. However, these fixed atlases often introduce significant limitations, such as spatial misalignment across individuals, functional heterogeneity within predefined regions, and atlas-selection biases, collectively undermining the reliability and interpretability of the derived brain network ...

数据来源：ArXiv Domain LLM Domain Papers1. The Physical Basis of Prediction: World Model Formation in Neural Organoids via an LLM-Generated CurriculumThe capacity of an embodied agent to understand, predict, and interact with its environment is fundamentally contingent on an internal world model. This paper introduces a novel framework for investigating the formation and adaptation of such world models within a biological substrate: human neural organoids. We present a curriculum of three scalable, ...

数据来源：ArXiv Domain LLM Domain Papers1. Atlas-free Brain Network TransformerCurrent atlas-based approaches to brain network analysis rely heavily on standardized anatomical or connectivity-driven brain atlases. However, these fixed atlases often introduce significant limitations, such as spatial misalignment across individuals, functional heterogeneity within predefined regions, and atlas-selection biases, collectively undermining the reliability and interpretability of the derived brain network ...

数据来源：ArXiv Domain LLM Domain Papers1. Atlas-free Brain Network TransformerCurrent atlas-based approaches to brain network analysis rely heavily on standardized anatomical or connectivity-driven brain atlases. However, these fixed atlases often introduce significant limitations, such as spatial misalignment across individuals, functional heterogeneity within predefined regions, and atlas-selection biases, collectively undermining the reliability and interpretability of the derived brain network ...

数据来源：ArXiv Domain LLM Domain Papers1. Lost in the Middle: An Emergent Property from Information Retrieval Demands in LLMsThe performance of Large Language Models (LLMs) often degrades when crucial information is in the middle of a long context, a “lost-in-the-middle” phenomenon that mirrors the primacy and recency effects in human memory. We propose that this behavior is not simply a flaw indicative of information loss but an adaptation to different information retrieval demands during pre-tra ...

数据来源：ArXiv Domain LLM Domain Papers1. Lost in the Middle: An Emergent Property from Information Retrieval Demands in LLMsThe performance of Large Language Models (LLMs) often degrades when crucial information is in the middle of a long context, a “lost-in-the-middle” phenomenon that mirrors the primacy and recency effects in human memory. We propose that this behavior is not simply a flaw indicative of information loss but an adaptation to different information retrieval demands during pre-tra ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Vision Transformers for Functional MRI with Flat MapsA key question for adapting modern deep learning architectures to functional MRI (fMRI) is how to represent the data for model input. To bridge the modality gap between fMRI and natural images, we transform the 4D volumetric fMRI data into videos of 2D fMRI activity flat maps. We train Vision Transformers on 2.3K hours of fMRI flat map videos from the Human Connectome Project using the spatiotemp ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Vision Transformers for Functional MRI with Flat MapsA key question for adapting modern deep learning architectures to functional MRI (fMRI) is how to represent the data for model input. To bridge the modality gap between fMRI and natural images, we transform the 4D volumetric fMRI data into videos of 2D fMRI activity flat maps. We train Vision Transformers on 2.3K hours of fMRI flat map videos from the Human Connectome Project using the spatiotemp ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Vision Transformers for Functional MRI with Flat MapsA key question for adapting modern deep learning architectures to functional MRI (fMRI) is how to represent the data for model input. To bridge the modality gap between fMRI and natural images, we transform the 4D volumetric fMRI data into videos of 2D fMRI activity flat maps. We train Vision Transformers on 2.3K hours of fMRI flat map videos from the Human Connectome Project using the spatiotemp ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Vision Transformers for Functional MRI with Flat MapsA key question for adapting modern deep learning architectures to functional MRI (fMRI) is how to represent the data for model input. To bridge the modality gap between fMRI and natural images, we transform the 4D volumetric fMRI data into videos of 2D fMRI activity flat maps. We train Vision Transformers on 2.3K hours of fMRI flat map videos from the Human Connectome Project using the spatiotemp ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Vision Transformers for Functional MRI with Flat MapsA key question for adapting modern deep learning architectures to functional MRI (fMRI) is how to represent the data for model input. To bridge the modality gap between fMRI and natural images, we transform the 4D volumetric fMRI data into videos of 2D fMRI activity flat maps. We train Vision Transformers on 2.3K hours of fMRI flat map videos from the Human Connectome Project using the spatiotemp ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Vision Transformers for Functional MRI with Flat MapsA key question for adapting modern deep learning architectures to functional MRI (fMRI) is how to represent the data for model input. To bridge the modality gap between fMRI and natural images, we transform the 4D volumetric fMRI data into videos of 2D fMRI activity flat maps. We train Vision Transformers on 2.3K hours of fMRI flat map videos from the Human Connectome Project using the spatiotemp ...