数据来源：ArXiv Domain LLM Domain Papers1. From Prediction to Understanding: Will AI Foundation Models Transform Brain Science?Generative pretraining (the “GPT” in ChatGPT) enables language models to learn from vast amounts of internet text without human supervision. This approach has driven breakthroughs across AI by allowing deep neural networks to learn from massive, unstructured datasets. We use the term foundation models to refer to large pretrained systems that can be adapted to a wide range ...

数据来源：ArXiv Domain LLM Domain Papers1. From Prediction to Understanding: Will AI Foundation Models Transform Brain Science?Generative pretraining (the “GPT” in ChatGPT) enables language models to learn from vast amounts of internet text without human supervision. This approach has driven breakthroughs across AI by allowing deep neural networks to learn from massive, unstructured datasets. We use the term foundation models to refer to large pretrained systems that can be adapted to a wide range ...

数据来源：ArXiv Domain LLM Domain Papers1. From Prediction to Understanding: Will AI Foundation Models Transform Brain Science?Generative pretraining (the “GPT” in ChatGPT) enables language models to learn from vast amounts of internet text without human supervision. This approach has driven breakthroughs across AI by allowing deep neural networks to learn from massive, unstructured datasets. We use the term foundation models to refer to large pretrained systems that can be adapted to a wide range ...

数据来源：ArXiv Domain LLM Domain Papers1. From Prediction to Understanding: Will AI Foundation Models Transform Brain Science?Generative pretraining (the “GPT” in ChatGPT) enables language models to learn from vast amounts of internet text without human supervision. This approach has driven breakthroughs across AI by allowing deep neural networks to learn from massive, unstructured datasets. We use the term foundation models to refer to large pretrained systems that can be adapted to a wide range ...

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. 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. 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. 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. 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 ...