数据来源：ArXiv Domain LLM Domain Papers1. Scaling Environments for Organoid Intelligence with LLM-Automated Design and Plasticity-Based EvaluationAs the complexity of artificial agents increases, the design of environments that can effectively shape their behavior and capabilities has become a critical research frontier. We propose a framework that extends this principle to a novel class of agents: biological neural networks in the form of neural organoids. This paper introduces three scalable, cl ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Environments for Organoid Intelligence with LLM-Automated Design and Plasticity-Based EvaluationAs the complexity of artificial agents increases, the design of environments that can effectively shape their behavior and capabilities has become a critical research frontier. We propose a framework that extends this principle to a novel class of agents: biological neural networks in the form of neural organoids. This paper introduces three scalable, cl ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Environments for Organoid Intelligence with LLM-Automated Design and Plasticity-Based EvaluationAs the complexity of artificial agents increases, the design of environments that can effectively shape their behavior and capabilities has become a critical research frontier. We propose a framework that extends this principle to a novel class of agents: biological neural networks in the form of neural organoids. This paper introduces three scalable, cl ...

数据来源：ArXiv Domain LLM Domain Papers1. Bitune: Leveraging Bidirectional Attention to Improve Decoder-Only LLMsDecoder-only large language models typically rely solely on masked causal attention, which limits their expressiveness by restricting information flow to one direction. We propose Bitune, a method that enhances pretrained decoder-only LLMs by incorporating bidirectional attention into prompt processing. We evaluate Bitune in instruction-tuning and question-answering settings, showing si ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Environments for Organoid Intelligence with LLM-Automated Design and Plasticity-Based EvaluationAs the complexity of artificial agents increases, the design of environments that can effectively shape their behavior and capabilities has become a critical research frontier. We propose a framework that extends this principle to a novel class of agents: biological neural networks in the form of neural organoids. This paper introduces three scalable, cl ...

数据来源：ArXiv Domain LLM Domain Papers1. Scaling Environments for Organoid Intelligence with LLM-Automated Design and Plasticity-Based EvaluationAs the complexity of artificial agents increases, the design of environments that can effectively shape their behavior and capabilities has become a critical research frontier. We propose a framework that extends this principle to a novel class of agents: biological neural networks in the form of neural organoids. This paper introduces three scalable, cl ...

数据来源：ArXiv Domain LLM Domain Papers1. Charting trajectories of human thought using large language modelsLanguage provides the most revealing window into the ways humans structure conceptual knowledge within cognitive maps. Harnessing this information has been difficult, given the challenge of reliably mapping words to mental concepts. Artificial Intelligence large language models (LLMs) now offer unprecedented opportunities to revisit this challenge. LLMs represent words and phrases as high-di ...

数据来源：ArXiv Domain LLM Domain Papers1. Charting trajectories of human thought using large language modelsLanguage provides the most revealing window into the ways humans structure conceptual knowledge within cognitive maps. Harnessing this information has been difficult, given the challenge of reliably mapping words to mental concepts. Artificial Intelligence large language models (LLMs) now offer unprecedented opportunities to revisit this challenge. LLMs represent words and phrases as high-di ...

数据来源：ArXiv Domain LLM Domain Papers1. Charting trajectories of human thought using large language modelsLanguage provides the most revealing window into the ways humans structure conceptual knowledge within cognitive maps. Harnessing this information has been difficult, given the challenge of reliably mapping words to mental concepts. Artificial Intelligence large language models (LLMs) now offer unprecedented opportunities to revisit this challenge. LLMs represent words and phrases as high-di ...

数据来源：ArXiv Domain LLM Domain Papers1. Charting trajectories of human thought using large language modelsLanguage provides the most revealing window into the ways humans structure conceptual knowledge within cognitive maps. Harnessing this information has been difficult, given the challenge of reliably mapping words to mental concepts. Artificial Intelligence large language models (LLMs) now offer unprecedented opportunities to revisit this challenge. LLMs represent words and phrases as high-di ...

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. 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 ...