The paper introduces CoLabScience with PULI, a positive-unlabeled RL framework for proactive interventions in streaming biomedical dialogues, plus the BSDD benchmark dataset, claiming superior performance over baselines.
Large language models as biomedical hypothesis generators: a comprehensive evaluation
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Structuring LLM hypothesis generation around deductive-nomological explanation, causal processes, and universals is reported to beat direct prompting, with two generated ideas implemented as the CTAT and HALO algorithms.
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.
Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.
citing papers explorer
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"Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborations
The paper introduces CoLabScience with PULI, a positive-unlabeled RL framework for proactive interventions in streaming biomedical dialogues, plus the BSDD benchmark dataset, claiming superior performance over baselines.
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DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations
Structuring LLM hypothesis generation around deductive-nomological explanation, causal processes, and universals is reported to beat direct prompting, with two generated ideas implemented as the CTAT and HALO algorithms.
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Large Language Model Agent: A Survey on Methodology, Applications and Challenges
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.
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Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning
Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.