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Notes on ai for science
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Prior or Feedback? What an LLM Uses When Adapting Neural Operators
The study tests whether an LLM scientific agent adapts neural-operator tuning using experimental feedback under a limited trial budget. The authors report lower held-out nRMSE than random search and Bayesian…
TCA-SIR: Learning Target-Conditioned Abstractions for Scientific Inspiration Retrieval
Researchers have developed a new method called TCA-SIR for AI to generate scientific hypotheses by learning target-conditioned abstractions (TCAs). This method improves upon previous methods by specifically extracting…
TCA-SIR: Learning Target-Conditioned Abstractions for Scientific Inspiration Retrieval
In this paper, the authors propose a new method called TCA-SIR (Target-Conditioned Abstraction for Scientific Inspiration Retrieval) to help AI generate scientific hypotheses more effectively. The method focuses on…
AutoSynthesis: An agentic system for automated meta-analysis
AutoSynthesis is a multi-agent system designed for automated meta-analysis of research questions in natural language. The system searches for scientific literature, assesses eligibility, extracts quantitative…
Optimal Resource Utilization for Autonomous Laboratory Orchestrators
The authors propose a two-step method to optimize resource utilization in autonomous laboratories, specifically for metal-organic framework synthesis. The first step involves using constraint programming to find the…
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