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SGHA is a fully automated system that uses a local 9B language model to discover research problems from scientific literature by structuring evidence and detecting structural gaps, offering transparency and privacy over proprietary models.
Discovery Loop is a new AI research lab founded by Jeff Dean, Sanjay Ghemawat, Quoc Le, and Oriol Vinyals to automate scientific and engineering experimental loops using frontier AI models and large-scale infrastructure.
auto-psych is an agent-based system that automates theory discovery and experimentation in computational cognitive science, using LLM agents to generate hypotheses, design experiments, and analyze data from crowdsourced participants. It demonstrates faster and better theory generation compared to human-derived theories in a classic psychology paradigm.
This paper introduces VESTA, a framework that equips vision-language models with dynamically growing toolkits for data exploration and statistical model refinement, outperforming prior agent-based methods on complex scientific modeling tasks. The authors also present Dawn, a benchmark for distribution fitting and time series modeling, including real-world astronomy challenges.
A tweet sharing AutoSci, a system from Peking University that automates the entire research lifecycle from literature review to rebuttal, with self-improvement between projects.
Proposes Graphs of Research (GoR), a supervised fine-tuning method that uses citation evolution graphs as supervision for LLM-based research idea generation, achieving state-of-the-art results against gpt-4o-driven baselines.
DeepMind's Deep Tank AI system optimizes the growth of 2D semiconductors, achieving crystals measuring 130 micrometers—surpassing the 100-micrometer target—and significantly accelerating the parameter search process for material fabrication.