Difference between revisions of "Science Agents"

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* 2024-10: [https://arxiv.org/abs/2411.00027 Personalization of Large Language Models: A Survey]
 
* 2024-10: [https://arxiv.org/abs/2411.00027 Personalization of Large Language Models: A Survey]
 
* 2024-11: [https://arxiv.org/abs/2411.00412 Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation]
 
* 2024-11: [https://arxiv.org/abs/2411.00412 Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation]
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==AI/LLM Control of Scientific Instruments/Facilities==
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* 2023-12: [https://www.nature.com/articles/s41524-024-01423-2 Opportunities for retrieval and tool augmented large language models in scientific facilities]
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* 2023-12: [https://arxiv.org/abs/2312.17180 Virtual Scientific Companion for Synchrotron Beamlines: A Prototype]
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* 2023-12: [https://www.nature.com/articles/s41586-023-06792-0 Autonomous chemical research with large language models]
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* 2024-01: [https://iopscience.iop.org/article/10.1088/2632-2153/ad52e9 Synergizing Human Expertise and AI Efficiency with Language Model for Microscopy Operation and Automated Experiment Design]
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* 2024-06: [https://pubs.rsc.org/en/content/articlelanding/2025/dd/d4dd00143e From Text to Test: AI-Generated Control Software for Materials Science Instruments]
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* 2024-12: [https://arxiv.org/abs/2412.18161 VISION: A Modular AI Assistant for Natural Human-Instrument Interaction at Scientific User Facilities]
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* 2025-01: [https://www.science.org/doi/10.1126/sciadv.adr4173 Large language models for human-machine collaborative particle accelerator tuning through natural language]
  
 
==AI/ML Methods tailored to Science==
 
==AI/ML Methods tailored to Science==
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* [https://www.markov.bio/ Markov Bio]: [https://www.markov.bio/research/mech-interp-path-to-e2e-biology Through a Glass Darkly: Mechanistic Interpretability as the Bridge to End-to-End Biology] ([https://x.com/adamlewisgreen/status/1853206279499751531 quick description], [https://markovbio.github.io/biomedical-progress/ background info on recent bio progress])
 
* [https://www.markov.bio/ Markov Bio]: [https://www.markov.bio/research/mech-interp-path-to-e2e-biology Through a Glass Darkly: Mechanistic Interpretability as the Bridge to End-to-End Biology] ([https://x.com/adamlewisgreen/status/1853206279499751531 quick description], [https://markovbio.github.io/biomedical-progress/ background info on recent bio progress])
 
* 2023-01: [https://arxiv.org/abs/2301.05062 Tracr: Compiled Transformers as a Laboratory for Interpretability] ([https://github.com/google-deepmind/tracr code])
 
* 2023-01: [https://arxiv.org/abs/2301.05062 Tracr: Compiled Transformers as a Laboratory for Interpretability] ([https://github.com/google-deepmind/tracr code])
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* 2024-10: [https://arxiv.org/abs/2410.03334 An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation]
 
* 2024-12: [https://www.arxiv.org/abs/2412.16247 Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models]
 
* 2024-12: [https://www.arxiv.org/abs/2412.16247 Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models]
 
* 2024-12: [https://arxiv.org/abs/2412.12101 InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders]
 
* 2024-12: [https://arxiv.org/abs/2412.12101 InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders]
 
* 2025-01: [https://arxiv.org/abs/2501.00089 Insights on Galaxy Evolution from Interpretable Sparse Feature Networks]
 
* 2025-01: [https://arxiv.org/abs/2501.00089 Insights on Galaxy Evolution from Interpretable Sparse Feature Networks]
 
* 2025-02: [https://www.biorxiv.org/content/10.1101/2025.02.06.636901v1 From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models]
 
* 2025-02: [https://www.biorxiv.org/content/10.1101/2025.02.06.636901v1 From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models]
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* 2025-02: [https://www.goodfire.ai/blog/interpreting-evo-2 Interpreting Evo 2: Arc Institute's Next-Generation Genomic Foundation Model]
  
 
===Uncertainty===
 
===Uncertainty===

Latest revision as of 13:12, 2 April 2025

AI Use-cases for Science

Literature

LLM extract data from papers

AI finding links in literature

(Pre) Generate Articles

Explanation

Autonomous Ideation

Adapting LLMs to Science

AI/LLM Control of Scientific Instruments/Facilities

AI/ML Methods tailored to Science

Regression (Data Fitting)

Tabular Classification/Regression

Symbolic Regression

Literature Discovery

Commercial

AI/ML Methods in Science

Chemistry

Biology

Successes

AI/ML Methods co-opted for Science

Mechanistic Interpretability

Train large model on science data. Then apply mechanistic interpretability (e.g. sparse autoencoders, SAE) to the feature/activation space.

Uncertainty

Science Benchmarks

Science Agents

Reviews

Specific

Science Multi-Agent Setups

AI Science Systems

Inorganic Materials Discovery

Chemistry

LLMs Optimized for Science

Impact of AI in Science

Related Tools

Literature Search

Data Visualization

Generative

Chemistry

See Also

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