AI for science
14 notes, newest first. Includes the vault tags science, scientific discovery, biology.
ScienceClaw: Benchmarking Continual Self-Evolution of AI-for-Science Agents Across the Natural and Social Sciences
The preprint formalizes fixed-parameter program self-evolution that links task solving, scientific verification, and program updates. ScienceClaw-Eval spans 23 disciplines and measures correctness, evolutionary gain…
ExplorationBench: Measuring AI Systems' Exploration in Verifiable Alien Worlds
ExplorationBench evaluates whether AI systems can acquire genuinely unfamiliar rules through exploration rather than recall. It uses two executable sandboxes, AlienCode and AlienLogic, with discovery targets, flawed…
ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents
ScienceBuddy is described as an interactive scientific-research workspace that converts researcher requests, feedback, and execution evidence into tasks and evaluation rubrics for continual learning. Its…
Eureka: Task-Conditioned Meta-Agent Orchestration for Scientific Discovery
The paper introduces Eureka, a Meta-Agent architecture that can complete long-horizon tasks by forming specialized Macro-Agents through receding-horizon planning and architecture promotion. The agents can evolve to…
Eureka: Task-Conditioned Meta-Agent Orchestration for Scientific Discovery
The paper introduces Eureka, a meta-agent system designed for scientific discovery tasks. Eureka forms specialized agents through planning, promotion, and minimal-sufficient compilation, adapting to recurring…
How the story of life and our planet unfolded — together
This article from Nature discusses a book about the evolution of life and our planet, provides a commentary on heatwaves affecting health, reviews books on AI as scientific tools, and offers a brief list of books.
TCA-SIR: Learning Target-Conditioned Abstractions for Scientific Inspiration Retrieval
Researchers developed a new method called TCA-SIR for AI to generate scientific hypotheses. This method focuses on transferable abstract principles rather than topical similarity when retrieving inspirations for a…
From Static Bibliometrics to Dynamic Knowledge Graphs: An LLM-Powered Framework for Modernizing Science, Technology, and Innovation (STI) Analytics
The text discusses a new framework for modernizing Science, Technology, and Innovation (STI) analytics. The proposed framework aims to improve upon traditional bibliometric indicators by using dynamic knowledge graphs…
Antigen-specific Antibody Multi-modal Foundation Model for Functional Antibody Design
Researchers have developed a new model called AAMFM to improve the design of functional antibodies. This model learns unified representations of antibody sequences and structures, considering antigen context, which…
Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System
The MEDA system is an automated framework using Large Language Models (LLMs) and Symbolic Regression (SR) to discover Ordinary Differential Equations (ODEs) for biological systems. It retrieves background knowledge…
Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System
The MEDA system is a new framework that uses large language models and symbolic regression to automatically discover ordinary differential equation (ODE) models for biological systems. The system retrieves background…
BayesEvolve: Explicit Belief States for Autonomous Scientific Discovery
BayesEvolve is a new framework for autonomous scientific discovery. It uses large language models (LLMs) to maintain explicit, uncertainty-aware beliefs about the quality of hypotheses, and it uses these beliefs to…
BayesEvolve: Explicit Belief States for Autonomous Scientific Discovery
The paper introduces a new framework called BayesEvolve for autonomous scientific discovery. This framework aims to improve the efficiency of experimentation by maintaining explicit, uncertainty-aware beliefs about…
RetiSEM: Generalising Causal Models for Fragmented Biomedical Data
A new method called RetiSEM is proposed to learn causal models from fragmented biomedical data, where clinical, molecular, and imaging variables are often incomplete or not jointly observed. The method organizes…
