AI Scientists
An Omni-Modal Omni-Discipline AI Scientist
End-to-End 35
Self-Reinforcing Autonomous Research with Human-AI Collaboration
Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery
Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery
AI Accelerates AI
Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering
An AI Scientist for Autonomous Discovery
The Denario project: Deep knowledge AI agents for scientific discovery
The AI-XR Co-Scientist That Sees and Works With Humans
Advancing Frontier-Pushing Scientific Findings Progressively
Towards Open-Ended And Sample-Efficient Program Evolution
Self-Evolving LLM Agent for Biomedical Research
A coding agent for scientific and algorithmic discovery
Machine Learning Engineering Agent via Search and Targeted Refinement
A multi-agent system for automating scientific discovery
Open-Ended Evolution of Self-Improving Agents
An Autonomous Agent for Quantum Chemistry
Workshop-Level Automated Scientific Discovery via Agentic Tree Search
A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge
Accelerated Inorganic Materials Design with Generative AI Agents
Using LLM Agents as Research Assistants
Moving Towards Closed-loop Auto-research through Thinking, Practice, and Feedback
Improving Automated Research via Automated Review
Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance
A Multi-Agent Framework for Autonomous Data Science Competitions
A Multi-Agent LLM Framework for Full-Pipeline AutoML
Towards Fully Automated Open-Ended Scientific Discovery
Autonomous Machine Learning Research based on Large Language Models Agents
Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence
Autonomous LLM-driven research from data to human-verifiable research papers
CRISPR-GPT for Agentic Automation of Gene-editing Experiments
Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
Protein discovery via large language model multi-agent collaborations combining physics and machine learning
A Robotic Assistant for Automated Chemistry Experimentation and Characterization
Mathematical discoveries from program search with large language models
Co-Scientists 27
Accelerating scientific discovery with Co-Scientist
Language agents achieve superhuman synthesis of scientific knowledge
Synthesizing Scientific Literature with Retrieval-augmented LMs
A General-Purpose Biomedical AI Agent
Autonomous chemical research with large language models
Augmenting large-language models with chemistry tools
Iterative Research Idea Generation over Scientific Literature with Large Language Models
training language agents on challenging scientific tasks
Automating scientific discovery through multi-agent intelligent graph reasoning
An open platform for democratizing AI scientists
Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents
SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam?
An AI Agent for Designing Genetic Perturbation Experiments
Self-verification Language Agent for Gene Set Knowledge Discovery using Domain Databases
Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data
A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis
Rational Inverse Design of Materials with AI Agents
Self-updating Library in Large Language Models Improves Chemical Reasoning
Autonomous Materials Discovery with Large Language Models
Multi-agent pipeline searches literature and datasets, then drafts and reviews dry and wet-lab biomedical experimental protocols.
★ 6
A Multi-Agent AI for Hypothesis Generation from Mass Spectrometry Data
The AI Cosmologist I: An Agentic System for Automated Data Analysis
An Iterative Planning and Search Approach to Enhance Novelty and Diversity of LLM Generated Ideas
Advancing Human-AI Collaboration in the Science of Science
Tool-augmented Language Models for Scientific Reasoning
Screens and extracts structured data from 125M papers, automating systematic-review screening into reusable tables.
Iteratively reads and scores hundreds of papers and follows citation trails until a search is exhaustive.
Nothing matches those filters.