MatMMExtract pipeline creates MatSciFig dataset of 391k annotated materials science figure panels and MaterialScope detection dataset with high accuracy.
super hub Canonical reference
Towards an AI co-scientist
Canonical reference. 88% of citing Pith papers cite this work as background.
117
Pith papers citing it
Background
88% of classified citations
abstract
Scientific discovery relies on scientists generating novel hypotheses that undergo rigorous experimental validation. To augment this process, we introduce an AI co-scientist, a multi-agent system built on Gemini 2.0. The AI co-scientist is intended to help uncover new, original knowledge and to formulate demonstrably novel research hypotheses and proposals, building upon prior evidence and aligned to scientist-provided research objectives and guidance. The system's design incorporates a generate, debate, and evolve approach to hypothesis generation, inspired by the scientific method and accelerated by scaling test-time compute. Key contributions include: (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling; (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute, improving hypothesis quality. While general purpose, we focus development and validation in three biomedical areas: drug repurposing, novel target discovery, and explaining mechanisms of bacterial evolution and anti-microbial resistance. For drug repurposing, the system proposes candidates with promising validation findings, including candidates for acute myeloid leukemia that show tumor inhibition in vitro at clinically applicable concentrations. For novel target discovery, the AI co-scientist proposed new epigenetic targets for liver fibrosis, validated by anti-fibrotic activity and liver cell regeneration in human hepatic organoids. Finally, the AI co-scientist recapitulated unpublished experimental results via a parallel in silico discovery of a novel gene transfer mechanism in bacterial evolution. These results, detailed in separate, co-timed reports, demonstrate the potential to augment biomedical and scientific discovery and usher an era of AI empowered scientists.
hub tools
citation-role summary
background 22
baseline 1
other 1
citation-polarity summary
claims ledger
- abstract Scientific discovery relies on scientists generating novel hypotheses that undergo rigorous experimental validation. To augment this process, we introduce an AI co-scientist, a multi-agent system built on Gemini 2.0. The AI co-scientist is intended to help uncover new, original knowledge and to formulate demonstrably novel research hypotheses and proposals, building upon prior evidence and aligned to scientist-provided research objectives and guidance. The system's design incorporates a generate, debate, and evolve approach to hypothesis generation, inspired by the scientific method and accele
authors
co-cited works
representative citing papers
Formalizes interface-constrained semi-Markov decision processes and proves a finite-sample bound for neural IC-Q that decomposes into neural approximation error, interface gap, and mixing-time residual, with experiments showing parity to centralized oracles.
AutoResearchBench is a new benchmark showing top AI agents achieve under 10% success on complex scientific literature discovery tasks that demand deep comprehension and open-ended search.
FermiLink is a unified AI agent framework that automates multidomain scientific simulations via separated package knowledge bases and a four-layer progressive disclosure mechanism, reproducing 56% of target figures in benchmarks and generating research-grade results on unpublished problems.
The SDE benchmark shows LLMs lag on scientific discovery tasks relative to general science tests, with diminishing scaling returns and shared weaknesses across models.
The authors create the first large-scale dataset and taxonomy of failure modes in multi-agent LLM systems to explain their limited performance gains.
A token-based authorization system with simulation and human gates enables safe LLM-written ARTIQ control code execution on trapped-ion platforms while blocking unauthorized hardware access.
Coding agents rediscovering a particle observable in Geant4 simulations reach correct answers via incorrect mechanisms in 7 of 28 episodes, requiring separate measurement of mechanism fidelity and honesty.
PhySciBench benchmark shows current AI models achieve at most 33.5% accuracy on physical science tasks; DelveAgent framework improves accuracy by up to 7.5 points and cuts costs to one-third.
SciAgentArena is a new interactive benchmark for AI agents on scientific tasks that finds agents handle clear data-analysis workflows but struggle with novel insights, self-directed exploration, and open-ended questions.
InquiTree shows LLM agents suffer from degrading critical capabilities during extended scientific interactions and perform worse on papers published after their training cutoffs.
CollabSim is a new CSCW-grounded simulation framework that enables controlled multi-agent experiments to measure collaborative competence in LLM agents.
Introduces the Matter to Mechanism benchmark of 2,645 structured instances and a composite metric suite for evaluating AI co-scientists on problem-to-hypothesis reasoning in battery materials research.
ResearchClawBench supplies 40 grounded tasks and expert rubrics to measure autonomous research agents, with the strongest systems scoring only 21.5 and 20.7 on average.
Introduces the CUSP benchmark across 4760 events and finds frontier AI models can pick plausible directions but fail to predict whether or when scientific advances will occur, with performance varying by domain and insensitive to training cutoffs.
EngiAI introduces a LangGraph-based multi-agent framework and a three-part benchmark suite for LLM-driven engineering design, reporting high task completion rates for proprietary models on Beams2D and Photonics2D problems.
Hybrid human-AI networks in 5x5 grids reached lower final polarization than human-only networks after eight rounds of opinion revision on polarizing topics.
MLS-Bench is a benchmark with 140 tasks that evaluates AI agents on inventing generalizable and scalable ML methods, finding they lag human performance especially in insight-driven invention rather than tuning.
An interactive AI workbench for mathematicians achieves 48% on FrontierMath Tier 4 and helped solve open problems in early tests.
AI CFD Scientist autonomously discovers a Spalart-Allmaras runtime correction reducing lower-wall Cf RMSE by 7.89% on the periodic hill at Reh=5600 while using a vision-language gate to detect 14 of 16 silent failures missed by solver checks.
An LLM agent autonomously identifies and experimentally validates a previously unreported optical bilinear interaction on a physical platform.
RosettaSearch applies LLM-driven multi-objective search at inference time to improve backbone-conditioned protein sequences, recovering designs with 18-68% better structural fidelity and 2.5x higher success rates than single-pass models like LigandMPNN.
Kosmos is an AI scientist that maintains coherence over hundreds of agent steps via a shared world model, executes thousands of code lines and reads thousands of papers per run, and produces traceable reports with 79.4% statement accuracy according to independent reviewers.
AlphaEvolve is an LLM-orchestrated evolutionary coding agent that discovered a 4x4 complex matrix multiplication algorithm using 48 scalar multiplications, the first improvement over Strassen's algorithm in 56 years, plus optimizations for Google data centers and hardware.
citing papers explorer
-
TSAssistant: A Human-in-the-Loop Agentic Framework for Automated Target Safety Assessment
TSAssistant decomposes target safety assessment report generation into research and synthesis subagents with tool-based evidence retrieval, hierarchical instructions, and interactive human refinement, reporting high reproducibility and grounding.
-
Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
A survey proposing a three-level capability taxonomy (L1 Predictor, L2 Simulator, L3 Evolver) for world models across physical, digital, social, and scientific domains.
-
LARA: Validation-Driven Agentic Supercomputer Workflows for Atomistic Modeling
LARA-HPC introduces a validation-first agentic system with dry-run verification and multi-phase refinement that improves robustness of AI-generated DFT workflows on HPC systems.
-
EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale
EvoMaster is an open-source agent framework whose iterative self-evolution loop achieves the highest average score on 9 of 10 scientific agent benchmarks compared to OpenHands, OpenClaw, and Codex.
-
Agentic Risk-Aware Set-Based Engineering Design
Multi-agent LLM system applies set-based design and Conditional Value-at-Risk to explore and risk-filter airfoil designs with human manager coordination.
-
MIND: AI Co-Scientist for Material Research
MIND is an LLM-driven multi-agent system for automated hypothesis validation in materials science using scalable in-silico experiments with ML interatomic potentials.
-
Omakase: proactive assistance with actionable suggestions for evolving scientific research projects
Omakase monitors project documents to infer timely queries and distills research reports into actionable suggestions that users rated significantly more useful than raw reports.
-
Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator
The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.
-
URSA: The Universal Research and Scientific Agent
URSA is a modular agent ecosystem that uses LLMs and scientific tools to accelerate research tasks of varying complexity.
-
From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review
A survey consolidating benchmarks, agent frameworks, real-world applications, and protocols for LLM-based autonomous agents into a proposed taxonomy with recommendations for future research.
-
AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions
Position paper arguing that multi-agent AI systems can become AI scientists and calling for reformed scientific institutions to support their development with emphasis on verification and dual-use safety.
-
Generative artificial intelligence and the marginalization of minoritized knowledges in higher education: the case of disability
Generative AI reinforces epistemic coloniality and double marginalization of disabled knowledges in higher education via Anglophone/Western datasets and reductive stereotypes or design exclusion.
-
The Agentification of Scientific Research: A Physicist's Perspective
AI will evolve from a research tool into a collaborator, fundamentally reshaping scientific collaboration, discovery, publishing, and evaluation while requiring continuous learning and idea diversity for original contributions.
-
WisPaper: Your AI Scholar Search Engine
WisPaper integrates semantic search with agent-based validation, library organization, and personalized AI feeds into a closed-loop system that improves academic paper discovery and long-term awareness.
- Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems
- VERITAS: A Multi-Agent Co-Scientist for Verifiable Image-Derived Hypothesis Testing
- AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery