Elicit Research Agent is an AI environment built for high-stakes decisions
New in Elicit: Research Agents - Elicit
Elicit Research Agent powering workflows for competitive landscapes, research landscape and broad topic exploration
AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions
Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code generation, data analysis, hypothesis proposal, and model criticism. We argue that this transition is qualitative rather than incremental, and that suitably designed multi-agent systems may evolve from passive computational tools into ``AI scientists'' that can expand the hypothesis-generating and verification capacity of science. Such systems must be developed and deployed within a scientific ecosystem fit for purpose: institutions must be redesigned for verification, accountability, interpretability, and dual-use safety. We sketch how multi-agent architectures, illustrated by the prototype framework \textit{Denario}, accelerate the discovery cycle and traverse model spaces beyond human reach; examine what this implies for authorship, peer review, and the enduring role of human scientists; and close with recommendations for governing AI as an epistemic actor rather than a mere instrument.
AI Research Agents Narrow Scientific Exploration
AI research agents now support large-scale AI-assisted scientific discovery. We examine whether AI-generated ideas broaden scientific exploration or primarily reinforce existing work. Using five agent frameworks and five large language models, we generate 219,655 ideas for different scientific fields. Across experiments, four consistent patterns emerge. First, AI-generated ideas are more concentrated than human-authored papers within the same research area. Second, they remain much closer to starting literature than later human follow-on work does. Third, AI-generated ideas align less with future human research. Last, AI-generated ideas are located in lower-impact regions of the historical scientific landscape. Overall, current AI research agents appear better suited to local elaboration than to broadening scientific exploration.
How we built our multi-agent research system
On the the engineering challenges and lessons learned from building Claude's Research system
Introducing Routines: Research that keeps working - Elicit
Tell the Elicit Research Agent what to keep current. Routines find new evidence, update your work, and report back for your review.
AI agents team up in Agent Laboratory to speed scientific research
Johns Hopkins University and AMD have developed Agent Laboratory, a new open-source framework that pairs human creativity with AI-powered workflows.
ScientistTwo: Pioneering the Human Knowledge Frontier with Autonomous AI
Scientific discovery is defined by the ability to identify the boundaries of existing knowledge and venture into unexplored territory. The ultimate vision for AI in science is problem-driven autonomous research: given a fundamental challenge by a human expert, the AI independently navigates the scientific landscape, uncovers theoretical and empirical bottlenecks, and systematically expands the frontier of knowledge. In this paper, we introduce ScientistTwo, a fully autonomous multi-agent framework designed to realize this vision. Specifically, ScientistTwo takes an initial problem as input, establishes state-of-the-art baselines, formulates novel hypotheses, and coordinates specialized agents to orchestrate an end-to-end discovery cycle without human intervention. Moreover, the framework rigorously conducts experiments using diverse datasets and metrics, refines methodologies through automated ablation studies, and validates research findings via a closed-loop simulated peer-review rebuttal engine. To evaluate ScientistTwo's capabilities against the highest standards of human scientific achievement, we benchmark it across papers accepted at top-tier conferences such as ICLR, ICML, and NeurIPS. As a result, ScientistTwo autonomously generates expert-level, publishable papers and fully verified, executable codebases. Its solutions consistently outperform human state-of-the-art models, and achieve higher average review ratings than human-authored papers under automated AI review agents. These results show that ScientistTwo is not merely an assistive tool but an autonomous scientific pioneer capable of pushing the frontiers of human discovery. Project website: https://scientist-two.github.io/
Human scientists trounce the best AI agents on complex tasks
State-of-the-industry report finds that, despite the limitations of artificial-intelligence systems, researchers have embraced them.
Introduction to Agents
Discover what actually works in AI. Join millions of builders, researchers, and labs evaluating agents, models, and frontier technology through crowdsourced benchmarks, competitions, and hackathons.
Lightcone Research
An open ecosystem for inspectable, composable, and referenceable scientific research in the age of agentic AI.
Alerts | Elicit: Al for scientific research
AI monitoring and screening with Elicit Alerts help you stay on top of all new research. Save a new alert mid-workflow and be notified when new relevant papers become available.
OpenScience.ai — Autonomous AI Research Agents
AI agents conducting reproducible scientific inquiry. Full provenance, executable notebooks, peer validation.
Elicit: AI for scientific research
Use AI to search, summarize, extract data from, and chat with over 125 million papers. Used by over 2 million researchers in academia and industry.
Elicit: AI for scientific research
Use AI to search, summarize, extract data from, and chat with over 125 million papers. Used by over 2 million researchers in academia and industry.
AI for science needs reasoning, not just data
AI agents that can model the human process of research will accelerate discoveries in science.
