arXiv AI
Jun 16
Towards End-to-End Automation of AI Research
arXiv:2606. 15497v1 Announce Type: new Abstract: The automation of science is a long-standing ambition in the field of AI.
Closing the empirical loop: autonomous AI agents conduct end-to-end research with human participants
The paper reports that a domain‑agnostic, agentic AI system can autonomously carry out the full scientific workflow—from hypothesis generation to data collection and manuscript preparation—by conducting three online psychological studies involving 288 participants. The AI designed experiments on visual working memory, mental rotation, and imagery vividness, coded analysis pipelines in over eight hours, and produced completed manuscripts, achieving methodological rigor comparable to experienced researchers. The work demonstrates the feasibility of AI‑driven scientific discovery while highlighting limitations in conceptual nuance and raising questions about scientific credit and understanding.
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arXiv AI
Sep 18
ScientistTwo: Pioneering the Human Knowledge Frontier with Autonomous AI
The paper introduces ScientistTwo, a fully autonomous multi‑agent framework that takes a scientific problem, establishes baselines, generates hypotheses, and coordinates specialized agents to conduct an end‑to‑end discovery cycle without human intervention. It rigorously tests and refines its methods through automated experiments, ablation studies, and a closed‑loop peer‑review engine. Benchmarking against top conferences (ICLR, ICML, NeurIPS) shows that ScientistTwo produces expert‑level, publishable papers and codebases that outperform human state‑of‑the‑art models and receive higher review ratings under automated AI review.
arXiv AI
Aug 14
OmniScientist: An Omni-Modal Omni-Discipline AI Scientist
arXiv:2608. 13558v1 Announce Type: new Abstract: Recent advances in foundation models have enabled AI scientists to automate increasingly complete research workflows, from hypothesis generation and code execution to manuscript preparation.
arXiv AI
Jun 8
Act As a Real Researcher: A Suite of Benchmarks Evaluating Frontier LLMs and Agentic Harnesses in Research Lifecycle
arXiv:2606. 07462v1 Announce Type: new Abstract: As foundation models advance and agent scaffolding becomes increasingly sophisticated, agents have demonstrated remarkable proficiency in complex, long-horizon coding tasks and even autonomous experiment execution.
arXiv Machine Learning
Oct 15
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery
arXiv:2610.18933v1 Announce Type: cross Abstract: Modern coding agents are fundamentally reshaping the role of human input for computational research: they write code, launch in silico experiments, a...
arXiv AI
Sep 15
Atria Dawn: The Dawn of Agentic Superintelligence
The paper introduces Atria Dawn Preview, a foundation agentic language model aimed at scientific research and engineering workflows. Trained through a Verifiable Experience Pipeline, it performs competitively across 16 real‑world benchmarks, achieving the highest scores on five. The authors also present a detailed case study of human–AI collaboration, showing that while AI proposes methods and revisions, humans retain final decision‑making and guide the research direction.
arXiv AI
Sep 10
AgentIdeaBench: Benchmarking Scientific Ideation in the Agent Era
arXiv:2609.07611v1 Announce Type: new Abstract: Scientific ideation is the capacity to formulate novel and testable hypotheses from scientific evidence, and autonomous AI scientists depend on it. Exi...
arXiv AI
Aug 19
AutoResearch: Insight In, Hallucination Out
AutoResearch is a two‑stage autonomous research system that links Idea Generation with Idea Execution. In the generation phase it blends new research signals with existing domain knowledge, identifies transferable mechanistic insights, and produces grounded, testable research plans through multi‑model generation and cross‑review. The execution phase then decomposes these plans into experiments, iteratively implements and diagnoses them, and uses independent evidence‑based review to accept or revise conclusions, thereby turning ideas into measurable progress while minimizing hallucinations.
arXiv AI
Jul 17
BrainPilot: Automating Brain Discovery with Agentic Research
arXiv:2607. 15079v1 Announce Type: new Abstract: Understanding the brain increasingly depends on integrating evidence across scales, modalities, and disciplines.
arXiv AI
Oct 27
DISCERN: Can AI Agents Work Like Scientists and Guide Discovery?
arXiv:2609.33357v2 Announce Type: replace Abstract: Reliable automated research requires agents to vet data, verify analyses, and generate hypotheses grounded in trustworthy evidence, potentially red...
arXiv Computation and Language
Oct 22
Building AI Research Teams for Autonomous Discovery of Linear Attention Architectures
The paper proposes a framework for building autonomous AI research teams that can discover linear attention architectures. By assembling agents with diverse disciplinary expertise, private research histories, shared experimental evidence, and adaptive allocation of research opportunities, the team can balance exploration and exploitation under limited experimental budgets. Experiments show that this design outperforms GDN2 at 1.3B scale on language modeling, common‑sense reasoning, and retrieval tasks, and that removing any single component weakens discovery.
arXiv AI
Sep 25
AI in Science: Early Insights
The paper "AI in Science: Early Insights" analyzes AI’s impact on scientific work using data from 15 million Gemini interactions, 2,600 specialized AI models, and a survey of 600 scientists. It finds widespread AI adoption, complementary use of large language models and specialized tools, significant productivity gains of about seven hours per week, and a shift in research bottlenecks toward hypothesis backlog and verification needs. The study suggests AI can boost scientific productivity but its full effect depends on addressing new downstream challenges.