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.
Learning Scientific Exploration from Human Research Decision Trajectories
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arXiv AI
Aug 12
Tree-of-Ideas: Automated Research Ideation via Cross-Trajectory Reasoning over Scholarly Evolution
arXiv:2608. 10740v1 Announce Type: new Abstract: Effective research ideation requires moving beyond a static understanding of prior work to trace how research problems and solutions evolve across the literature.
arXiv AI
Oct 2
ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research
ScholarCatalyst is a new benchmark that evaluates how well AI systems can retrieve research papers that inspire new work. The dataset was created by having 184 lead authors of 207 recent computer science papers annotate which earlier papers helped their projects, providing detailed rationales. The benchmark tests retrieval from the literature available at the start of a project, revealing that current agentic search and even advanced models like Claude Fable 5.1 perform only modestly better than simple embedding retrieval.
Hugging Face Trending Papers
Jul 29
SciDataSailor: Deep Scientific Data Exploring
Scientific datasets are commonly organized as hierarchical repositories containing heterogeneous and interdependent files, making their inspection, integration, and analysis labor-intensive and reliant on domain expertise. Although large language model (LLM) agents have advanced substantially in planning, reasoning, and tool use, existing research has largely overlooked their ability to interact with real scientific data assets through executable environments.
arXiv Machine Learning
Sep 23
GitScholar: A Dataset for Predicting AI Research Impact from GitHub Engagement
arXiv:2609.26361v1 Announce Type: cross Abstract: With the rapid pace of AI research and the hundreds of daily new publications, staying up-to-date with the latest developments has become increasingl...
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
Aug 18
Personalized Auto-Research: Towards a True AI Co-Scientist
arXiv:2608. 14881v1 Announce Type: new Abstract: AI co-scientists that generate hypotheses, retrieve related work, design experiments, execute code, and draft full papers are beginning to change how research is carried out.
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
Sep 17
PrimeScientist: Strategic Allocation of Research Effort in Autonomous Research
PrimeScientist is a system that jointly selects research directions and allocates resources for autonomous research agents. It models the problem as a sequential decision task, using an executable plan tree to track competing plans and an adaptive MCTS-based policy to balance exploration and exploitation based on remaining resources and experimental feedback. Experiments on AI research, systems, code optimization, and machine learning engineering show that PrimeScientist improves average reward by 10.3% while reducing research attempts by 50.6% compared to AutoResearch under the same budget.
arXiv AI
Sep 10
AutoKD: Autonomous Knowledge Discovery
arXiv:2609.06366v1 Announce Type: new Abstract: Scientific discovery in data-rich domains is currently constrained by human bandwidth: the growth in the volume and complexity of real-world data far o...
arXiv AI
Jul 3
PreScience: A Dataset and Benchmark for Scientific Forecasting
arXiv:2602. 20459v2 Announce Type: replace Abstract: Can AI systems trained on the existing scientific record forecast the advances that will follow?
arXiv AI
Sep 16
ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents
arXiv:2609.17523v1 Announce Type: new Abstract: We introduce and release ScienceBuddy, an interactive scientific research workspace that brings continually improving scientific agents into researcher...