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.
Evolutionary Intelligence for Scientific Discovery: From Evolutionary Computation to Cumulative Discovery Systems
arXiv:2607. 09025v1 Announce Type: cross Abstract: Artificial intelligence (AI) is shifting scientific discovery from task-specific workflows towards autonomous systems that organize exploration with experimental and human feedback in open-ended candidate spaces.
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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...
arXiv AI
Aug 17
ScienceFlow: A long-horizon agent for ML research, scientific discovery and beyond
arXiv:2608. 14354v1 Announce Type: new Abstract: Enabling LLM agents to sustain productive, stable, and goal-aligned research over extended horizons is a central challenge for autonomous machine learning and scientific discovery, as progress hinges on continuously managing evolving state, exploration decisions, and computational resources.
arXiv AI
Jun 10
Towards Diverse Scientific Hypothesis Search with Large Language Models
arXiv:2606. 10587v1 Announce Type: cross Abstract: Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses.
Hugging Face Trending Papers
Sep 15
ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents
We introduce and release ScienceBuddy, an interactive scientific research workspace that brings continually improving scientific agents into researchers' everyday workflows. ScienceBuddy supports rese...
arXiv AI
Jul 21
From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Data
arXiv:2607. 16845v1 Announce Type: new Abstract: Scientists at European XFEL conduct experiments that generate very large and complex datasets.
arXiv AI
Aug 5
Towards a new paradigm of scientific discovery with socialized artificial intelligence
arXiv:2608. 02775v1 Announce Type: new Abstract: Scientific discovery has advanced through successive transformations in the organization of knowledge.
arXiv AI
Jun 24
ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms
arXiv:2512. 03476v3 Announce Type: replace-cross Abstract: Progress in computational science depends on complex numerical workflows that must faithfully encode physical laws, yet translating conceptual insight into reliable code remains a major bottleneck.
arXiv AI
Jun 12
Benchmarking AI Agents for Addressing Scientific Challenges Across Scales
arXiv:2606. 12736v1 Announce Type: new Abstract: AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood.
arXiv AI
Jul 14
GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization
arXiv:2607. 10127v1 Announce Type: cross Abstract: Evolutionary program search guided by Large Language Models (LLMs) has emerged as a powerful paradigm for automated scientific discovery.
arXiv AI
Jul 24
SciExplore: Evaluating Autonomous Agents from Scientific Navigation to Information Integration
arXiv:2607. 20926v1 Announce Type: new Abstract: Scientific research involves complex information-seeking and reasoning workflows across heterogeneous sources.
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.