arXiv AI
Jun 12
EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery
arXiv:2606. 13662v1 Announce Type: new Abstract: LLM-based agents have shown increasing potential in automating scientific discovery.
Compressing the Validation Bottleneck: An Agentic Self-Driving Lab for Scientific Discovery
arXiv:2607. 04508v1 Announce Type: new Abstract: Agentic AI-for-Science can automate ideation, planning, and analysis, but final validation still depends on real experiments.
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arXiv AI
Jun 9
A case study of evaluating AI agents on a neuroscience data-to-discovery pipeline
arXiv:2606. 07718v1 Announce Type: new Abstract: Agentic AI tools offer a promising path to automating software development bottlenecks in scientific research pipelines, particularly for stages that take domain experts days to months to build, where scientists care about correctness and robustness, not implementation details.
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
Jun 6
AutoDFT: A Closed-Loop Multi-Agent Framework for Autonomous DFT Calculations
arXiv:2605. 26179v2 Announce Type: replace-cross Abstract: Density functional theory (DFT) serves as the basis for computational discovery in materials science and chemistry, yet each calculation demands extensive human effort: adjusting algorithms when convergence stalls, revising plans when unexpected physics emerges, and inserting steps as intermediate results reshape the problem.
arXiv AI
Aug 5
Can LLM design high-quality experiments? A Comprehensive and Systematic Benchmark on Autonomous Experimental Design
arXiv:2608. 03501v1 Announce Type: new Abstract: AI for Research (AI4Research) leverages AI to automate and improve scientific workflows.
arXiv AI
Aug 3
Scaling Scientific Discovery Environments for Turn-Level Agentic RL
arXiv:2607. 28990v1 Announce Type: new Abstract: Large language model agents have shown promising capabilities in data-driven scientific discovery tasks, where an agent interacts with an execution environment and produces a statistical claim.
arXiv AI
2d ago
ML-AutoResearch: Training Machine Learning Research Agents with Automatically Generated Environments
arXiv:2603. 17216v2 Announce Type: replace Abstract: With the advent of AI agents, automated scientific discovery is becoming an increasingly plausible goal.
Hugging Face Trending Papers
Jun 3
AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?
Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifacts. Yet existing benchmarks for frontier models primarily evaluate either single-turn responses or short-horizon agent trajectories, failing to capture the challenges of sustained iterative improvement over extended time horizons.
arXiv AI
Aug 11
Agentic Auto-Research is Fuzz Testing
arXiv:2608. 09855v1 Announce Type: new Abstract: Autonomous research agents can generate experiments faster than researchers can validate them.
arXiv AI
Jul 20
Recursive Harness Self-Improvement
arXiv:2607. 15524v1 Announce Type: cross Abstract: Under model--harness co-evolution, harnesses are not merely inference-time scaffolds but data-generating components whose execution traces can shape future foundation models.
arXiv AI
Jul 1
Agentic-Ideation: Sample Efficient Agentic Trajectories Synthesis for Scientific Ideation Agents
arXiv:2606. 31229v1 Announce Type: new Abstract: Ideation plays a pivotal role in scientific discovery.
Hugging Face Trending Papers
Aug 4
Can LLM design high-quality experiments? A Comprehensive and Systematic Benchmark on Autonomous Experimental Design
AI for Research (AI4Research) leverages AI to automate and improve scientific workflows. While experimental design is a critical stage of the research process, prior work has focused primarily on code implementation and execution, overlooking the importance of this stage, and no benchmark exists to evaluate AI's ability to conduct systematic experiment design.