arXiv AI
SlowLab: Benchmarking Autonomous AI Agents on a Year of Experiments They Cannot Undo
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arXiv AI
Sep 7
La Agente \'Optima: Towards Agentic Self-Driving Laboratories
La Agente ’Optima is an agentic framework that builds and manages Bayesian optimization campaigns for self‑driving laboratories, separating large language model reasoning from campaign execution. It maintains a persistent optimization state, allowing consistent repetitive loops and auditable decisions, and only returns control to the agent when interpretation or revision is needed. In tests on digital discovery tasks and physical platforms, it corrected measurement failures, improved yields, and recommended formulation changes, outperforming human‑directed campaigns in cost and material usage.
arXiv AI
Jul 15
Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhouses
arXiv:2607. 11959v1 Announce Type: new Abstract: Greenhouse reinforcement learning can test climate-control ideas at a speed and scale that is difficult to achieve with crop experiments alone.
arXiv AI
Jul 7
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.
arXiv AI
Oct 15
Autonomous Science Bench: Evaluating AI Agents for Autonomous Scientific Discovery
arXiv:2610.18449v1 Announce Type: new Abstract: AI agents for autonomous scientific discovery must decide which experiments to run under a limited budget. Most benchmarks for science agents provide f...
Hugging Face Trending Papers
Oct 13
Autonomous Science Bench: Evaluating AI Agents for Autonomous Scientific Discovery
The paper introduces Autonomous Science Bench, a benchmark for AI agents that must choose experiments under a limited budget. It features six real-world design-build-test-learn campaigns in biology, chemistry, and materials science, where agents select design batches, receive lab measurements, and iteratively refine a predictive model. Success is measured by two calibrated gates—discovery and learning—highlighting distinct agent weaknesses that overall success rates alone would mask.
arXiv AI
Sep 23
Agentic Explainable Artificial Intelligence (Agentic XAI) Approach To Explore Better Explanation: A Case Study in Decision Support for Rice Cultivation in Japan
arXiv:2512. 21066v4 Announce Type: replace Abstract: Explainable artificial intelligence (XAI) reveals how explanatory variables relate to a response variable, yet communicating XAI outputs to laypersons remains difficult, limiting trust in AI-based predictions.