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.
Self-Building Labs
The paper introduces the self‑building laboratory (SBL), an autonomous system that designs, configures, and operates its own laboratory setup based on a high‑level scientific goal. Unlike existing self‑driving laboratories that can only run pre‑built procedures, the SBL starts from any existing environment—whether empty or partially equipped—and builds missing capabilities such as lab layout, digital twin, robot skills, and experimental workflows. The authors demonstrate the system across multiple robots and setups, including a real‑world quantum‑dot synthesis experiment, showing that researchers can shift from manual configuration to simply specifying scientific objectives.
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arXiv AI
Jul 7
Agentic Artificial Intelligence for Multistage Physics Experiments at a Large-Scale User Facility Particle Accelerator
arXiv:2509. 17255v2 Announce Type: replace-cross Abstract: We present the first language-model-driven agentic artificial intelligence (AI) system to autonomously execute multi-stage physics experiments on a production synchrotron light source.
arXiv AI
Jul 28
Stress-testing large language model agents in a robotic chemistry laboratory
arXiv:2607. 23045v1 Announce Type: new Abstract: AI is evaluated through knowledge, reasoning and plan generation, yet scientific agency requires reliable physical action and adaptation to evidence.
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
Jul 1
A Self-Evolving Agentic System for Automated Generation and Execution of Biological Protocols
arXiv:2606. 31763v1 Announce Type: new Abstract: Autonomous wet-lab experimentation requires more than plausible protocol text: biological intent, quantitative procedures, device constraints and experimental feedback must remain aligned from protocol and SOP design to code and physical execution.
arXiv Machine Learning
Sep 25
LabFactory: Building and Evaluating Executable AI Labs
LabFactory is a framework that transforms a scientific brief into an executable AI lab, integrating models, knowledge resources, tools, and a controller behind a fixed interface. The builder packages the lab in a metered workspace, and a separate host evaluates the delivered artifact on held‑out inputs, ensuring the system itself is the evaluation target. Across 28 constructions in seven scientific domains, the delivered labs surpassed reference values on all 33 subtests, demonstrating that an AI agent can fully realize a scientific brief into a working, inspectable lab.
arXiv AI
Aug 17
The Past and Future of AI Scientists
arXiv:2608. 14407v1 Announce Type: new Abstract: We present a survey of the past and future of AI Scientists: machines capable of automating science.
arXiv AI
Sep 16
Evaluating Verified Autonomy in Quantum Engineering
The paper introduces Quantum‑Harbor, a virtual laboratory that lets AI agents interact with quantum systems in a controlled setting, enabling verification of their actions and conclusions. Using this platform, the authors created QIQCBench, a benchmark of 49 expert‑authored tasks covering calibration, control, error correction, compilation, sensing, and networking. Testing 17 state‑of‑the‑art agentic systems on QIQCBench revealed wide variation in verified performance, highlighting a gap between demonstrated capability and reliable operation and positioning Quantum‑Harbor as a foundation for measuring progress toward verified autonomy in quantum engineering.
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
Jun 12
LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories
arXiv:2606. 13578v1 Announce Type: cross Abstract: Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach.
arXiv AI
Jun 19
Dual-Agent Framework for Cross-Model Verified Translation of Natural-Language Protocols into Robotic Laboratory Platform
arXiv:2606. 20120v1 Announce Type: cross Abstract: Biological experiment protocols are written in natural language, whereas automation systems rely on predefined control commands, creating a semantic gap that limits autonomous execution.
arXiv AI
Oct 28
Making Self-Driving Laboratories Auditable:A Unified Framework for Hybrid Actions,Measurements, and Safe Execution
The paper introduces a unified framework for self‑driving laboratories (SDLs) that integrates experimental planning, automated execution, and measurement feedback. It comprises four components: LAM, a measurement‑conditioned policy that generates robot trajectories and typed instrument API calls; OpenSDL, an execution substrate that maps operations to heterogeneous devices and records causal events; Safe‑SDL, a runtime gate that performs pre‑dispatch checks and monitors execution with signal temporal logic; and L3‑Bench, a benchmark protocol evaluating physical outcomes, timing, reproducibility, safety, and fault recovery. Together, these elements aim to address coordination and traceability challenges in SDL workflows.