The paper reports on closed‑loop autonomous synchrotron X‑ray experiments performed on nanoparticle superlattices using a coding‑agent infrastructure. It presents detailed human‑agent and agent‑agent transcripts that allow an audit of decision provenance and the division of cognitive labor. Across three experiments, the study quantifies the proportion of messages involving interpretation, judgment, methodological input, and error corrections, showing how contributions shift as the experiments progress.
arXiv:2607. 09195v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly expected to play a central role in AI-driven scientific discovery.
arXiv:2606. 13662v1 Announce Type: new Abstract: LLM-based agents have shown increasing potential in automating scientific discovery.
arXiv:2608. 13331v1 Announce Type: cross Abstract: The replicability of papers is a cornerstone of scientific knowledge, ensuring the reliability of existing results and providing a base for further experiments.
arXiv:2607. 15079v1 Announce Type: new Abstract: Understanding the brain increasingly depends on integrating evidence across scales, modalities, and disciplines.
arXiv:2606. 12834v1 Announce Type: new Abstract: As scientific workflows shift from deterministic executables to LLM-based agents, the development practices on offer, such as fine-tuning, reinforcement learning, and prompt-and-go, bury the scientist's judgment.
arXiv:2608. 14667v1 Announce Type: new Abstract: Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the autonomous capabilities of "AI Scientists".
Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code generation, data analysis, hypothesis proposal, and model criticism. We argue that this transition is qualitative rather than incremental, and that suitably designed multi-agent systems may evolve from passive computational tools into ``AI scientists'' that can expand the hypothesis-generating and verification capacity of science.
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...
PAPERBENCHX is a new benchmark that tests whether AI can reproduce scientific experiments across 12 disciplines, including electromagnetics, photonics, chemistry, materials science, biology, and robotics. It consists of 93 expert-constructed tasks, each providing a source paper, a public specification, and a domain-native scientific environment. The benchmark evaluates AI agents by having them reconstruct, execute, and diagnose key experiments, then replay submissions from scratch to score regenerated evidence against expert-validated criteria, revealing that current AI configurations fully reproduce only 14% of tasks.
arXiv:2604. 17406v3 Announce Type: replace Abstract: The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science.
The paper introduces the Science Prediction Universe (SPU), a benchmark comprising 100 public verified tasks and 16 private wet‑lab tasks across chemistry, materials science, biology, and medicine, designed to test agents’ ability to predict experimental outcomes. Thirteen agent configurations using twelve frontier models were evaluated, revealing significant performance gaps: Spearman correlations ranged from 0.227 to 0.478 on the public set. Analysis of over a thousand trajectories indicates that current agents struggle more with scientific judgment than tool execution, often relying on shared priors and trivial proxies rather than accurate predictions.
arXiv:2607. 26064v1 Announce Type: cross Abstract: AI systems are becoming autonomous research agents that generate hypotheses, design experiments, and produce discoveries at scales beyond human oversight.