Research Automation

TL;DR for operators aiXiv is not mainly a claim that AI scientists are ready to flood the world with publishable research and we should all politely applaud the machines. It is more interesting than that, and less comforting. The paper proposes an infrastructure layer for AI-generated science: structured submission, automated review, retrieval-grounded feedback, revision loops, pairwise comparison, prompt-injection detection, multi-model voting, provisional acceptance, DOI-style publication, APIs, MCP interfaces, and public discussion.1 ...

TL;DR for operators Lab work is mostly not white coats and dramatic discoveries. It is protocol design, ethics paperwork, recruitment settings, data cleaning, model diagnostics, figure formatting, reference checking, and the slow discovery that your beautiful hypothesis has politely declined to exist. That is what makes this paper interesting. Virtuous Machines: Towards Artificial General Science presents an agentic AI system that did not merely write a speculative research proposal. It designed and executed an online human-participant experiment, collected data through Prolific and Pavlovia, analysed the results, produced figures and tables, wrote manuscripts, and ran peer-style review over the outputs.1 ...

TL;DR for operators STELLA is not interesting because it calls itself a “self-evolving scientist”. The internet has suffered enough from ambitious nouns. It is interesting because it attacks a real operational bottleneck in biomedical research: the best answer often requires not just reasoning, but finding the right database, building the right analysis environment, running code, checking intermediate results, and deciding when the current workflow is inadequate. ...