arXiv:2606. 30246v1 Announce Type: new Abstract: Existing autonomous research agents can support parts of the research process, but most systems still treat research as either an isolated assistant task or a closed workflow.
arXiv:2607. 16038v1 Announce Type: new Abstract: Scientific work increasingly spans heterogeneous artifacts -- papers, code, datasets, scientific file formats, model outputs, figures, manuscripts, and team decisions -- yet general-purpose AI assistants rarely preserve these objects as a coherent, auditable research state.
arXiv:2606. 13662v1 Announce Type: new Abstract: LLM-based agents have shown increasing potential in automating scientific discovery.
arXiv:2605. 18661v2 Announce Type: replace Abstract: AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents can execute experiments, draft manuscripts, and simulate critique with minimal human input.
arXiv:2607. 13220v1 Announce Type: new Abstract: Most AI-for-science systems focus on scaling a single reasoning process through better models, larger context windows, long-horizon agentic execution, or digital co-scientists working with one principal user.
arXiv:2607. 02703v1 Announce Type: cross Abstract: In this paper, we describe LLMoxie, an institutional AI platform whose three-tiered architecture supports multi-cloud and on-premise inference, a LiteLLM/MLflow control plane for authentication, budgeting, PII masking, and observability, and an application augmentation layer for AI coding agents.
arXiv:2607. 08043v1 Announce Type: cross Abstract: Research software collaborations span meetings, informal chats, pull requests, and GitHub issues.
arXiv:2606. 10402v1 Announce Type: cross Abstract: Scientific discovery is often a collective process: researchers share partial results, inspect failed attempts, and build on each other's ideas over long time horizons.
arXiv:2606. 15497v1 Announce Type: new Abstract: The automation of science is a long-standing ambition in the field of AI.
OpenAI4S is an open‑source scientific research agent that treats code as action and science as sessions, combining a persistent computing runtime with structured session management. It uses tool calls for orchestration, executes code cells in persistent Python and R kernels, and records an append‑only Action Ledger, per‑cell execution logs, versioned artifacts, environment snapshots, and workspace checkpoints to preserve provenance and enable session recovery, branching, and extension. Evaluated on 36 research scenarios—including retrosynthesis, molecular dynamics, and protein design—OpenAI4S achieved a higher overall score (7.83) than a general‑purpose coding harness, especially on long‑horizon, computation‑intensive workflows, though reproducibility remains an open challenge. whyItMatters":"The system demonstrates that persistent execution coupled with session‑level provenance can enhance the reliability of AI‑assisted scientific workflows, as evidenced by its superior performance across diverse research scenarios."
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:2603. 21489v2 Announce Type: replace-cross Abstract: AI agents have become increasingly capable at isolated software engineering (SWE) tasks such as resolving issues on Github.
arXiv:2602. 11354v3 Announce Type: replace Abstract: The literature has witnessed an emerging interest in AI agents for automated assessment of scientific papers.