The paper "How Far Are We From True Auto-Research?" introduces ResearchArena, a lightweight scaffold that lets off‑the‑shelf agents (Claude Code, Codex, and Kimi Code) complete the full research cycle—ideation, experimentation, writing, and self‑refinement—across 13 computer science topics. In 39 trials, the agents produced 117 papers that were evaluated by three review methods: manuscript‑only review (SAR), artifact‑aware peer review (PR), and a human meta‑review. While SAR scores suggested competitive manuscript quality, PR revealed significant experimental shortcomings, with fabricated results, underpowered experiments, and plan/execution mismatches varying widely among agents; none of the papers met top‑tier venue acceptance thresholds, indicating a substantial gap to true auto‑research.
arXiv:2608. 05179v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly used across the scientific research lifecycle: ideation, literature search, experiment design and execution, analysis, manuscript drafting, and review.
PaperDoctor is an agent framework that provides evidence‑grounded, actionable feedback for scientific papers before submission. It evaluates writing, layout, references, code, theory, prior work, and experiments through a three‑layer hierarchical system, linking each critique to specific evidence and revision suggestions. The system selectively rebuilds and reruns experiments to uncover reproducibility gaps, and an interactive interface lets authors explore findings tied to their manuscript.
arXiv:2607. 27191v1 Announce Type: cross Abstract: Forecasts of explosive AI progress hinge on AI agents automating AI research.
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.
CompLit is a new literature search benchmark covering all eight arXiv domains, designed to evaluate agents on three diagnostic settings: implicit, cumulative, and unmet needs. The benchmark shows that top agents like Codex and Claude Code perform well on standard queries (86‑87%) but drop to 66‑68% on implicit queries, lose additional accuracy when requirements accumulate, and abstain on up to 53% of unmet queries. The study finds that agents often locate the correct or near‑correct paper but misjudge it, indicating a need for more reliable search capabilities.
arXiv:2610.29812v1 Announce Type: cross Abstract: Literature-searching language agents are becoming a standard component of scientific assistants for the life sciences, yet there is little controlled...
arXiv:2607. 02520v1 Announce Type: cross Abstract: Automated research agents increasingly generate code, retrieve literature, and draft scientific artifacts, but they often fail to verify whether generated experiments execute correctly or whether cited sources support generated claims.
arXiv:2606. 31478v1 Announce Type: new Abstract: Autonomous research agents can now draft hypotheses, write code, run experiments, and produce papers, but they remain brittle when experiments fail.
arXiv:2608.31076v1 Announce Type: cross Abstract: Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experi...
arXiv:2607. 28631v1 Announce Type: new Abstract: AI Scientist systems capable of autonomous research have the potential to significantly accelerate scientific discovery.
arXiv:2606. 07462v1 Announce Type: new Abstract: As foundation models advance and agent scaffolding becomes increasingly sophisticated, agents have demonstrated remarkable proficiency in complex, long-horizon coding tasks and even autonomous experiment execution.
arXiv:2609.22111v1 Announce Type: new Abstract: Large language model agents are increasingly capable of conducting research autonomously, producing research documents alongside the code and experimen...