RIDAR is a framework designed to detect and mitigate research integrity violations (RIVs) in autonomous research agents. It introduces RIVT, a taxonomy of 14 RIV types, and offers two components: RIDAR-post-hoc, which identifies RIVs in agent trajectories with 85.2% F1 on a benchmark, and RIDAR-runtime, which dynamically corrects RIVs during execution, reducing remaining violations by 36.8%. The authors applied RIDAR to large datasets, uncovering 3,964 RIV instances in 99.7% of 300 agent trajectories across 50 tasks, highlighting the prevalence of RIVs in current agents.
arXiv:2610.31372v2 Announce Type: replace Abstract: Autonomous research agents are increasingly used to automate large portions of scientific research, yet they are prone to research integrity violat...
arXiv:2610.19679v1 Announce Type: cross Abstract: Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in completing complex tasks through multi-step reasoning and tool i...
arXiv:2610.32441v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly capable of supporting scientific workflows, but their capabilities across diverse research activitie...
arXiv:2606. 31651v1 Announce Type: new Abstract: Recent automated research systems show that language-model agents can generate hypotheses, run experiments, and write complete manuscripts, but most evidence still comes from selected examples, human-framed topics, or a few pre-defined research tasks.
arXiv:2409. 11363v2 Announce Type: replace-cross Abstract: AI agents have the potential to aid users on a variety of consequential tasks, including conducting scientific research.
arXiv:2606. 02060v1 Announce Type: new Abstract: Deep-research agents solve tasks through long trajectories of search, tool use, evidence inspection, and answer synthesis.
arXiv:2602. 02905v2 Announce Type: replace Abstract: Autonomous agents powered by large language models (LLMs) promise to accelerate scientific discovery end-to-end, but rigorously evaluating their capacity for verifiable discovery remains a central challenge.
Recent automated research systems show that language-model agents can generate hypotheses, run experiments, and write complete manuscripts, but most evidence still comes from selected examples, human-framed topics, or a few pre-defined research tasks. We present FARS (Fully Automated Research System), a fully automated AI-for-AI research system designed to operate across research topics at scale.
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: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.
The paper introduces Continual Search, an iterative framework that guides large language models to persistently search for diagnostic evidence in long AI agent execution logs, addressing the limitations of one-shot judgments. Evaluated on four existing RCA benchmarks and a new large-scale dataset called MegaRCA-Mix, Continual Search consistently boosts attribution performance, achieving a 40% F1 improvement for GPT‑5.5 on MegaRCA‑Mix. The results show that effective search can outweigh raw model scale, enabling lower-tier models to outperform higher-tier ones in root‑cause attribution tasks.
arXiv:2606. 07054v1 Announce Type: cross Abstract: Autonomous LLM agents can pursue hidden malicious objectives through sequences of individually benign actions, making sabotage difficult to detect using standard trajectory-level monitoring.