arXiv:2607.22375v2 Announce Type: replace Abstract: Large Language Models (LLMs) have significantly advanced the automation of scientific discovery. However, existing systems share a core limitation:...
arXiv:2607. 28631v1 Announce Type: new Abstract: AI Scientist systems capable of autonomous research have the potential to significantly accelerate scientific discovery.
arXiv:2609.38445v1 Announce Type: new Abstract: Frontier LLMs are increasingly used to automate scientific research through iterative search. We distinguish idea-driven search from solution-driven se...
arXiv:2607. 04439v1 Announce Type: new Abstract: Large language models have made research ideation increasingly accessible, yet effective idea development requires more than generating candidate directions.
Ideation Arena is a battle-style platform that evaluates research ideas generated by large language models (LLMs) and research agents through pairwise human assessment. The system builds shared literature contexts, collects over 6,000 double-blind comparisons from 105 computer science researchers, and constructs an Elo rating leaderboard to rank proposal-stage expert preferences. It also introduces Ideation Arena Eval, a benchmark to test whether automated evaluators align with human preferences, finding that current LLM judges achieve at best 72.56% Soft Accuracy on overall quality.
arXiv:2609.07611v1 Announce Type: new Abstract: Scientific ideation is the capacity to formulate novel and testable hypotheses from scientific evidence, and autonomous AI scientists depend on it. Exi...
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.
The paper introduces ClaimNov, a claim‑level novelty detection module designed to improve AI scientific discovery pipelines. By evaluating novelty at the proposal and claim levels using four datasets of 1,060 proposals, the authors show that existing checks either reject too many novel ideas or fail to isolate non‑novel parts of a proposal. ClaimNov achieves a 97% detection rate of disguised papers with only a 1.5% false‑alarm rate, significantly reducing false positives compared to prior methods and preserving proposals that build on established work.
arXiv:2606. 25198v2 Announce Type: replace Abstract: Autonomous AI Research promises to accelerate the scientific progress of machine learning.
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.
Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experimentation, and report generation. However, open-end...
Sciπvot is a 248‑case benchmark that evaluates how well large language model (LLM) agents perform scientific judgment during autonomous research. The benchmark is built from six months of real‑world experiments where senior researchers ran LLM agents on 26 projects across six scientific fields and repeatedly corrected the agents’ decisions. In each case, the agent must choose between its original plan and a human‑corrected alternative using only the project record available before the correction, allowing a direct comparison of LLM and human performance under varying reasoning effort and record access.