arXiv:2502. 18864v2 Announce Type: replace Abstract: Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation.
The paper introduces CellForge, a team of autonomous agents that can autonomously generate, train, and validate predictive models for single‑cell perturbation data across transcriptomic, epigenomic, and proteomic contexts. By surveying literature, parsing data, and engaging in critic‑mediated discussions among specialized agents, CellForge produces end‑to‑end model architectures and code without human specification, achieving performance competitive with state‑of‑the‑art hand‑crafted methods. Human evaluators found CellForge’s research plans to be more scientifically sound and innovative than those produced by single large language models or deep‑research systems.
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.
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...
The paper introduces SEER‑Repro, a benchmark of 20 SEER‑based studies with data and resources for reproduction. An LLM agent reproduces 77.4% of 2,190 atomic claims and provides executable baselines. An agentic analysis pipeline then tests robustness by identifying analytical choices, proposing alternatives, and rerunning analyses, flagging potentially consequential choices in 9.5% of fully reproduced findings for expert review.
arXiv:2609.33357v2 Announce Type: replace Abstract: Reliable automated research requires agents to vet data, verify analyses, and generate hypotheses grounded in trustworthy evidence, potentially red...
The paper introduces DiscoveryRSI, a framework that recursively learns to generate valuable scientific hypotheses by building hypothesis trees through multi‑agent evolutionary search, compiling investigations into executable tasks, and refining the process via credit optimization, replay, and rediscovery. It also presents DFM‑Bench, a benchmark for assessing hypothesis quality across multiple dimensions. Experiments in four life‑science domains show that the system improves hypothesis quality, particularly in falsifiability and data resolvability, and outperforms other large‑language‑model agents in expert evaluations.
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. 16262v1 Announce Type: cross Abstract: The acceleration of automated scientific discovery has been fundamentally bottlenecked by the epistemic gap between the semantic reasoning of large language models (LLMs) and the deterministic physics of mammalian biology.
arXiv:2607. 09195v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly expected to play a central role in AI-driven scientific discovery.
arXiv:2607. 05456v1 Announce Type: new Abstract: While recent advances in large language models have enabled end-to-end automated manuscript generation, existing systems suffer from three critical deficiencies: (i) generated claims are not deterministically grounded in verifiable literature, (ii) experimental results are frequently fabricated rather than executed, and (iii) there exists no standardized, multi-dimensional framework to assess whether AI-generated manuscripts meet the quality and rigor required for real-world publication.
The paper introduces an LLM-as-a-Judge framework for evaluating the outputs of an agentic drug discovery assistant, ChatInvent, deployed at AstraZeneca. It defines four quality dimensions—Completeness, Relevancy, Structural Clarity, and Scope Adherence—alongside deterministic Tool Call Correctness checks, and validates the judge against five expert annotators. After optimizing the best-performing judge with few-shot demonstrations, alignment with human majority votes improves from 0.80 to 0.86, and the framework reveals that informal question phrasing does not degrade output quality.