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.
By Jianyuan Zhong, Chenwei Wu, Zhaochen Yu, Xixian Jiang, Xuewei Li, Hao Xiao, Xilin Dai, Junyang Wang, Jiaru Zou, Zhiqing Cui, Boyuan Zheng, Jing Xiong, Zhen Zhang, Qiang Gao, Michael Morley, Luis Nakayama, Qiang Xu, Liyue Shen, Tao Gui, Leo Anthony Celi, James Zou, Zhenfei Yin, Yingcheng Wu, Ling Yang
arXiv:2607. 01131v1 Announce Type: cross Abstract: Autonomous scientific discovery systems offer the potential to accelerate research by automating the process of hypothesis generation and validation.
By Bingchen Zhao, Sara Beery, Oisin Mac Aodha
The paper introduces OmicsBench, a new reasoning benchmark for multi‑omics sequences that includes 1,160 expert‑validated questions across DNA regulation, RNA processing, and protein function tasks, requiring traceable evidence chains. Evaluation of 17 large language models shows that scientific LLMs, while more accurate in classification, often lack valid evidence, suggesting shortcut learning. To address this, the authors propose tool‑augmented on‑policy distillation (TA‑OPD), a post‑training method that improves both evidence grounding and predictive performance across five Qwen3.5 models of varying sizes.
By Jie Ying, Zhefan Wang, Zihong Chen, Zhengqing Li, Jinzhe Li, Gang Li, Jian Liu, Fang Hu, Tao Luo, Zhonghang Yuan, Wanli Ouyang, Stan Z. Li, Fan Yang, Nanqing Dong
arXiv:2606. 08532v5 Announce Type: replace Abstract: Modern artificial intelligence excels at prediction but cannot explain.
By Lei Lin, Xinlong Pan, Ronghao Wang, Chunbao Zhou, Jue Wang, Yangang Wang, Ivana Rasovska
arXiv:2608. 13558v1 Announce Type: new Abstract: Recent advances in foundation models have enabled AI scientists to automate increasingly complete research workflows, from hypothesis generation and code execution to manuscript preparation.
By Bobo Li, Hao Fei, Tianjie Ju, Mong-Li Lee, Wynne Hsu
The paper introduces HypoKG, a unified biochemical knowledge graph built from KEGG, Rhea, and UniProt, and uses it to benchmark 13,200 biomedical hypotheses generated by six large language models (LLMs). By varying the biological information provided—source enzyme only, full biological path, or source and disease endpoint—the study finds that LLMs produce higher-scoring hypotheses when given minimal information, but these are less evidence‑grounded. When supplied with the full biological path, the models generate hypotheses that align more closely with known mechanistic relationships, a phenomenon the authors term evidence‑disciplined reasoning, which is confirmed by shuffling intermediate path steps.
"whyItMatters":"The study demonstrates that knowledge graphs can both uncover novel disease–enzyme pairs and guide LLMs to reason more accurately from evidence, improving the reliability of AI‑generated biomedical hypotheses."
By Dominic Okonkwo, Adetayo Okunoye, Ismailcem Budak Arpinar