SciEvalKit: An Open-source Evaluation Toolkit for Scientific General Intelligence

By: Yiheng Wang · Yixin Chen · Shuo Li · Yifan Zhou · Bo Liu · Hengjian Gao · Jiakang Yuan · Jia Bu · Wanghan Xu · Yuhao Zhou · Xiangyu Zhao · Zhiwang Zhou · Fengxiang Wang · Haodong Duan · Songyang Zhang · Jun Yao · Han Deng · Yizhou Wang · Jiabei Xiao · Jiaqi Liu · Encheng Su · Yujie Liu · Weida Wang · Junchi Yao · Shenghe Zheng · Haoran Sun · Runmin Ma · Xiangchao Yan · Bo Zhang · Dongzhan Zhou · Shufei Zhang · Peng Ye · Xiaosong Wang · Shixiang Tang · Wenlong Zhang · Lei Bai

30 December 2025 at 13:00

arXiv:2512.22334v1 Announce Type: new Abstract: We introduce SciEvalKit, a unified benchmarking toolkit designed to evaluate AI models for science across a broad range of scientific disciplines and task capabilities. Unlike general-purpose evaluation platforms, SciEvalKit focuses on the core competencies of scientific intelligence, including Scientific Multimodal Perception, Scientific Multimodal Reasoning, Scientific Multimodal Understanding, Scientific Symbolic Reasoning, Scientific Code Generation, Science Hypothesis Generation and Scientific Knowledge Understanding. It supports six major scientific domains, spanning from physics and chemistry to astronomy and materials science. SciEvalKit builds a foundation of expert-grade scientific benchmarks, curated from real-world, domain-specific datasets, ensuring that tasks reflect authentic scientific challenges. The toolkit features a flexible, extensible evaluation pipeline that enables batch evaluation across models and datasets, supports custom model and dataset integration, and provides transparent, reproducible, and comparable results. By bridging capability-based evaluation and disciplinary diversity, SciEvalKit offers a standardized yet customizable infrastructure to benchmark the next generation of scientific foundation models and intelligent agents. The toolkit is open-sourced and actively maintained to foster community-driven development and progress in AI4Science.

Precision Oncology: Current Landscape, Emerging Trends, Challenges, and Future Perspectives

MRD

By: Diane Qiao · Richard C Wang · Zhixiang Wang

26 November 2025 at 19:00

Cells. 2025 Nov 17;14(22):1804. doi: 10.3390/cells14221804.

ABSTRACT

Precision oncology is broadly defined as cancer prevention, diagnosis, and treatment specifically tailored to the patient based on his/her genetics and molecular profile. In simple terms, the goal of precision medicine is to deliver the right cancer treatment to the right patient, at the right dose, at the right time. Precision oncology is the most studied and widely applied subarea of precision medicine. Now, precision oncology has expanded to include modern technology (big data, single-cell spatial multiomics, molecular imaging, liquid biopsy, CRISPR gene editing, stem cells, organoids), a deeper understanding of cancer biology (driver cancer genes, single nucleotide polymorphism, cancer initiation, intratumor heterogeneity, tumor microenvironment ecosystem, pan-cancer), cancer stratification (subtyping of traditionally defined cancer types and pan-cancer re-classification based on shared properties across traditionally defined cancer types), clinical applications (cancer prevention, early detection, diagnosis, targeted therapy, minimal residual disease monitoring, managing drug resistance), lifestyle changes (physical activity, smoking, alcohol consumption, sunscreen), cost management, public policy, and more. Despite being the most developed area in precision medicine, precision oncology is still in its early stages and faces multiple challenges that need to be overcome for its successful implementation. In this review, we examine the history, development, and future directions of precision oncology by focusing on emerging technology, novel concepts and principles, molecular cancer stratification, and clinical applications.

PMID:41294857 | PMC:PMC12651332 | DOI:10.3390/cells14221804

When Models Outthink Their Safety: Mitigating Self-Jailbreak in Large Reasoning Models with Chain-of-Guardrails

cs.AI, q-bio.NC updates on arXiv.org

27 October 2025 at 12:00

arXiv:2510.21285v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) demonstrate remarkable capabilities on complex reasoning tasks but remain vulnerable to severe safety risks, including harmful content generation and jailbreak attacks. Existing mitigation strategies rely on injecting heuristic safety signals during training, which often suppress reasoning ability and fail to resolve the safety-reasoning trade-off. To systematically investigate this issue, we analyze the reasoning trajectories of diverse LRMs and uncover a phenomenon we term Self-Jailbreak, where models override their own risk assessments and justify responding to unsafe prompts. This finding reveals that LRMs inherently possess the ability to reject unsafe queries, but this ability is compromised, resulting in harmful outputs. Building on these insights, we propose the Chain-of-Guardrail (CoG), a training framework that recomposes or backtracks unsafe reasoning steps, steering the model back onto safe trajectories while preserving valid reasoning chains. Extensive experiments across multiple reasoning and safety benchmarks demonstrate that CoG substantially improves the safety of current LRMs while preserving comparable reasoning ability, significantly outperforming prior methods that suffer from severe safety-reasoning trade-offs.

GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer

Omics In Lung

By: Zijian Zhang · Xiang Wang · Chenyang Li · Robert Taylor Ripley · Jia Wu · Jianjun Zhang · Christopher I Amos · Chao Cheng

5 September 2025 at 18:00

bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.

ABSTRACT

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519

GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Zijian Zhang · Xiang Wang · Chenyang Li · Robert Taylor Ripley · Jia Wu · Jianjun Zhang · Christopher I Amos · Chao Cheng

5 September 2025 at 18:00

bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.

ABSTRACT

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519

Genetic, DNA methylation, and immune profile discrepancies between early-stage single primary lung cancer and synchronous multiple primary lung cancer

Most Recent Articles: Clinical Epigenetics

By: Fenglei Yu， Xiaojie Huang， Danting Zhou， Zhenyu Zhao， Fang Wu， Banglun Qian， Qiang Wang， Juan Chen， Qingchun Liang， Yi Jiang， Qi Ding， Qiongzhi He， Jingqun Tang， Xiang Wang， Wenliang Liu and Chen Chen

7 January 2023 at 08:00

To explore the possible carcinogenesis and help better diagnose and treat patients with synchronous multiple primary lung cancers (sMPLC), we systematically investigated the genetic and DNA methylation profile...

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