cs.AI, q-bio.NC updates on arXiv.org
Language Ranker: A Lightweight Ranking framework for LLM Decoding Chenheng Zhang · Tianqi Du · Jizhe Zhang · Mingqing Xiao · Yifei Wang · Yisen Wang · Zhouchen Lin
arXiv:2510.21883v1 Announce Type: cross Abstract: Conventional research on large language models (LLMs) has primarily focused on refining output distributions, while paying less attention to the decoding process that transforms these distributions into final responses. Recent advances, such as scaling the computation of inference time with reward models, have underscored the importance of decoding, but these methods often suffer from high computational costs and limited applicability. In this p
28 October 2025 at 12:00

Language Ranker: A Lightweight Ranking framework for LLM Decoding

By: Chenheng Zhang · Tianqi Du · Jizhe Zhang · Mingqing Xiao · Yifei Wang · Yisen Wang · Zhouchen Lin

28 October 2025 at 12:00

arXiv:2510.21883v1 Announce Type: cross Abstract: Conventional research on large language models (LLMs) has primarily focused on refining output distributions, while paying less attention to the decoding process that transforms these distributions into final responses. Recent advances, such as scaling the computation of inference time with reward models, have underscored the importance of decoding, but these methods often suffer from high computational costs and limited applicability. In this paper, we revisit LLM generation through the lens of recommender systems, conceptualizing the decoding process as analogous to the ranking stage in recommendation pipelines. From this perspective, we observe that both traditional decoding methods and reward models exhibit clear limitations such as redundancy. Motivated by this insight, we propose Language Ranker, a novel framework that introduces a lightweight module to rerank candidate responses using features extracted by the base model. Experiments across a wide range of tasks show that Language Ranker achieves performance comparable to large-scale reward models, while requiring only

Omics In Lung
Cell-free epigenomes enhanced fragmentomics-based model for early detection of lung cancer Yadong Wang · Qiang Guo · Zhicheng Huang · Liyang Song · Fei Zhao · Tiantian Gu · Zhe Feng · Haibo Wang · Bowen Li · Daoyun Wang · Bin Zhou · Chao Guo · Yuan Xu · Yang Song · Zhibo Zheng · Zhongxing Bing · Haochen Li · Xiaoqing Yu · Ka Luk Fung · Heqing Xu · Jianhong Shi · Meng Chen · Shuai Hong · Haoxuan Jin · Shiyuan Tong · Sibo Zhu · Chen Zhu · Jinlei Song · Jing Liu · Shanqing Li · Hefei Li · Xueguang Sun · Naixin Liang
Clin Transl Med. 2025 Feb;15(2):e70225. doi: 10.1002/ctm2.70225.ABSTRACTBACKGROUND: Lung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung ca
5 February 2025 at 19:00

Cell-free epigenomes enhanced fragmentomics-based model for early detection of lung cancer

Omics In Lung

By: Yadong Wang · Qiang Guo · Zhicheng Huang · Liyang Song · Fei Zhao · Tiantian Gu · Zhe Feng · Haibo Wang · Bowen Li · Daoyun Wang · Bin Zhou · Chao Guo · Yuan Xu · Yang Song · Zhibo Zheng · Zhongxing Bing · Haochen Li · Xiaoqing Yu · Ka Luk Fung · Heqing Xu · Jianhong Shi · Meng Chen · Shuai Hong · Haoxuan Jin · Shiyuan Tong · Sibo Zhu · Chen Zhu · Jinlei Song · Jing Liu · Shanqing Li · Hefei Li · Xueguang Sun · Naixin Liang

5 February 2025 at 19:00

Clin Transl Med. 2025 Feb;15(2):e70225. doi: 10.1002/ctm2.70225.

ABSTRACT

BACKGROUND: Lung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model.

METHODS: To address this issue, a multi-centre prospective cohort study was conducted, with participants harbouring suspicious malignant lung nodules and healthy volunteers recruited from two clinical centres. Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low-pass whole-genome sequencing. Machine learning algorithms were then employed to integrate the multi-omics data, aiding in the development of a precise lung cancer detection model.

RESULTS: Cancer-related changes in cfDNA fragmentomics were significantly enriched in specific genes marked by cell-free epigenomes. A total of 609 genes were identified, and the corresponding cfDNA fragmentomic features were utilised to construct the ensemble model. This model achieved a sensitivity of 90.4% and a specificity of 83.1%, with an AUC of 0.94 in the independent validation set. Notably, the model demonstrated exceptional sensitivity for stage I lung cancer cases, achieving 95.1%. It also showed remarkable performance in detecting minimally invasive adenocarcinoma, with a sensitivity of 96.2%, highlighting its potential for early detection in clinical settings.

CONCLUSIONS: With feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics-based machine learning model demonstrated outstanding performance in the independent validation cohort. These findings highlight its potential as an effective non-invasive strategy for the early detection of lung cancer.

KEYPOINTS: Our study elucidated the regulatory relationships between epigenetic modifications and their effects on fragmentomic features. Identifying epigenetically regulated genes provided a critical foundation for developing the cfDNA fragmentomics-based machine learning model. The model demonstrated exceptional clinical performance, highlighting its substantial potential for translational application in clinical practice.

PMID:39909829 | PMC:PMC11798665 | DOI:10.1002/ctm2.70225