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Received — 10 September 2026 ⏭

BTBR: A Bayesian-Theory-Driven Probabilistic-Fuzzy Framework for Implicit Bias Removal in Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yongxin Deng (University of Technology Sydney) · Xiaoyu Tan (National University of Singapore) · Jing Pan (Monash University) · Ling Chen (University of Technology Sydney) · Zhen Fang (University of Technology Sydney) · Xihe Qiu (National University of Singapore)
10 September 2026 at 12:00
arXiv:2408.10608v2 Announce Type: replace-cross Abstract: Large language models (LLMs) may encode biased associations from heterogeneous training corpora that are not immediately visible under ordinary prompting, but can surface when the model is steered toward particular demographic personas. Such behavior often manifests not as explicit toxic output, but as systematic performance differences across semantically equivalent tasks, making the resulting bias difficult to detect and mitigate. To address this issue, we formalize the implicit bias problem as persona-induced performance disparity and argue that bias evidence should be treated as a graded signal rather than a binary label. Motivated by this observation, we model biased knowledge as a fuzzy subset equipped with an explicit membership function that reflects the strength of bias evidence for each candidate example. Building on this formulation, we propose Bayesian-Theory-based Bias Removal (BTBR), a hybrid probabilistic-fuzzy framework for identifying and removing latent bias traces from model parameters. BTBR first performs likelihood-ratio screening to measure how strongly candidate samples align with a target biased persona, then converts high-membership samples into structured knowledge triples, and finally applies targeted model editing with a lightweight fuzzy rule scheduler to reduce collateral performance degradation under high entanglement risk. Extensive experiments across multiple bias sources, tasks, model families and editing backends show that BTBR consistently reduces persona-induced performance gaps while preserving general reasoning ability. These results demonstrate that combining probabilistic evidence with fuzzy degree modeling provides an effective and practical approach for mitigating implicit bias in large language models.
Received — 27 May 2026 ⏭

The 2025 lung cancer landscape: advances in screening, molecular taxonomy and therapeutic strategy: a narrative review

✇Pulmonary nodule
By: Wenhai Fu · Xusen Zou · Tianrui He · Qi Cai · Peiling Chen · Bo Cheng · Yi Feng · Caichen Li · Feng Li · Jianfu Li · Huiting Wang · Shan Xiong · Wenjun Ye · Xin Zheng · Jianxing He · Wenhua Liang
15 April 2026 at 18:00

Transl Lung Cancer Res. 2026 Mar 23;15(3):62. doi: 10.21037/tlcr-2025-1-1477. Epub 2026 Mar 18.

ABSTRACT

BACKGROUND AND OBJECTIVE: In 2025, lung cancer research advanced rapidly across the disease continuum, from population-level risk assessment and screening to mechanistic studies of early carcinogenesis and therapeutic innovation in perioperative and metastatic settings. A key shift moved beyond a smoking-centred paradigm toward a multidimensional risk framework reflecting the growing burden among never-smokers and the roles of air pollution, occupational exposures, and systemic metabolic-inflammatory states. This narrative review aims to synthesize influential 2025 evidence across prevention, diagnosis, treatment, and survivorship, and to identify convergent themes and translational gaps relevant to clinical practice and policy.

METHODS: We performed a narrative synthesis of influential lung cancer studies published in major international journals in 2025. Evidence was organized along a clinically oriented pathway spanning carcinogenesis and screening, precision diagnosis, treatment optimization in resectable and advanced disease, and survivorship, emphasizing practice-informing trials, high-impact translational research, and implementation-relevant technologies.

KEY CONTENT AND FINDINGS: Lineage tracing, single-cell and spatial omics, and evolutionary inference refined concepts of field cancerization, clonal selection, and copy-number-driven fitness. In small-cell lung cancer, evidence further supported neuronal coupling and synapse-like programs as potentially tractable vulnerabilities. Clinically, low-dose computed tomography (CT) strategies and data-informed nodule thresholds aimed to balance under-detection against over-surveillance harms. In diagnostics, artificial intelligence (AI) models increasingly inferred molecular features from routine histopathology ("virtual molecular testing") and should be regarded as decision support requiring prospective validation, population calibration, and explicit failure-mode reporting. Multimodal approaches integrating imaging with circulating tumor DNA (ctDNA) improved feasibility in tissue-limited settings, but clinical utility remains contingent on assay standardization and pathway-level implementation. In resectable disease, longer follow-up consolidated neoadjuvant chemo-immunotherapy for selected patients, while ctDNA kinetics emerged as a candidate biomarker for response-adaptive escalation and de-escalation. In advanced non-small cell lung cancer (NSCLC), phase III evidence for antibody-drug conjugates and bispecific antibodies began reshaping sequencing, while highlighting challenges in toxicity, access, affordability, and immature overall survival in several programs.

CONCLUSIONS: The 2025 landscape reflects coordinated progress in risk conceptualization, biology, diagnostics, and therapeutics, yet gaps in validation, standardization, and real-world deliverability persist. Priorities include prospective evaluation of AI- and ctDNA-enabled pathways, toxicity-informed sequencing, and equitable implementation aligned with health-system capacity.

PMID:41982682 | PMC:PMC13071762 | DOI:10.21037/tlcr-2025-1-1477

Integrative fragmentomic and mutational signature profile of plasma cfDNA for early lung cancer detection

✇Omics In Lung
By: Jianxing He · Huiting Wang · Yi Feng · Jianfu Li · Peiling Chen · Xin Zheng · Wenhai Fu · Caichen Li · Hua Bao · Song Wang · Shuang Chang · Dongqin Zhu · Shanshan Yang · Yang Shao · Wen Zhong · Weisheng Guo · Rong Yin · Wenhua Liang
15 April 2026 at 18:00

NPJ Precis Oncol. 2026 Apr 15. doi: 10.1038/s41698-026-01416-y. Online ahead of print.

ABSTRACT

Detecting lung cancer effectively in the general population is essential for optimizing treatment outcomes and improving the 5-year survival rate. While low-dose computed tomography (LDCT) is the current standard, it has limitations in broader populations. We developed a blood-based multi-omics model using whole-genome cell-free DNA (cfDNA) features to distinguish lung cancer from non-cancer individuals. This study included 1600 patients and an equal number of non-cancer controls, divided into training and validation cohorts. The model achieved an area under the curve (AUC) of 95.59% for the training cohort and 95.74% for the validation cohort. The model consistently performed well across various cancer stages and histological subtypes. To further validate the performance of the model, an external validation cohort was utilized. Notably, it also effectively differentiated non-cancer samples from cancer samples in the external validation cohort, with 85.9% sensitivity and 94.78% specificity. Importantly, in simulated population screenings, our ctDNA assay outperformed both LDCT and a previously established method. This suggests its potential utility in wider lung cancer screening programs, possibly complementing the LDCT approach. In conclusion, our ctDNA assay emerges as a promising and highly sensitive tool for the early detection and categorization of lung cancer.

PMID:41986614 | DOI:10.1038/s41698-026-01416-y

Received — 14 March 2026 ⏭

Multi-Omics and Single-Cell Mendelian Randomization Reveal a Potential Role of VNN2 in Lung Adenocarcinoma in Resting Natural Killer Cells

✇Omics In Lung
By: Zhi Chun Xue · Jing Lin · Jian Hui Wu · Zhi Wen Peng · Mei Yan Tang · Peng Liang · Hui Ling Chen · Gui Ju Fang · Qing Xue
13 March 2026 at 18:00

World J Oncol. 2026 Mar 5;17(2):247-255. doi: 10.14740/wjon2689. eCollection 2026 Apr.

ABSTRACT

BACKGROUND: We aimed to evaluate the potential association between genetically predicted vanin-2 (VNN2) expression and lung adenocarcinoma (LUAD) risk, and to explore the immune cell subtype that may underlie this relationship.

METHODS: We integrated whole-blood expression quantitative trait loci (eQTL) data from eQTLGen, plasma protein quantitative trait loci (pQTL) data from deCODE, and LUAD genome-wide association study (GWAS) data from European-ancestry cohorts, together with differential expression analysis using GEPIA2, to identify candidate genes for subsequent single-cell eQTL (sc-eQTL) Mendelian randomization (MR) analysis. For the sc-eQTL analysis, VNN2-associated eQTLs from 14 immune cell types profiled in the OneK1K single-cell eQTL resource were tested for associations with LUAD risk.

RESULTS: Bulk-level MR analysis showed that genetically predicted increases in VNN2 expression and protein levels were significantly associated with a reduced risk of LUAD (eQTL-MR: odds ratio (OR) = 0.964, 95% confidence interval (95% CI), 0.934-0.995; P = 0.024; pQTL-MR: OR = 0.946, 95% CI, 0.921-0.970; P = 2.87 × 10-5). Transcriptomic analyses confirmed significant downregulation of VNN2 in LUAD tumors compared with normal lung tissues. sc-eQTL MR identified the strongest association in resting natural killer (rNK) cells (OR = 0.896, 95% CI, 0.829-0.967; P = 0.005).

CONCLUSIONS: Multi-omics and sc-eQTL MR analyses indicated that genetically predicted increases in VNN2 expression were associated with a reduced risk of LUAD, with the most pronounced effect observed in rNK cells. These findings suggest a potential cell type-specific role of VNN2 in LUAD susceptibility and warrant further studies to validate its biological relevance and clinical implications.

PMID:41822323 | PMC:PMC12978397 | DOI:10.14740/wjon2689

Received — 11 March 2026 ⏭

Isotonic Layer: A Universal Framework for Generic Recommendation Debiasing

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hailing Cheng · Yafang Yang · Hemeng Tao · Fengyu Zhang
10 March 2026 at 12:00
arXiv:2603.06589v1 Announce Type: cross Abstract: Model calibration and debiasing are fundamental to the reliability and fairness of large scale recommendation systems. We introduce the Isotonic Layer, a novel, differentiable framework that integrates piecewise linear fitting directly into neural architectures. By partitioning the feature space into discrete segments and optimizing non negative slopes via a constrained dot product mechanism, we enforce a global monotonic inductive bias. This ensures model outputs remain logically consistent with critical features such as latent relevance, recency, or quality scores. We further generalize this architecture by parameterizing segment wise slopes as learnable embeddings. This enables the model to adaptively capture context specific distortions, such as position based CTR bias through specialized isotonic profiles. Our approach utilizes a dual task formulation that decouples the recommendation objective into latent relevance estimation and bias aware calibration. A major contribution of this work is the ability to perform highly granular, customized calibration for arbitrary combinations of context features, a level of control difficult to achieve with traditional non parametric methods. We also extend this to Multi Task Learning environments with dedicated embeddings for distinct objectives. Extensive empirical evaluations on real world datasets and production AB tests demonstrate that the Isotonic Layer effectively mitigates systematic bias and enhances calibration fidelity, significantly outperforming production baselines in both predictive accuracy and ranking consistency.
Received — 17 February 2026 ⏭

Beyond In-Domain Detection: SpikeScore for Cross-Domain Hallucination Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yongxin Deng · Zhen Fang · Sharon Li · Ling Chen
17 February 2026 at 13:00
arXiv:2601.19245v4 Announce Type: replace Abstract: Hallucination detection is critical for deploying large language models (LLMs) in real-world applications. Existing hallucination detection methods achieve strong performance when the training and test data come from the same domain, but they suffer from poor cross-domain generalization. In this paper, we study an important yet overlooked problem, termed generalizable hallucination detection (GHD), which aims to train hallucination detectors on data from a single domain while ensuring robust performance across diverse related domains. In studying GHD, we simulate multi-turn dialogues following LLMs' initial response and observe an interesting phenomenon: hallucination-initiated multi-turn dialogues universally exhibit larger uncertainty fluctuations than factual ones across different domains. Based on the phenomenon, we propose a new score SpikeScore, which quantifies abrupt fluctuations in multi-turn dialogues. Through both theoretical analysis and empirical validation, we demonstrate that SpikeScore achieves strong cross-domain separability between hallucinated and non-hallucinated responses. Experiments across multiple LLMs and benchmarks demonstrate that the SpikeScore-based detection method outperforms representative baselines in cross-domain generalization and surpasses advanced generalization-oriented methods, verifying the effectiveness of our method in cross-domain hallucination detection.
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