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L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Fan Zhang · Shijun Chen · Hua Wang
arXiv:2605.17730v2 Announce Type: replace-cross Abstract: Mainstream methods for multivariate time-series forecasting largely follow the Direct-Mapping paradigm. They learn a unified mapping from history to the future in the observation space to fit value-level dependencies. However, real-world systems often undergo distribution shifts and regime changes. In such cases, a unified mapping can exhibit response lag around turning points, causing error accumulation within the switching window and reducing forecasting reliability. To address this issue, we propose L-Drive, a change-aware forecasting framework. L-Drive introduces a Latent-Context, to explicitly characterize high-level dynamics evolving over time, and uses gating to modulate increment representations. This provides more timely change cues and improves adaptation to changing segments. In addition, it incorporates patch-shared relative positional basis functions to strengthen intra-segment structural modeling and reduce overfitting caused by absolute-position memorization. Extensive experiments validate the effectiveness of L-Drive and show a better overall trade-off between forecasting accuracy and computational efficiency.
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Integrated radiopathomics nomogram for predicting angiogenic microvascular patterns in NSCLC: a dual-center validation study

✇Omics In Lung
By: Ronghua Wang · Lin Wang · Xinzheng Wang · Shuai Quan · Linning E · Jiangfeng Du

Ann Med. 2026 Dec;58(1):2654291. doi: 10.1080/07853890.2026.2654291. Epub 2026 Apr 17.

ABSTRACT

BACKGROUND: To develop and validate an integrated radiopathomics nomogram combining multiphase CT images, H&E-stained slides, and clinicopathological variables for predicting microvascular patterns (MVPs) in non-small cell lung cancer (NSCLC).

METHODS: We retrospectively included consecutive surgically resected NSCLC patients from two centers (n = 258). Patients from center 1 were randomly divided into training and internal validation cohorts, while patients from center 2 formed external validation cohort. CD34-immunohistochemistry was used as the reference standard for MVPs to classify patients into non-angiogenic alveolar (NAA) and non-NAA groups. Radiomics and pathomics features were extracted to construct single-phase radiomics, combined radiomics, and pathomics models. Rad-score and Path-score were derived from combined radiomics and pathomics models, respectively. Rad-score, Path-score, and clinicopathological independent predictors were integrated to develop a nomogram. Model performance was assessed by area under the curve (AUC), calibration curve, decision curve analysis (DCA), and DeLong test.

RESULTS: On multivariable analysis, histological grade was an independent predictor of NAA MVP. Combined radiomics model for predicting MVPs achieved AUCs of 0.863, 0.856, and 0.849 in training, internal validation, and external validation cohorts, showing better performance than single-phase models. Pathomics model yielded AUCs of 0.878, 0.860, and 0.833, however, its specificity markedly decreased in validation cohorts. Nomogram model achieved the superior performance across all cohorts, with AUCs of 0.911, 0.903, and 0.901, outperforming single-modality models (DeLong test: all p < 0.05).

CONCLUSION: The nomogram demonstrated high accuracy and robustness in predicting MVPs in NSCLC, offering a promising tool for characterizing the tumor microenvironment and supporting individualized treatment.

PMID:41992828 | DOI:10.1080/07853890.2026.2654291

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Development and multi-center evaluation of domain-adapted speech recognition for human-AI teaming in real-world gastrointestinal endoscopy

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ruijie Yang · Yan Zhu · Peiyao Fu · Te Luo · Zhihua Wang · Xian Yang · Quanlin Li · Pinghong Zhou · Shuo Wang
arXiv:2604.01705v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) is a critical interface for human-AI interaction in gastrointestinal endoscopy, yet its reliability in real-world clinical settings is limited by domain-specific terminology and complex acoustic conditions. Here, we present EndoASR, a domain-adapted ASR system designed for real-time deployment in endoscopic workflows. We develop a two-stage adaptation strategy based on synthetic endoscopy reports, targeting domain-specific language modeling and noise robustness. In retrospective evaluation across six endoscopists, EndoASR substantially improves both transcription accuracy and clinical usability, reducing character error rate (CER) from 20.52% to 14.14% and increasing medical term accuracy (Med ACC) from 54.30% to 87.59%. In a prospective multi-center study spanning five independent endoscopy centers, EndoASR demonstrates consistent generalization under heterogeneous real-world conditions. Compared with the baseline Paraformer model, CER is reduced from 16.20% to 14.97%, while Med ACC is improved from 61.63% to 84.16%, confirming its robustness in practical deployment scenarios. Notably, EndoASR achieves a real-time factor (RTF) of 0.005, significantly faster than Whisper-large-v3 (RTF 0.055), while maintaining a compact model size of 220M parameters, enabling efficient edge deployment. Furthermore, integration with large language models demonstrates that improved ASR quality directly enhances downstream structured information extraction and clinician-AI interaction. These results demonstrate that domain-adapted ASR can serve as a reliable interface for human-AI teaming in gastrointestinal endoscopy, with consistent performance validated across multi-center real-world clinical settings.
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Unified modeling of 3D molecular generation via atomic interactions with PocketXMol

✇Cell
By: Xingang Peng · Ruihan Guo · Fenglin Guo · Ziyi Wang · Jiayu Sun · Jiaqi Guan · Yinjun Jia · Yan Xu · Yanwen Huang · Muhan Zhang · Jian Peng · Xinquan Wang · Chuanhui Han · Zihua Wang · Jianzhu Ma
A versatile, atom-level generative AI model enables unified pocket-interacting tasks, from docking to de novo design, and demonstrates robust experimental validation for both small-molecule and peptide therapeutics.
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Dual function of DOT1L suppresses tumor cell-intrinsic immunogenicity in hepatocellular carcinoma

✇Oncogene - Issue - nature.com science feeds
By: Siyuan Xu · Ruijie Gong · Songchen Liu · Jianhua Wang · Yijie Shen · Chuxuan Peng · Qin Feng · Min Luo · Fei Lan · Jia Fan · Jiabin Cai · Xianjiang Lan

Oncogene, Published online: 31 March 2026; doi:10.1038/s41388-026-03744-6

Dual function of DOT1L suppresses tumor cell-intrinsic immunogenicity in hepatocellular carcinoma
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Trem1 regulates neutrophil metabolism and recruitment in lung ischemia-reperfusion injury

✇Omics In Lung
By: Fengjing Yang · Song Tong · Junhao Wan · Yixing Li · Jiani Gao · Yan Sun · Xiangfu Sun · Huikang Fu · Wenzhuo Luo · Jiayang Xu · Ting Zhou · Sowe Babou · Junqi Wu · Guangjian Zhang · Chang Chen · Sihua Wang

Redox Biol. 2026 Jan 14;92:104026. doi: 10.1016/j.redox.2026.104026. Online ahead of print.

ABSTRACT

Primary graft dysfunction (PGD) caused by ischemia-reperfusion injury (IRI) is a major complication after lung transplantation, yet its underlying mechanisms remain unclear. Triggering receptor expressed on myeloid cells 1 (Trem1) is an important mediator of inflammation, but its role in neutrophil function and metabolic reprogramming during lung IRI is not well understood. In this study, we used a murine orthotopic lung transplantation model with cold ischemia and reperfusion, and Trem1 knockout (Trem1-/-) and myeloid-specific Trem1 conditional knockout mice (LysmCreTrem1fl) to explore the role of Trem1 in neutrophil recruitment, neutrophil extracellular trap (NET) formation, and metabolism. Our results show that Trem1 expression increases in both mouse and human lungs after reperfusion and correlates with neutrophil infiltration and lung injury. Trem1 deficiency significantly reduced neutrophil and macrophage recruitment, NET formation, and tissue damage. Multi-omics analysis revealed that Trem1 deletion suppressed oxidative phosphorylation (OXPHOS) and induced a metabolic shift in neutrophils toward glycolysis. In clinical samples, the abundance of TREM1+ neutrophils was correlated with PGD severity and OXPHOS activity. These findings identify Trem1 as a key regulator of neutrophil metabolism and recruitment in lung IRI, and suggest that targeting Trem1 may provide a novel therapeutic strategy to mitigate PGD and improve lung transplant outcomes.

PMID:41861599 | DOI:10.1016/j.redox.2026.104026

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Reallocating Attention Across Layers to Reduce Multimodal Hallucination

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haolang Lu · Bolun Chu · WeiYe Fu · Guoshun Nan · Junning Liu · Minghui Pan · Qiankun Li · Yi Yu · Hua Wang · Kun Wang
arXiv:2510.10285v3 Announce Type: replace Abstract: Multimodal large reasoning models (MLRMs) often suffer from hallucinations that stem not only from insufficient visual grounding but also from imbalanced allocation between perception and reasoning processes. Building upon recent interpretability findings suggesting a staged division of attention across layers, we analyze how this functional misalignment leads to two complementary failure modes: perceptual bias in shallow layers and reasoning drift in deeper layers. To alleviate these issues, we propose Functional Head Identification and Class-Conditioned Rescaling , a lightweight, training-free plugin that identifies perception- and reasoning-oriented heads and adaptively rebalances their layerwise contributions. Our method improves reasoning consistency and visual faithfulness without retraining or any architectural modification. Evaluations across three representative MLRMs and five multimodal reasoning benchmarks show an average 4.2% point gain, with less than 1% additional computation and only 9% baseline latency. Beyond empirical improvements, our study provides an interpretable perspective on regulating cross-layer functional dynamics to enhance the reliability of multimodal reasoning.
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JPmHC Dynamical Isometry via Orthogonal Hyper-Connections

✇cs.AI, q-bio.NC updates on arXiv.org
By: Biswa Sengupta · Jinhua Wang · Leo Brunswic
arXiv:2602.18308v2 Announce Type: replace-cross Abstract: Recent advances in deep learning, exemplified by Hyper-Connections (HC), have expanded the residual connection paradigm by introducing wider residual streams and diverse connectivity patterns. While these innovations yield significant performance gains, they compromise the identity mapping property of residual connections, leading to training instability, limited scalability, and increased memory overhead. To address these challenges, we propose JPmHC (Jacobian-spectrum Preserving manifold-constrained Hyper-Connections), a framework that replaces identity skips with a trainable linear mixer acting on n parallel streams while explicitly controlling gradient conditioning. By constraining the mixer M on operator-norm-bounded manifolds (e.g., bistochastic, Stiefel, Grassmann), JPmHC prevents gradient pathologies and enhances stability. JPmHC introduces three key contributions: (i) a free-probability analysis that predicts Jacobian spectra for structured skips, providing actionable design rules for mixer selection; (ii) memory-efficient implicit differentiation for fixed-point projections, reducing activation memory and synchronization overhead; and (iii) a Stiefel-constrained mixer via Cayley transforms, ensuring orthogonality without post-hoc normalization. Empirical evaluations on ARC-AGI demonstrate that JPmHC achieves faster convergence, higher accuracy, and lower computational cost compared to bistochastic baselines, with a rank-$p$ Grassmannian variant tracking between the two -- consistent with the spectral theory predictions. As a flexible and scalable extension of HC, JPmHC advances spectrum-aware, stable, and efficient deep learning, offering insights into topological architecture design and foundational model evolution. \newline \newline
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A Secure and Private Distributed Bayesian Federated Learning Design

✇cs.AI, q-bio.NC updates on arXiv.org
By: Nuocheng Yang · Sihua Wang · Zhaohui Yang · Mingzhe Chen · Changchuan Yin · Kaibin Huang
arXiv:2602.20003v1 Announce Type: cross Abstract: Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server. However, DFL faces three critical challenges: privacy leakage from honest-but-curious neighbors, slow convergence due to the lack of central coordination, and vulnerability to Byzantine adversaries aiming to degrade model accuracy. To address these issues, we propose a novel DFL framework that integrates Byzantine robustness, privacy preservation, and convergence acceleration. Within this framework, each device trains a local model using a Bayesian approach and independently selects an optimal subset of neighbors for posterior exchange. We formulate this neighbor selection as an optimization problem to minimize the global loss function under security and privacy constraints. Solving this problem is challenging because devices only possess partial network information, and the complex coupling between topology, security, and convergence remains unclear. To bridge this gap, we first analytically characterize the trade-offs between dynamic connectivity, Byzantine detection, privacy levels, and convergence speed. Leveraging these insights, we develop a fully distributed Graph Neural Network (GNN)-based Reinforcement Learning (RL) algorithm. This approach enables devices to make autonomous connection decisions based on local observations. Simulation results demonstrate that our method achieves superior robustness and efficiency with significantly lower overhead compared to traditional security and privacy schemes.
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From Pixels to Words -- Towards Native Vision-Language Primitives at Scale

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haiwen Diao · Mingxuan Li · Silei Wu · Linjun Dai · Xiaohua Wang · Hanming Deng · Lewei Lu · Dahua Lin · Ziwei Liu
arXiv:2510.14979v2 Announce Type: replace-cross Abstract: The edifice of native Vision-Language Models (VLMs) has emerged as a rising contender to typical modular VLMs, shaped by evolving model architectures and training paradigms. Yet, two lingering clouds cast shadows over its widespread exploration and promotion: (-) What fundamental constraints set native VLMs apart from modular ones, and to what extent can these barriers be overcome? (-) How to make research in native VLMs more accessible and democratized, thereby accelerating progress in the field. In this paper, we clarify these challenges and outline guiding principles for constructing native VLMs. Specifically, one native VLM primitive should: (i) effectively align pixel and word representations within a shared semantic space; (ii) seamlessly integrate the strengths of formerly separate vision and language modules; (iii) inherently embody various cross-modal properties that support unified vision-language encoding, aligning, and reasoning. Hence, we launch NEO, a novel family of native VLMs built from first principles, greatly narrowing the gap with top-tier modular counterparts across diverse real-world scenarios. With 390M image-text examples, NEO efficiently develops visual perception from scratch while mitigating vision-language conflicts inside a dense and monolithic model crafted from our elaborate primitives. We position NEO as a cornerstone for scalable and powerful native VLM development, paired with a rich set of reusable components that foster a cost-effective and extensible ecosystem. Our code and models are publicly available at: https://github.com/EvolvingLMMs-Lab/NEO.
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