Hierarchical Belief Modeling for Zero-Shot Opponent Adaptation in Partially Observable Multi-Agent Navigation

By: Kowei Shih · Lu Cheng · Zeyu Wang · Yeyun Xu · Kejian Tong

14 September 2026 at 12:00

arXiv:2609.12422v1 Announce Type: new Abstract: Lux AI Season 3 requires agents to act under partial observability, randomized episode level dynamics, and a best of five match structure that rewards both tactical execution and fast adaptation. We present HORIZON, a hierarchical agent that combines symmetry aware spatial perception, dual memory belief tracking, relic centric graph attention, information gain driven exploration, and an opponent conditioned policy mixture. HORIZON separates short horizon control from cross match meta reasoning, while auxiliary belief and world model objectives stabilize learning. Trained with PPO in a large scale JAX simulator, the resulting agent explicitly infers hidden game parameters and opponent style. Experiments show consistent gains in match win rate, episode win rate, adaptation gain, and league rating over strong recurrent and feed forward baselines.

The Anatomy and Boundary of Adaptation under Temporal Tabular Shift

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

By: Tianyu Wang · Xi Vincent Wang · Lihui Wang · Mian Li · Zhihao Liu

14 September 2026 at 12:00

arXiv:2609.12136v1 Announce Type: cross Abstract: Prequential adaptation of frozen tabular foundation models under temporal drift, with each label revealed only after prediction, helps some deployments and harms others, yet current practice does not predict which. We study the sources and limits of these gains. A diagnostic anatomy attributes gains to four recurring mechanisms under a streaming protocol that removes three optimistic biases and quantifies a fourth. Within an agnostic total-variation drift class, the target conditional is only partially identified: its identified-set diameter, the \emph{wall}, is irreducible from unlabeled data uniformly in sample size. A second, orthogonal $L^2$ projection wall quantifies what the frozen representation cannot express. Two canonical mechanism priors collapse the first wall. Under stated nuisance-rate conditions, the wall can be estimated from labeled historical windows at a $\sqrt N$ rate above the margin threshold $\gamma^\star=d_0/(2\alpha_s)$. At $\gamma=0$, the conditional lower-bound program depends on an open affinity estimate; the positive-margin lower branch also remains open. Semi-synthetic data illustrate the finite-sample mechanism with calibrated exponents. Stream-level proxies on eight industrial streams fall on the difficult side under a stated roughness bound, while the equality case $\gamma=\gamma^\star$ remains unresolved.

SCOPE-OPSD: Fisher-Conditioned Privileged Subspaces for On-Policy Self-Distillation

arXiv:2609.12579v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) scores student-generated prefixes with a solution-conditioned self-teacher, yet transfers supervision only through next-token probabilities. We ask whether the aligned final-layer discrepancy offers a useful second channel, and how to test that channel without confusing its geometry with auxiliary strength. SCOPE-OPSD projects the privileged teacher-student residual onto a frozen rank-64 factor estimated from residual covariance and language-model-head Fisher sensitivity. It reuses the forwards already required by OPSD and adds neither rollouts nor inference-time modules. A matched Random control preserves the structured factor's rank and nonzero spectrum and uses per-arm gradient-RMS calibration, isolating the effect of the data-dependent orientation. Across the complete 25/50/75/100-step trajectories for Qwen3-1.7B, 4B, and 8B, Structured is never below Pure OPSD, with strict gains in 11 of the 12 model-checkpoint combinations and an exact tie at 4B step 25. Structured also exceeds matched Random in 10 of the 12 combinations. At step 75 on Qwen3-1.7B, Structured exceeds matched Random by 1.39 Macro Avg@12 points in each of two independent training reruns. A cross-fitted diagnostic also shows 4.40 times greater held-out privileged-gap capture than the matched random orientation. The results support a compact, Fisher-conditioned privileged subspace for short-budget OPSD.

MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis

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

By: Yuqi Zhan · Xinyue Wu · Tianyu Lin · Yutong Bao · Xiaoyu Wang · Weihao Cheng · Huangwei Chen · Feiwei Qin · Zhu Zhu

14 September 2026 at 12:00

arXiv:2603.01131v4 Announce Type: replace-cross Abstract: Clinical diagnosis is a gradual process of evidence integration, in which physicians move from symptoms and medical history to examinations, competing hypotheses, disease relations, and treatment decisions. Large language models have advanced medical text understanding and generation. Yet their clinical use remains limited by weak evidence grounding, opaque reasoning, and inconsistent links among differential diagnosis, final diagnosis, diagnostic basis, and treatment planning. We introduce MedCollab, a multi-agent framework for full-cycle clinical diagnosis and report generation. MedCollab coordinates specialist and examination agents according to patient records. It structures agent deliberation with an Issue-Based Information System (IBIS) protocol, so that each diagnostic position is supported by patient-specific evidence and medical knowledge. It also builds Hierarchical Disease Relation Chains (HDRC) to connect accepted hypotheses through progression, complication, and comorbidity relations. During multi-round deliberation, a verifier-guided consensus module evaluates evidence support, medical plausibility, and logical conflicts. It then adjusts agent contributions and filters unsupported reasoning. Experiments on ClinicalBench and MIMIC-IV show that MedCollab outperforms leading LLMs and medical multi-agent baselines in diagnostic accuracy, evidence consistency, and clinical reasoning quality. These results indicate that structured and auditable collaboration can produce more faithful and clinically coherent diagnostic reports.

PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement

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

By: Tianyidan Xie · Peiyu Wang · Hu Jiaxin · Yuyi Qian · Yuxuan Wang · Shenyi Wang · Rui Ma · Yanlun Peng · Lanjun Wang · Ying Tai · Jian Yang · Zili Yi

14 September 2026 at 12:00

arXiv:2604.23580v2 Announce Type: replace-cross Abstract: Translating natural-language descriptions of physical phenomena into executable simulation code requires both programming expertise and physical reasoning. Current large language models (LLMs) lack this combination: they frequently produce code that runs but simulates the wrong physics. We introduce PhysCodeBench, the first benchmark for this task, with 1,200 expert-validated examples spanning four physical domains. Its evaluation suite, PhysCodeEval, goes beyond executability and visual fidelity to measure physical correctness directly from the engine state via conservation-law residuals and expert-written assertions, and supports cross-engine evaluation to disentangle physics reasoning from API fluency. As a reference method, we propose the Self-Corrective Multi-Agent Refinement Framework (SMRF), which decouples physics-aware error correction from code generation through specialized agents. This design is motivated by our finding that targeted correction, rather than generic iterative refinement, is the key driver of physical accuracy. SMRF nearly triples the physical-assertion pass rate of the best proprietary baseline (70.6\% vs.\ 23.8\%) and retains its advantage under cross-engine transfer.

LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization

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

By: Haoyu Wang · Xingyu Yu · Haiyan Zhao · Fengxiang Wang · Xu Han

14 September 2026 at 12:00

arXiv:2606.10531v3 Announce Type: replace-cross Abstract: Quantization-aware training (QAT) is essential for extremely low-bit large language models (LLMs). Current QAT methods are mainly based on scalar quantization (SQ), which enables efficient optimization but suffers from severe performance degradation at 2-bit precision. On the other hand, vector quantization (VQ) provides substantially higher representational capacity, but its discrete codebook lookup prevents end-to-end training. We propose LC-QAT, a 2-bit weight-only VQ-QAT framework that represents quantized weights via a learned affine mapping over discrete vectors, which yields a high-quality PTQ initialization and enables fully differentiable end-to-end optimization without explicit codebook lookup in the training forward pass. This strong post-training initialization makes LC-QAT highly data-efficient. Experiments across diverse LLMs demonstrate that LC-QAT consistently outperforms state-of-the-art QAT methods while using only 0.1%--10% of the training data. Our results establish LC-QAT as a practical and scalable solution for extreme low-bit model deployment. Codes are publicly available at https://github.com/AI9Stars/UniSVQ.

Gut dysbiosis, metabolic signals, and pulmonary immune reprogramming: decoding the gut microbiota -immune axis in stroke-associated pneumonia

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

By: Li Yang · Jingru Han · Shengqiang Li · Xinyu Wang · Yujie Jia · Zhijun Bao · Ting Ge · Guixin He · Juyue Zhou · Jiaming Cui · Yuanyuan Tian · Yingying Xie · Lefan Liu · Jinghui Du · Wentao Li · Jianchun Yu

11 September 2026 at 18:00

Front Immunol. 2026 Aug 27;17:1812306. doi: 10.3389/fimmu.2026.1812306. eCollection 2026.

ABSTRACT

Stroke-associated pneumonia (SAP) is the most common infectious complication following acute stroke. The limited efficacy of conventional antimicrobial therapy suggests that SAP may be fundamentally a syndrome driven by dysregulated cross-system interactions. This review proposes the "gut microbiota-immune axis" (GMIA) as a comprehensive framework for the development of SAP and systematically discusses the potential mechanisms by which post-stroke microbial-derived metabolic signals-including short-chain fatty acids (SCFAs), bile acids, tryptophan metabolites, and endotoxins-drive systemic immune reprogramming, predisposing patients to SAP. Based on the GMIA, we highlight several promising intervention strategies, including dietary modulation, precision antibiotic use, probiotics, fecal microbiota transplantation (FMT), supplementation with microbial metabolites, and receptor-targeted therapies, and summarize the current clinical translation related to the GMIA. Future research directions require high-quality clinical trials that integrate multi-omics data from the microbiome with immune biomarkers and clinical parameters. Such an approach is essential for constructing validated risk stratification models and advancing the management of SAP from empirical anti-infective treatment toward a precision medicine model centered on GMIA-based immune modulation.

PMID:42724580 | PMC:PMC13560329 | DOI:10.3389/fimmu.2026.1812306

A clinically-oriented foundation model for intraoperative pathology

Nature Medicine

By: Zihan Zhao · Fengtao Zhou · Ronggang Li · Bing Chu · Xinke Zhang · Xueyi Zheng · Ke Zheng · Xiaobo Wen · Jiabo Ma · Yihui Wang · Jiewei Chen · Chengyou Zheng · Jiangyu Zhang · Yongqin Wen · Jiajia Meng · Ziqi Zeng · Xiaoqing Li · Jing Li · Dan Xie · Yaping Ye · Yu Wang · Hao Chen · Muyan Cai

10 September 2026 at 08:00

Nature Medicine, Published online: 10 September 2026; doi:10.1038/s41591-026-04703-0

CRISP, a vision-based pathology foundation model developed exclusively from frozen section slides, supports treatment decision-making throughout the surgical workflow with superior performance to current foundation models and extensive validation, including in a prospective cohort.

Targeting the MNK1-MYH9 axis blocks YAP1 recruitment to prevent thrombosis and platelet activation-induced NETosis

Molecular Therapy

By: Kang Liu · Xinke Fang · Jingjun Sheng · Zhiming Wang · Ruijuan Yin · Qi Yuan · Tianyu Wang · Shaoyun Zhou · Qisheng Ling · Haidong Chen · Muhammad Awais · Tao Jiang · Chuanbin Shen

3 August 2026 at 08:00

MNK1 acts as a structural shield on MYH9, preventing YAP1-mediated platelet activation. Developing MD2 to lock this MNK1-MYH9 complex introduces a safe antithrombotic strategy, shifting the therapeutic paradigm from kinase inhibition to stabilizing protein-protein interactions against immunothrombosis.

Synchronized latency reversal and immune clearance by a multifunctional fusion protein enables HIV-1 reservoir reduction

Molecular Therapy

By: Fei Luo · Yinghui Cao · Na Liu · Xiaowei Wang · Wenjia Hu · Yajun Yan · Xiaoqing Xie · Dongmei Jiang · Yangyang Xu · Jun Wang · Hongyu Wang · Zhenyang Yu · Peng Qian · Shicheng Wan · Zhida Liu · Yan Gong · Tielong Chen · Shihui Song · Yong Xiong · Xiaoping Tang · Linghua Li · Li Wang · Haisheng Yu · Liang Cheng

29 July 2026 at 08:00

Latent HIV reservoirs evade both antiviral therapy and immune surveillance. Luo and colleagues develop a multifunctional fusion protein that couples reservoir reactivation with targeted immune engagement and clearance, offering a coordinated strategy to expose and eliminate persistent HIV-infected cells.

Engineering inflammation-responsive proteins through nitric oxide-caged amino acids

Nature Biomedical Engineering

By: Wenkang Cai · Junhao Cui · Zhiying Zeng · Zexian Xiang · Yuanzhe Xie · Yeyu Su · Yi Zuo · Yingze Liu · Haoyu Wang · Liying Chang · Xue Wang · Jingjing Wang · Jun-An Ma · Tao Liu

31 August 2026 at 08:00

Nature Biomedical Engineering, Published online: 31 August 2026; doi:10.1038/s41551-026-01782-9

A protein engineering strategy enables nitric oxide-triggered reactivation of proteins using genetically encoded caged amino acids, allowing inflammation-localized control of protein activity, viral gene delivery and biosensing in vivo.

Structural Process Supervision for Latent Chain-of-Thought Reasoning

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

By: Yiqi Li · Xu Chen · Chen Ju · Jiangchao Yao · Zhaoyang Li · Jinsong Lan · Xiaoyong Zhu · Bo Zheng · Yu Wang

10 September 2026 at 12:00

arXiv:2609.09928v1 Announce Type: new Abstract: Latent reasoning approaches enhance token-level efficiency and robustness by replacing verbose, explicit chain-of-thought (CoT) tokens with compact continuous-space embeddings. However, existing methods lack direct process supervision over these latent embeddings, which often leads to representation collapse and uneven information distribution. To address this, we propose Prototype-Mediated Process Supervision (PMPS), which introduces learnable reasoning prototypes as semantic anchors to provide structural process-level supervision for latent reasoning. PMPS projects latent embeddings and explicit CoT embeddings into a shared prototype space, achieving many-to-many soft alignment between unequal-length representations through prototype assignment. Meanwhile, we introduce a Progressive Sequential Alignment (PSA) module to further guide training: positional priors initially encourage sequential alignment structure, then gradually relax to permit adaptive matching. Experimental results show that PMPS compresses output token length to under 50% of explicit CoT on GSM8K-Aug. Compared to leading baseline SIM-CoT, our method achieves average accuracy gains of 2.08% across different model families. On GPT-2, PMPS even surpasses CoT-SFT. On larger models and a more challenging task, PMPS consistently attains the highest accuracy among all latent reasoning methods with comparable output length.

VANTAGE-Bench: Evaluating the Infrastructure AI Gap in Vision-Language Models

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

By: Zaid Pervaiz Bhat · Nimra Nayyar · Arihant Jain · Lap Fung Chan · John Suchanek · Yu Wang · Varun Praveen · Tomasz Kornuta · Vidya Nariyambut Murali

10 September 2026 at 12:00

arXiv:2609.09396v1 Announce Type: cross Abstract: As Vision-Language Models (VLMs) advance toward physical deployment, the focus has remained on action-oriented Embodied AI evaluated on subject-centric consumer video. This overlooks a pervasive class of Physical AI: Infrastructure AI, which relies on fixed cameras for open-loop insights like safety monitoring and operational logging. We introduce VANTAGE-Bench, a benchmark measuring this "Infrastructure AI Gap." It spans three operational domains (Logistics, Transportation, and Smart Spaces), unifies image and video evaluation across semantic, spatial, temporal, and spatio-temporal capabilities, and moves beyond multiple-choice to eight task formulations including dense captioning and spatio-temporal grounding. It adds a single-pass trajectory protocol for Single Object Tracking and, to our knowledge, the first such evaluation on fixed-camera infrastructure video, scored against specialist trackers. Annotation spans three regimes over 3,346 media assets: 3,342 video-task annotations, 4,281 image-grounding annotations, and 27,404 detection boxes. Evaluating 17 models zero-shot, we find the shortfall relative to consumer-centric benchmarks is concentrated, not general. Event verification, referring expressions, and temporal localization fall roughly 9 to 24 points at every model scale, while video question answering stays within 5.3 points of VideoMME and 2D spatial pointing shows no shortfall against BLINK. The temporal pillar is weakest in absolute terms: no system exceeds 55.7 mIoU on temporal localization or 37.3 SODA_c on dense video captioning. On tracking, frontier models come within roughly 5 points of specialist trackers over short horizons but separate as the horizon extends. Open-weight models lead 2D object localization outright, so neither scale nor proprietary access explains the pattern. Data, evaluation harness, and leaderboard: https://vantage-bench.org/

FrontierChallenge: Evaluating Scientific Workflow Completion

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

By: Liangcai Su · Zhaopeng Feng · Zhuo Chen · Zhen Zhang · Xiang Lin · Ruilin Li · Handuo Zhang · Ning Wang · Kailong Wen · Yueqi Guo · Feng Xing · Yiling Guo · Brian Wang · Chenxiong Qian · Simon Shaolei Du · Lidong Bing · Xinyu Wang

10 September 2026 at 12:00

arXiv:2608.24979v2 Announce Type: replace Abstract: Scientific agents increasingly analyze data, execute code, and produce research artifacts, yet most benchmarks emphasize final answers, isolated programs, or a single domain. We introduce FrontierChallenge, a cross-domain benchmark comprising 300 end-to-end scientific workflows. In this paper, we release and evaluate 97 of these tasks, spanning quantum chemistry, molecular dynamics, materials characterization, analytical chemistry, life science, and electrochemistry/environment. Each task provides fixed inputs and specifies a bundle of required scientific deliverables. We evaluate twelve frontier models with three agent scaffolds. Pass Rate measures the fraction of tasks satisfying the full-completion criterion, while Avg. Score captures partial progress. Each of the best-performing configurations completed only 20 of the 97 released tasks, yielding a Pass Rate of 20.6%. Partial progress translated especially poorly into complete delivery in analytical chemistry and electrochemistry/environment: Avg. Scores reached 87.6 and 94.9, but the highest Pass Rates were only 4% and 0%. Among non-passing Claude Code trajectories, 75.5% still ended with language claiming completion. Complementary HDS6 process scores correlate strongly with task outcomes, supporting FrontierChallenge as a benchmark of Heavy Duty Solver capabilities. These findings show that neither high partial scores nor confident claims of completion reliably indicate that a scientific task has been fully delivered, highlighting the need to evaluate end-to-end workflow execution and the completeness of scientific deliverables together.

LightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation

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

By: Shaoan Wang · Aocheng Luo · Fei Huang · Jingyi Xu · Xiaoyang Wang · Yueyu Wang · Qianli Ma · Fan Yang · Ran Mei · Jia Wei · Jiangpeng Hu · Xuhao Liu · Hongming Chen · Yuanbin Shao · Yiyang Lin · Ziliang Li · Liang Pan · Xinhang Liu · Yuntao Ma · Tingxiang Fan

10 September 2026 at 12:00

arXiv:2608.30935v2 Announce Type: replace-cross Abstract: Embodied navigation requires agents to translate heterogeneous goals and visual observations into actions across tasks, environments, and robot embodiments. Modern vision-language models (VLMs) already encode spatial priors for visual grounding, spatial reasoning, and pointing, but these capabilities are rarely elicited directly for robot control. Existing navigation systems instead rely on task- or embodiment-specific components, fragmenting perception, reasoning, and action while offering limited generalization. Here we present LightNav-0, a compact generalist embodied navigation model that elicits the spatial intelligence of a pretrained VLM and aligns it with navigation, without task-specific prediction heads. LightNav-0 represents diverse navigation tasks through a unified token interface: dual-channel pointing expresses task-, scene-, and embodiment-agnostic spatial intent, while a residual vector-quantized action tokenizer maps this intent to precise, embodiment-specific trajectories. Together with temporally aware visual history compression, ER mid-training, supervised fine-tuning, and reinforcement learning, this formulation supports instruction following, open-vocabulary object navigation, and visual tracking within a single model. The navigation training corpus spans 2K+ scenes and 4K+ hours of embodied navigation data. LightNav-ER, the embodied-reasoning checkpoint used to initialize LightNav-0, attains the highest complete-set average across 8 embodied-reasoning benchmarks, while LightNav-0 achieves state-of-the-art monocular success rates across all 10 public navigation simulation settings. Real-world evaluations further demonstrate zero-shot generalization across robot embodiments, diverse scenes, and static and dynamic targets. These results establish compact VLMs as a unified and transferable backbone for generalist embodied navigation.

A Non-Canonical Role of SMAD4 in Regulating 3D Genome Architecture to Inhibit Lung Squamous Cell Carcinoma Development

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

By: Qian Tang · Chen Lian · Xinyan Han · Xuhan Zhang · Zihan Wang · Boyu Wang · Taoyu Zhu · Xinrui Lin · Xiaolei Wang · Yaping Xu · Manyu Xiao · Zijin Wang · Junming Li · Silin Chen · Yunze Wang · Yufei Liu · Songsong Li · Zuolin Shen · Xi Lu · Xueqi Han · Yilu Zhou · Mingyang Xiao · Jiayi Ran · Xiaoran Cao · Xinyi Xu · Hugo Sámano-Sánchez · Alfredo Rodríguez · Lian Wang · Shuifang Chen · Zhanyu Xu · Shirong Zhang · Nuo Yang · Yong Tang · Jian Liu

26 May 2026 at 18:00

Adv Sci (Weinh). 2026 May 26:e75839. doi: 10.1002/advs.75839. Online ahead of print.

ABSTRACT

Lung squamous cell carcinoma (LUSC) lacks clearly defined key drivers and effective targeted therapies, reflecting an incomplete understanding of its molecular pathogenesis. Here, we identify SMAD4 as a critical regulator of three-dimensional (3D) genome organization in LUSC and uncover a mechanistic link between tumor suppressor loss and oncogenic transcriptional activation. By integrating clinical datasets, genetically engineered mouse models, human and murine LUSC cell lines, and multi-omics analyses, we demonstrate that SMAD4 deficiency promotes LUSC progression by unleashing EP300-mediated enhancer-promoter looping at the SOX2 locus. Mechanistically, SMAD4 does not directly bind SOX2 regulatory elements but instead constrains chromatin looping by sequestering EP300 away from loop anchor regions. Loss of SMAD4 leads to enhanced H3K27ac deposition, aberrant SOX2 activation, and increased LUSC tumor cell proliferation. Together, these findings reveal a non-canonical role for a transcription factor (e.g., SMAD4) in regulating dysregulated 3D genome architecture to inhibit tumor development.

PMID:42189071 | DOI:10.1002/advs.75839

Multi-Omics Identification of Biomarkers for High-Altitude Pulmonary Hypertension

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

By: Zhe Chen · Linhong Pang · Yidan Zheng · Li Xu · Mingjing Tang · Ziwen Zhao · Tianyu Wang · Jin Li · Yunfei Zhou · Lin Duo · Wenlong Zhu · Zhiling Luo · Fei Li · Da Zhu

26 May 2026 at 18:00

J Cardiovasc Dev Dis. 2026 Apr 30;13(5):195. doi: 10.3390/jcdd13050195.

ABSTRACT

(1) Aim: The incidence of high-altitude pulmonary hypertension (HAPH) has risen in recent years and is expected to continue increasing; however, its diagnosis remains challenging. In this study, we employed proteomics and metabolomics to identify the proteins and metabolic biomarkers that contribute to the development of HAPH. (2) Methods: We applied integrated proteomics and metabolomics to match blood samples from 40 HAPH patients and 40 healthy controls in Yunnan's high-altitude regions to characterize molecular profiles, identify biomarkers, and develop a predictive model. (3) Results: Proteomic analysis identified four proteins (A2IPH7, K1C14, PSME2, SERPINE2) commonly dysregulated in HAPH patients from two high-altitude regions. SERPINE2 was notably downregulated and showed a negative correlation with clinical severity, which was further validated in HAPH rat lung tissues and supported by UK Biobank data for idiopathic PAH. Concurrent metabolomics uncovered 11 shared metabolites, largely acyl fatty acids, enriched in pathways such as unsaturated fatty acid synthesis. Integration of these multi-omics data enabled the development of a robust predictive model. (4) Conclusion: Our study identified key protein and metabolic biomarkers involved in HAPH development, which were validated in animal models. Based on these findings, a predictive model was developed, highlighting SERPINE2 and 11 metabolites as promising targets for the prediction and prevention of HAPH.

PMID:42188081 | DOI:10.3390/jcdd13050195

A Non-Canonical Role of SMAD4 in Regulating 3D Genome Architecture to Inhibit Lung Squamous Cell Carcinoma Development

Omics In Lung

By: Qian Tang · Chen Lian · Xinyan Han · Xuhan Zhang · Zihan Wang · Boyu Wang · Taoyu Zhu · Xinrui Lin · Xiaolei Wang · Yaping Xu · Manyu Xiao · Zijin Wang · Junming Li · Silin Chen · Yunze Wang · Yufei Liu · Songsong Li · Zuolin Shen · Xi Lu · Xueqi Han · Yilu Zhou · Mingyang Xiao · Jiayi Ran · Xiaoran Cao · Xinyi Xu · Hugo Sámano-Sánchez · Alfredo Rodríguez · Lian Wang · Shuifang Chen · Zhanyu Xu · Shirong Zhang · Nuo Yang · Yong Tang · Jian Liu

26 May 2026 at 18:00

Adv Sci (Weinh). 2026 May 26:e75839. doi: 10.1002/advs.75839. Online ahead of print.

ABSTRACT

Lung squamous cell carcinoma (LUSC) lacks clearly defined key drivers and effective targeted therapies, reflecting an incomplete understanding of its molecular pathogenesis. Here, we identify SMAD4 as a critical regulator of three-dimensional (3D) genome organization in LUSC and uncover a mechanistic link between tumor suppressor loss and oncogenic transcriptional activation. By integrating clinical datasets, genetically engineered mouse models, human and murine LUSC cell lines, and multi-omics analyses, we demonstrate that SMAD4 deficiency promotes LUSC progression by unleashing EP300-mediated enhancer-promoter looping at the SOX2 locus. Mechanistically, SMAD4 does not directly bind SOX2 regulatory elements but instead constrains chromatin looping by sequestering EP300 away from loop anchor regions. Loss of SMAD4 leads to enhanced H3K27ac deposition, aberrant SOX2 activation, and increased LUSC tumor cell proliferation. Together, these findings reveal a non-canonical role for a transcription factor (e.g., SMAD4) in regulating dysregulated 3D genome architecture to inhibit tumor development.

PMID:42189071 | DOI:10.1002/advs.75839

Multi-Omics Identification of Biomarkers for High-Altitude Pulmonary Hypertension

Omics In Lung

By: Zhe Chen · Linhong Pang · Yidan Zheng · Li Xu · Mingjing Tang · Ziwen Zhao · Tianyu Wang · Jin Li · Yunfei Zhou · Lin Duo · Wenlong Zhu · Zhiling Luo · Fei Li · Da Zhu

26 May 2026 at 18:00

J Cardiovasc Dev Dis. 2026 Apr 30;13(5):195. doi: 10.3390/jcdd13050195.

ABSTRACT

(1) Aim: The incidence of high-altitude pulmonary hypertension (HAPH) has risen in recent years and is expected to continue increasing; however, its diagnosis remains challenging. In this study, we employed proteomics and metabolomics to identify the proteins and metabolic biomarkers that contribute to the development of HAPH. (2) Methods: We applied integrated proteomics and metabolomics to match blood samples from 40 HAPH patients and 40 healthy controls in Yunnan's high-altitude regions to characterize molecular profiles, identify biomarkers, and develop a predictive model. (3) Results: Proteomic analysis identified four proteins (A2IPH7, K1C14, PSME2, SERPINE2) commonly dysregulated in HAPH patients from two high-altitude regions. SERPINE2 was notably downregulated and showed a negative correlation with clinical severity, which was further validated in HAPH rat lung tissues and supported by UK Biobank data for idiopathic PAH. Concurrent metabolomics uncovered 11 shared metabolites, largely acyl fatty acids, enriched in pathways such as unsaturated fatty acid synthesis. Integration of these multi-omics data enabled the development of a robust predictive model. (4) Conclusion: Our study identified key protein and metabolic biomarkers involved in HAPH development, which were validated in animal models. Based on these findings, a predictive model was developed, highlighting SERPINE2 and 11 metabolites as promising targets for the prediction and prevention of HAPH.

PMID:42188081 | DOI:10.3390/jcdd13050195

Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models

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

By: Weixian Waylon Li · Mengyu Wang · Tiejun Ma

26 May 2026 at 12:00

arXiv:2605.24564v1 Announce Type: new Abstract: Backtesting large language models (LLMs) on historical financial data is unreliable because pre-training cuts off after the events happened. An LLM trained in 2024 already "knows" which way 2018-2020 stocks moved. We name this failure parametric look-ahead bias and propose FinCAD, an inference-time adaptation of Context-Aware Decoding that suppresses an LLM's memory of historical outcomes without retraining. FinCAD pairs an adversarial bias-discovery pipeline that learns a model-specific memory-activating prior prompt with an entity- and date-adaptive rule that scales the CAD strength to per-(entity, date) memorisation, so the penalty fires on memorised in-sample dates and decays to zero out-of-sample. Across five 7-14B LLMs and five mega-cap equities, FinCAD cuts in-sample backtest returns by up to -67.1% on memorised dates while leaving 2025 out-of-sample returns within $8K and Sharpe within 0.10 of baseline, and preserves general-purpose reasoning within 1.7 pts. On an eleven-model leaderboard, it raises the in-sample / out-of-sample Spearman correlation from +0.779 to +0.846, recovering rankings that genuinely predict out-of-sample performance.

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