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Received — 8 April 2026 ⏭
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Symbolic-Vector Attention Fusion for Collective Intelligence Hongwei Xu
    arXiv:2604.03955v1 Announce Type: cross Abstract: When autonomous agents observe different domains of a shared environment, each signal they exchange mixes relevant and irrelevant dimensions. No existing mechanism lets the receiver evaluate which dimensions to absorb. We introduce Symbolic-Vector Attention Fusion (SVAF), the content-evaluation half of a two-level coupling engine for collective intelligence. SVAF decomposes each inter-agent signal into 7 typed semantic fields, evaluates each thr
     
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Symbolic-Vector Attention Fusion for Collective Intelligence

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hongwei Xu
7 April 2026 at 12:00
arXiv:2604.03955v1 Announce Type: cross Abstract: When autonomous agents observe different domains of a shared environment, each signal they exchange mixes relevant and irrelevant dimensions. No existing mechanism lets the receiver evaluate which dimensions to absorb. We introduce Symbolic-Vector Attention Fusion (SVAF), the content-evaluation half of a two-level coupling engine for collective intelligence. SVAF decomposes each inter-agent signal into 7 typed semantic fields, evaluates each through a learned fusion gate, and produces a remix -- new knowledge from the intersection of two domains. A band-pass model yields four outcomes (redundant, aligned, guarded, rejected), solving both selectivity and redundancy. The fusion gate independently discovers a cross-domain relevance hierarchy: mood emerges as the highest-weight field by epoch 1, before accuracy plateaus -- consistent with independent mechanistic evidence that LLM emotion representations are structurally embedded along valence-arousal axes. SVAF forms Layer 4 of the Mesh Memory Protocol (MMP); the other half of the coupling engine is a per-agent Closed-form Continuous-time (CfC) neural network at Layer 6, whose learned per-neuron time constants (tau) create the temporal dynamics from which collective intelligence emerges: fast neurons synchronise affect across agents in seconds, while slow neurons preserve domain expertise indefinitely. SVAF determines what enters each agent's cognitive state; CfC determines how that state evolves. Trained on 237K samples from 273 narrative scenarios, SVAF achieves 78.7% three-class accuracy. We verify the complete mesh cognition loop -- from per-field evaluation through remix, CfC state evolution, tau-modulated peer blending, and autonomous action -- in a live deployment with 7 nodes across macOS, iOS, and web.

ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wangsong Yin · Daliang Xu · Mengwei Xu · Gang Huang · Xuanzhe Liu
7 April 2026 at 12:00
arXiv:2508.16703v3 Announce Type: replace-cross Abstract: On-device running Large Language Models (LLMs) is nowadays a critical enabler towards preserving user privacy. We observe that the attention operator falls back from the special-purpose NPU to the general-purpose CPU/GPU because of quantization sensitivity in state-of-the-art frameworks. This fallback results in a degraded user experience and increased complexity in system scheduling. To this end, this paper presents shadowAttn, a system-algorithm codesigned sparse attention module with minimal reliance on CPU/GPU by only sparsely calculating the attention on a tiny portion of tokens. The key idea is to hide the overhead of estimating the important tokens with a NPU-based pilot compute. Further, shadowAttn proposes insightful techniques such as NPU compute graph bucketing, head-wise NPU-CPU/GPU pipeline and per-head fine-grained sparsity ratio to achieve high accuracy and efficiency. shadowAttn delivers the best performance with highly limited CPU/GPU resource; it requires much less CPU/GPU resource to deliver on-par performance of SoTA frameworks.
Received — 2 April 2026 ⏭

Semantic Voting: A Self-Evaluation-Free Approach for Efficient LLM Self-Improvement on Unverifiable Open-ended Tasks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chunyang Jiang · Yonggang Zhang · Yiyang Cai · Chi-Min Chan · Yulong Liu · Mingming Chen · Wei Xue · Yike Guo
1 April 2026 at 12:00
arXiv:2509.23067v2 Announce Type: replace-cross Abstract: The rising cost of acquiring supervised data has driven significant interest in self-improvement for large language models (LLMs). Straightforward unsupervised signals like majority voting have proven effective in generating pseudo-labels for verifiable tasks, while their applicability to unverifiable tasks (e.g., translation) is limited by the open-ended character of responses. As a result, self-evaluation mechanisms (e.g., self-judging and entropy minimization) are predominantly used to derive pseudo-labels. However, self-evaluation relying on LLMs typically incurs high computational overhead and introduces overconfidence issues due to intrinsic biases. To address these challenges, we propose a novel self-evaluation-free approach for unverifiable tasks, designed for lightweight yet effective self-improvement. Inspired by majority voting commonly employed in verifiable tasks, we propose semantic voting as a novel mechanism that relaxes the principle of hard matching (i.e., exact matching) toward soft matching (i.e., semantic similarity). Soft matching is achieved by leveraging a lightweight sentence embedding model to quantify semantic similarity, thereby mitigating excessive computational burden and intrinsic bias-associated limitations of self-evaluation. Comprehensive experiments demonstrate that our method achieves substantial gains in computational efficiency and overall better performance than self-evaluation methods across diverse model architectures and tasks.

Correction: The multifunctional RNA helicase DDX39A drives glioblastoma progression by modulating WISP1 alternative splicing that induces an immunosuppressive macrophage polarization

✇Oncogene - Issue - nature.com science feeds
By: Yan Zhang · Zhiwei Xue · Naibin Zhang · Yuehua Zhu · YanZhao Wu · Meilin Lv · Zhihan Zhang · Feiyu Mu · Wenchen Xing · Ziyi Tang · Chunjie Wang · Zhiyi Xue · Wenjing Zhou · Xiaofei Liu · Xingang Li · Rolf Bjerkvig · Bin Huang · Mingzhi Han · Jian Wang · Donghai Wang
1 April 2026 at 08:00

Oncogene, Published online: 01 April 2026; doi:10.1038/s41388-026-03756-2

Correction: The multifunctional RNA helicase DDX39A drives glioblastoma progression by modulating WISP1 alternative splicing that induces an immunosuppressive macrophage polarization

Nanoscale transfer-printed full-colour ultrahigh-resolution quantum dot LEDs

✇Nature - Issue - nature.com science feeds
By: Lihua Lin · Jie Wang · Hailong Hu · Haolin Luo · Yanbin Liu · Xingjie Yang · Jingnan Su · De’er Li · Zhongwei Xu · Chengyu Luo · Yongshen Yu · Tailiang Guo · Fushan Li
1 April 2026 at 08:00

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10333-w

A dual-action force dynamics strategy using a hard silicon template as a nanoimprinting stamp combined with inverted transfer printing is described for the manufacture of high-performance full-colour ultrahigh-resolution quantum dot light-emitting diodes (LEDs) for active-matrix displays, while revealing electric-field reconstruction in nanoscale arrays and introducing dielectric matching to mitigate field concentration and performance degradation.
Received — 26 March 2026 ⏭

Comprehensive multi omics profiling and Mendelian randomization assessment of lipid metabolites in lung cancer prognosis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zhiwei Xu · Jingjing Li
23 March 2026 at 18:00

Discov Oncol. 2026 Mar 23. doi: 10.1007/s12672-026-04893-6. Online ahead of print.

ABSTRACT

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide. This study aimed to develop prognostic prediction models for lung squamous cell carcinoma (LUSC) through multi-omics integration using Mendelian randomization analysis.This study addresses a critical gap in lung cancer research through two complementary approaches in major lung cancer subtypes: (1) hypothesis-generating multi-omics analysis in LUSC to identify prognostic biomarkers and characterize the metabolic-immune landscape. This integrated framework provides both predictive tools for personalized medicine and mechanistic insights into metabolic causality.

METHODS: Multi-omics analysis was performed using TCGA data, including RNA-seq, DNA methylation, and whole-exome sequencing. Machine learning models incorporating 15 algorithms were developed and externally validated in two independent GEO cohorts. Mendelian randomization analysis assessed causal relationships between 32 lipid metabolites and SCLC risk. RT-qPCR experiments validated key prognostic genes in lung squamous cell carcinoma (LUSC) cell lines.

RESULTS: The optimal machine learning model (StepCox [forward] + Random Survival Forest) demonstrated superior performance with C-index of 0.73 in internal testing and 0.71 and 0.68 in external validation cohorts. High CD8 + T cell and M1 macrophage infiltration was associated with favorable prognosis. Most lipid metabolites showed no significant causal associations with SCLC risk after multiple testing correction, though two phosphatidylcholine metabolites demonstrated potential protective effects. RT-qPCR validation confirmed significant upregulation of all four key genes in LUSC cell lines.

CONCLUSIONS: This study successfully developed robust machine learning-based prognostic models for LUSC with clinical utility for risk stratification and provided evidence that lipid alterations in lung cancer are likely downstream consequences rather than causal drivers of tumorigenesis.

PMID:41870745 | DOI:10.1007/s12672-026-04893-6

Comprehensive multi omics profiling and Mendelian randomization assessment of lipid metabolites in lung cancer prognosis

✇Omics In Lung
By: Zhiwei Xu · Jingjing Li
23 March 2026 at 18:00

Discov Oncol. 2026 Mar 23. doi: 10.1007/s12672-026-04893-6. Online ahead of print.

ABSTRACT

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide. This study aimed to develop prognostic prediction models for lung squamous cell carcinoma (LUSC) through multi-omics integration using Mendelian randomization analysis.This study addresses a critical gap in lung cancer research through two complementary approaches in major lung cancer subtypes: (1) hypothesis-generating multi-omics analysis in LUSC to identify prognostic biomarkers and characterize the metabolic-immune landscape. This integrated framework provides both predictive tools for personalized medicine and mechanistic insights into metabolic causality.

METHODS: Multi-omics analysis was performed using TCGA data, including RNA-seq, DNA methylation, and whole-exome sequencing. Machine learning models incorporating 15 algorithms were developed and externally validated in two independent GEO cohorts. Mendelian randomization analysis assessed causal relationships between 32 lipid metabolites and SCLC risk. RT-qPCR experiments validated key prognostic genes in lung squamous cell carcinoma (LUSC) cell lines.

RESULTS: The optimal machine learning model (StepCox [forward] + Random Survival Forest) demonstrated superior performance with C-index of 0.73 in internal testing and 0.71 and 0.68 in external validation cohorts. High CD8 + T cell and M1 macrophage infiltration was associated with favorable prognosis. Most lipid metabolites showed no significant causal associations with SCLC risk after multiple testing correction, though two phosphatidylcholine metabolites demonstrated potential protective effects. RT-qPCR validation confirmed significant upregulation of all four key genes in LUSC cell lines.

CONCLUSIONS: This study successfully developed robust machine learning-based prognostic models for LUSC with clinical utility for risk stratification and provided evidence that lipid alterations in lung cancer are likely downstream consequences rather than causal drivers of tumorigenesis.

PMID:41870745 | DOI:10.1007/s12672-026-04893-6

Single-cell multiomics uncovers an endothelial mechanosensitive PIEZO1-IL-33 axis driving pulmonary fibrosis

✇Omics In Lung
By: Lanlan Zhang · Xuezhen Gui · Ruijie Hou · Liping Jia · Shu Xia · Xin Zhang · Yingyun Fu · Qian-Fang Meng · Qun Luo · Xing Shi · Bingxin Guo · Ruifang Liang · Ludan Yue · Xue Chen · Haizhao Xu · Pengbo Wang · Xia Tong · Lujie Liu · Lingwei Wang · Baicun Li · Zi Chen · Linfu Zhou · Linshu Zhang · Rongchang Chen · Changbin Sun · Wei Xu · Lang Rao · Haibo Zhou · Bi-Sen Ding · Shanze Chen
21 March 2026 at 18:00

Nat Commun. 2026 Mar 20;17(1):2655. doi: 10.1038/s41467-026-70193-w.

ABSTRACT

Pulmonary fibrosis represents a progressive interstitial lung disease marked by excessive extracellular matrix deposition and architectural distortion. Vascular endothelial cells critically contribute to fibrogenesis through paracrine secretion of pro-fibrotic mediators, yet their mechanobiological regulation remains elusive. Using integrated single-cell multi-omics profiling of human pulmonary fibrosis specimens and experimental fibrosis models induced by bleomycin or silica, we identify mechanosensitive Piezo1 upregulation in Endothelial cells as a hallmark of fibrotic progression. Endothelial-specific Piezo1 knockout significantly attenuates Bleomycin-induced fibrotic remodeling in male mice, establishing its pathogenic necessity. Mechanistically, PIEZO1 activation promotes pulmonary fibrosis development via CAPN2-mediated STAT3 phosphorylation, which may regulate the secretion of the pro-fibrotic molecule interleukin-33. These findings suggest that the endothelial PIEZO1-CAPN2-STAT3-IL33 axis is a potential therapeutic target for PF intervention.

PMID:41862476 | PMC:PMC13004862 | DOI:10.1038/s41467-026-70193-w

Received — 11 March 2026 ⏭

DC-W2S: Dual-Consensus Weak-to-Strong Training for Reliable Process Reward Modeling in Biological Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chi-Min Chan · Ehsan Hajiramezanali · Xiner Li · Edward De Brouwer · Carl Edwards · Wei Xue · Sirui Han · Yike Guo · Gabriele Scalia
10 March 2026 at 12:00
arXiv:2603.08095v1 Announce Type: cross Abstract: In scientific reasoning tasks, the veracity of the reasoning process is as critical as the final outcome. While Process Reward Models (PRMs) offer a solution to the coarse-grained supervision problems inherent in Outcome Reward Models (ORMs), their deployment is hindered by the prohibitive cost of obtaining expert-verified step-wise labels. This paper addresses the challenge of training reliable PRMs using abundant but noisy "weak" supervision. We argue that existing Weak-to-Strong Generalization (W2SG) theories lack prescriptive guidelines for selecting high-quality training signals from noisy data. To bridge this gap, we introduce the Dual-Consensus Weak-to-Strong (DC-W2S) framework. By intersecting Self-Consensus (SC) metrics among weak supervisors with Neighborhood-Consensus (NC) metrics in the embedding space, we stratify supervision signals into distinct reliability regimes. We then employ a curriculum of instance-level balanced sampling and label-level reliability-aware masking to guide the training process. We demonstrate that DC-W2S enables the training of robust PRMs for complex reasoning without exhaustive expert annotation, proving that strategic data curation is more effective than indiscriminate training on large-scale noisy datasets.
Received — 5 March 2026 ⏭

Phys4D: Fine-Grained Physics-Consistent 4D Modeling from Video Diffusion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haoran Lu · Shang Wu · Jianshu Zhang · Maojiang Su · Guo Ye · Chenwei Xu · Lie Lu · Pranav Maneriker · Fan Du · Manling Li · Zhaoran Wang · Han Liu
5 March 2026 at 13:00
arXiv:2603.03485v1 Announce Type: cross Abstract: Recent video diffusion models have achieved impressive capabilities as large-scale generative world models. However, these models often struggle with fine-grained physical consistency, exhibiting physically implausible dynamics over time. In this work, we present \textbf{Phys4D}, a pipeline for learning physics-consistent 4D world representations from video diffusion models. Phys4D adopts \textbf{a three-stage training paradigm} that progressively lifts appearance-driven video diffusion models into physics-consistent 4D world representations. We first bootstrap robust geometry and motion representations through large-scale pseudo-supervised pretraining, establishing a foundation for 4D scene modeling. We then perform physics-grounded supervised fine-tuning using simulation-generated data, enforcing temporally consistent 4D dynamics. Finally, we apply simulation-grounded reinforcement learning to correct residual physical violations that are difficult to capture through explicit supervision. To evaluate fine-grained physical consistency beyond appearance-based metrics, we introduce a set of \textbf{4D world consistency evaluation} that probe geometric coherence, motion stability, and long-horizon physical plausibility. Experimental results demonstrate that Phys4D substantially improves fine-grained spatiotemporal and physical consistency compared to appearance-driven baselines, while maintaining strong generative performance. Our project page is available at https://sensational-brioche-7657e7.netlify.app/

PhyPrompt: RL-based Prompt Refinement for Physically Plausible Text-to-Video Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shang Wu · Chenwei Xu · Zhuofan Xia · Weijian Li · Lie Lu · Pranav Maneriker · Fan Du · Manling Li · Han Liu
5 March 2026 at 13:00
arXiv:2603.03505v1 Announce Type: cross Abstract: State-of-the-art text-to-video (T2V) generators frequently violate physical laws despite high visual quality. We show this stems from insufficient physical constraints in prompts rather than model limitations: manually adding physics details reliably produces physically plausible videos, but requires expertise and does not scale. We present PhyPrompt, a two-stage reinforcement learning framework that automatically refines prompts for physically realistic generation. First, we fine-tune a large language model on a physics-focused Chain-of-Thought dataset to integrate principles like object motion and force interactions while preserving user intent. Second, we apply Group Relative Policy Optimization with a dynamic reward curriculum that initially prioritizes semantic fidelity, then progressively shifts toward physical commonsense. This curriculum achieves synergistic optimization: PhyPrompt-7B reaches 40.8\% joint success on VideoPhy2 (8.6pp gain), improving physical commonsense by 11pp (55.8\% to 66.8\%) while simultaneously increasing semantic adherence by 4.4pp (43.4\% to 47.8\%). Remarkably, our curriculum exceeds single-objective training on both metrics, demonstrating compositional prompt discovery beyond conventional multi-objective trade-offs. PhyPrompt outperforms GPT-4o (+3.8\% joint) and DeepSeek-V3 (+2.2\%, 100$\times$ larger) using only 7B parameters. The approach transfers zero-shot across diverse T2V architectures (Lavie, VideoCrafter2, CogVideoX-5B) with up to 16.8\% improvement, establishing that domain-specialized reinforcement learning with compositional curricula surpasses general-purpose scaling for physics-aware generation.

How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ziwen Xu · Kewei Xu · Haoming Xu · Haiwen Hong · Longtao Huang · Hui Xue · Ningyu Zhang · Yongliang Shen · Guozhou Zheng · Huajun Chen · Shumin Deng
4 March 2026 at 13:00
arXiv:2603.02578v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors, ranging from misaligned intent to inconsistent personality, pose significant risks. We introduce SteerEval, a hierarchical benchmark for evaluating LLM controllability across three domains: language features, sentiment, and personality. Each domain is structured into three specification levels: L1 (what to express), L2 (how to express), and L3 (how to instantiate), connecting high-level behavioral intent to concrete textual output. Using SteerEval, we systematically evaluate contemporary steering methods, revealing that control often degrades at finer-grained levels. Our benchmark offers a principled and interpretable framework for safe and controllable LLM behavior, serving as a foundation for future research.
Received — 25 February 2026 ⏭

Buy versus Build an LLM: A Decision Framework for Governments

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiahao Lu · Ziwei Xu · William Tjhi · Junnan Li · Antoine Bosselut · Pang Wei Koh · Mohan Kankanhalli
24 February 2026 at 13:00
arXiv:2602.13033v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) represent a new frontier of digital infrastructure that can support a wide range of public-sector applications, from general purpose citizen services to specialized and sensitive state functions. When expanding AI access, governments face a set of strategic choices over whether to buy existing services, build domestic capabilities, or adopt hybrid approaches across different domains and use cases. These are critical decisions especially when leading model providers are often foreign corporations, and LLM outputs are increasingly treated as trusted inputs to public decision-making and public discourse. In practice, these decisions are not intended to mandate a single approach across all domains; instead, national AI strategies are typically pluralistic, with sovereign, commercial and open-source models coexisting to serve different purposes. Governments may rely on commercial models for non-sensitive or commodity tasks, while pursuing greater control for critical, high-risk or strategically important applications. This paper provides a strategic framework for making this decision by evaluating these options across dimensions including sovereignty, safety, cost, resource capability, cultural fit, and sustainability. Importantly, "building" does not imply that governments must act alone: domestic capabilities may be developed through public research institutions, universities, state-owned enterprises, joint ventures, or broader national ecosystems. By detailing the technical requirements and practical challenges of each pathway, this work aims to serve as a reference for policy-makers to determine whether a buy or build approach best aligns with their specific national needs and societal goals.
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