How nanophotonics can drive optical computing toward practical applications

By: Yitong Chen · Guoqiang Yang · Tao Yan · Chunyang Tang · Jiamin Wu · Qionghai Dai

9 September 2026 at 08:00

Nature Nanotechnology, Published online: 09 September 2026; doi:10.1038/s41565-026-02264-4

This Review discusses photonic platforms that have already shown programmability, high integration density, and stability, and that can support large-scale AI models, edge computing, and logic and scientific computing.

NPSolver: Neural Poisson Solver with Iterative Physics Supervision

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

By: Bocheng Zeng · Rui Zhang · Runze Mao · Mengtao Yan · Xuan Bai · Yang Liu · Zhi X. Chen · Hao Sun

26 May 2026 at 12:00

arXiv:2605.25786v1 Announce Type: cross Abstract: Efficiently solving Poisson equations on complex, irregular domains remains a fundamental challenge in scientific computing, as classical iterative solvers often suffer from prohibitive runtime due to ill-conditioned systems. While neural operators offer a fast alternative, they typically rely on large-scale labeled datasets or struggle with unstable training dynamics when using physics-informed residual losses. We propose \textsc{NPSolver}, a neural Poisson solver trained without solution labels via iterative physics supervision. Instead of relying on fully converged numerical solutions or raw PDE residuals, \textsc{NPSolver} utilizes a small number of preconditioned conjugate gradient (PCG) steps to refine its own predictions, providing a more stable and well-scaled training signal. Theoretical analysis confirms that this iterative supervision serves as a well-conditioned error proxy and that a stop-gradient design is essential for optimization stability. To better capture boundary-driven features under mixed boundary conditions, we further introduce the Boundary-Aware Transolver (\textsc{BA-Transolver}) architecture that explicitly separates interior and boundary tokenization. Extensive evaluations on 2D and 3D irregular geometries demonstrate that \textsc{NPSolver} outperforms both physics-informed and data-driven baselines. Furthermore, a downstream thermal control task highlights the model's capability for conducting efficient and reliable gradient-based boundary control. We will release our codes and data at https://github.com/intell-sci-comput/NPSolver.

High-throughput strategy for targeting MDM2 in uveal melanoma to reverse radiation therapy resistance

Cell Death Discovery nature.com science feeds

By: Qi Zhu · Xi Gong · Sirong Zhang · Jiaqi Zhang · Hongtao Yan

11 April 2026 at 08:00

Cell Death Discovery, Published online: 11 April 2026; doi:10.1038/s41420-026-02970-x

High-throughput strategy for targeting MDM2 in uveal melanoma to reverse radiation therapy resistance

PsychAgent: An Experience-Driven Lifelong Learning Agent for Self-Evolving Psychological Counselor

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

By: Yutao Yang · Junsong Li · Qianjun Pan · Jie Zhou · Kai Chen · Qin Chen · Jingyuan Zhao · Ningning Zhou · Xin Li · Liang He

3 April 2026 at 12:00

arXiv:2604.00931v2 Announce Type: replace Abstract: Existing methods for AI psychological counselors predominantly rely on supervised fine-tuning using static dialogue datasets. However, this contrasts with human experts, who continuously refine their proficiency through clinical practice and accumulated experience. To bridge this gap, we propose an Experience-Driven Lifelong Learning Agent (\texttt{PsychAgent}) for psychological counseling. First, we establish a Memory-Augmented Planning Engine tailored for longitudinal multi-session interactions, which ensures therapeutic continuity through persistent memory and strategic planning. Second, to support self-evolution, we design a Skill Evolution Engine that extracts new practice-grounded skills from historical counseling trajectories. Finally, we introduce a Reinforced Internalization Engine that integrates the evolved skills into the model via rejection fine-tuning, aiming to improve performance across diverse scenarios. Comparative analysis shows that our approach achieves higher scores than strong general LLMs (e.g., GPT-5.4, Gemini-3) and domain-specific baselines across all reported evaluation dimensions. These results suggest that lifelong learning can improve the consistency and overall quality of multi-session counseling responses.

NI-Tex: Non-isometric Image-based Garment Texture Generation

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

By: Hui Shan · Ming Li · Haitao Yang · Kai Zheng · Sizhe Zheng · Yanwei Fu · Xiangru Huang

16 March 2026 at 12:00

arXiv:2511.18765v2 Announce Type: replace-cross Abstract: Existing industrial 3D garment meshes already cover most real-world clothing geometries, yet their texture diversity remains limited. To acquire more realistic textures, generative methods are often used to extract Physically-based Rendering (PBR) textures and materials from large collections of wild images and project them back onto garment meshes. However, most image-conditioned texture generation approaches require strict topological consistency between the input image and the input 3D mesh, or rely on accurate mesh deformation to match to the image poses, which significantly constrains the texture generation quality and flexibility. To address the challenging problem of non-isometric image-based garment texture generation, we construct 3D Garment Videos, a physically simulated, garment-centric dataset that provides consistent geometry and material supervision across diverse deformations, enabling robust cross-pose texture learning. We further employ Nano Banana for high-quality non-isometric image editing, achieving reliable cross-topology texture generation between non-isometric image-geometry pairs. Finally, we propose an iterative baking method via uncertainty-guided view selection and reweighting that fuses multi-view predictions into seamless, production-ready PBR textures. Through extensive experiments, we demonstrate that our feedforward dual-branch architecture generates versatile and spatially aligned PBR materials suitable for industry-level 3D garment design.

Latilactobacillus curvatus IM01 Alleviates Allergic Airway Inflammation Through Microbial and Metabolic Crosstalk Along the Gut-Lung Axis

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

By: Yujia He · Jing Liu · Tao Yang · Yuanming Huang · Liqiong Song · Zhihong Ren

14 March 2026 at 18:00

Nutrients. 2026 Mar 4;18(5):834. doi: 10.3390/nu18050834.

ABSTRACT

Background: Gut microbiota dysbiosis is critically implicated in the pathogenesis of allergic airway inflammation (AAI) via the gut-lung axis. While Latilactobacillus curvatus is a promising probiotic candidate, its specific immunomodulatory mechanisms in respiratory diseases remain poorly understood. Objective: In this study, we investigated the protective effects and underlying mechanisms of L. curvatus IM01 in an ovalbumin (OVA)-induced murine AAI model using an integrated multi-omics approach. Results: Our results demonstrated that oral administration of L. curvatus IM01 significantly attenuated airway inflammation, suppressed Th2-type immune responses, and reduced serum IgE levels. Crucially, our multi-omics integration revealed a coherent gut-lung axis narrative driven by microbial and metabolic crosstalk. Specifically, 16S rRNA sequencing indicated that L. curvatus IM01 was closely linked to structural shifts in the gut microbial community, notably characterized by an enrichment trend for beneficial genera such as Odoribacter and Lactobacillus. This microbial restructuring was closely associated with a modulated cecal metabolic profile, as untargeted metabolomics exhibited a clear trend toward the restoration of key systemically active immunoregulatory metabolites, including indolelactic acid (ILA) and choline, which have been previously linked to the alleviation of AAI symptoms. Further linking this metabolic shift to respiratory immune tolerance, lung transcriptomic analysis showed that the treatment is strongly associated with the promotion of the differentiation of CD4⁺ T cells into Foxp3⁺ regulatory T cells (Tregs). Conclusions: Collectively, these findings suggest a novel potential pathway by which L. curvatus IM01 modulates the gut-lung axis through coordinated microbial and metabolic interventions, highlighting its potential as a therapeutic functional food ingredient for AAI.

PMID:41830004 | PMC:PMC12987261 | DOI:10.3390/nu18050834

Latilactobacillus curvatus IM01 Alleviates Allergic Airway Inflammation Through Microbial and Metabolic Crosstalk Along the Gut-Lung Axis

Omics In Lung

By: Yujia He · Jing Liu · Tao Yang · Yuanming Huang · Liqiong Song · Zhihong Ren

14 March 2026 at 18:00

Nutrients. 2026 Mar 4;18(5):834. doi: 10.3390/nu18050834.

ABSTRACT

Background: Gut microbiota dysbiosis is critically implicated in the pathogenesis of allergic airway inflammation (AAI) via the gut-lung axis. While Latilactobacillus curvatus is a promising probiotic candidate, its specific immunomodulatory mechanisms in respiratory diseases remain poorly understood. Objective: In this study, we investigated the protective effects and underlying mechanisms of L. curvatus IM01 in an ovalbumin (OVA)-induced murine AAI model using an integrated multi-omics approach. Results: Our results demonstrated that oral administration of L. curvatus IM01 significantly attenuated airway inflammation, suppressed Th2-type immune responses, and reduced serum IgE levels. Crucially, our multi-omics integration revealed a coherent gut-lung axis narrative driven by microbial and metabolic crosstalk. Specifically, 16S rRNA sequencing indicated that L. curvatus IM01 was closely linked to structural shifts in the gut microbial community, notably characterized by an enrichment trend for beneficial genera such as Odoribacter and Lactobacillus. This microbial restructuring was closely associated with a modulated cecal metabolic profile, as untargeted metabolomics exhibited a clear trend toward the restoration of key systemically active immunoregulatory metabolites, including indolelactic acid (ILA) and choline, which have been previously linked to the alleviation of AAI symptoms. Further linking this metabolic shift to respiratory immune tolerance, lung transcriptomic analysis showed that the treatment is strongly associated with the promotion of the differentiation of CD4⁺ T cells into Foxp3⁺ regulatory T cells (Tregs). Conclusions: Collectively, these findings suggest a novel potential pathway by which L. curvatus IM01 modulates the gut-lung axis through coordinated microbial and metabolic interventions, highlighting its potential as a therapeutic functional food ingredient for AAI.

PMID:41830004 | PMC:PMC12987261 | DOI:10.3390/nu18050834

SynPlanResearch-R1: Encouraging Tool Exploration for Deep Research with Synthetic Plans

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

By: Hansi Zeng · Zoey Li · Yifan Gao · Chenwei Zhang · Xiaoman Pan · Tao Yang · Fengran Mo · Jiacheng Lin · Xian Li · Jingbo Shang

10 March 2026 at 12:00

arXiv:2603.07853v1 Announce Type: new Abstract: Research Agents enable models to gather information from the web using tools to answer user queries, requiring them to dynamically interleave internal reasoning with tool use. While such capabilities can in principle be learned via reinforcement learning with verifiable rewards (RLVR), we observe that agents often exhibit poor exploration behaviors, including premature termination and biased tool usage. As a result, RLVR alone yields limited improvements. We propose SynPlanResearch-R1, a framework that synthesizes tool-use trajectories that encourage deeper exploration to shape exploration during cold-start supervised fine-tuning, providing a strong initialization for subsequent RL. Across seven multi-hop and open-web benchmarks, \framework improves performance by up to 6.0% on Qwen3-8B and 5.8% on Qwen3-4B backbones respectively compared to SOTA baselines. Further analyses of tool-use patterns and training dynamics compared to baselines shed light on the factors underlying these gains. Our code is publicly available at https://github.com/HansiZeng/syn-plan-research.

Discriminative Perception via Anchored Description for Reasoning Segmentation

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

By: Tao Yang · Qing Zhou · Yanliang Li · Qi Wang

5 March 2026 at 13:00

arXiv:2603.04002v1 Announce Type: cross Abstract: Reasoning segmentation increasingly employs reinforcement learning to generate explanatory reasoning chains that guide Multimodal Large Language Models. While these geometric rewards are primarily confined to guiding the final localization, they are incapable of discriminating whether the reasoning process remains anchored on the referred region or strays into irrelevant context. Lacking this discriminative guidance, the model's reasoning often devolves into unfocused and verbose chains that ultimately fail to disambiguate and perceive the target in complex scenes. This suggests a need to complement the RL objective with Discriminative Perception, an ability to actively distinguish a target from its context. To realize this, we propose DPAD to compel the model to generate a descriptive caption of the referred object, which is then used to explicitly discriminate by contrasting the caption's semantic relevance to the referred object against the wider context. By optimizing for this discriminative capability, the model is forced to focus on the unique attributes of the target, leading to a more converged and efficient reasoning chain. The descriptive caption also serves as an interpretability rationale that aligns with the segmentation. Experiments on the benchmarks confirm the validity of our approach, delivering substantial performance gains, with the cIoU on ReasonSeg increasing by 3.09% and the reasoning chain length decreasing by approximately 42%. Code is available at https://github.com/mrazhou/DPAD

3D Wavelet-Based Structural Priors for Controlled Diffusion in Whole-Body Low-Dose PET Denoising

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

By: Peiyuan Jing · Yue Yang · Chun-Wun Cheng · Zhenxuan Zhang · Liutao Yang · Thiago V. Lima · Klaus Strobel · Antoine Leimgruber · Angelica Aviles-Rivero · Guang Yang · Javier A. Montoya-Zegarra

5 March 2026 at 13:00

arXiv:2601.07093v4 Announce Type: replace-cross Abstract: Low-dose Positron Emission Tomography (PET) imaging reduces patient radiation exposure but suffers from increased noise that degrades image quality and diagnostic reliability. Although diffusion models have demonstrated strong denoising capability, their stochastic nature makes it challenging to enforce anatomically consistent structures, particularly in low signal-to-noise regimes and volumetric whole-body imaging. We propose Wavelet-Conditioned ControlNet (WCC-Net), a fully 3D diffusion-based framework that introduces explicit frequency-domain structural priors via wavelet representations to guide volumetric PET denoising. By injecting wavelet-based structural guidance into a frozen pretrained diffusion backbone through a lightweight control branch, WCC-Net decouples anatomical structure from noise while preserving generative expressiveness and 3D structural continuity. Extensive experiments demonstrate that WCC-Net consistently outperforms CNN-, GAN-, and diffusion-based baselines. On the internal 1/20-dose test set, WCC-Net improves PSNR by +1.21 dB and SSIM by +0.008 over a strong diffusion baseline, while reducing structural distortion (GMSD) and intensity error (NMAE). Moreover, WCC-Net generalizes robustly to unseen dose levels (1/50 and 1/4), achieving superior quantitative performance and improved volumetric anatomical consistency.

NI-Tex: Non-isometric Image-based Garment Texture Generation

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

By: Hui Shan · Ming Li · Haitao Yang · Kai Zheng · Sizhe Zheng · Yanwei Fu · Xiangru Huang

24 February 2026 at 13:00

arXiv:2511.18765v1 Announce Type: cross Abstract: Existing industrial 3D garment meshes already cover most real-world clothing geometries, yet their texture diversity remains limited. To acquire more realistic textures, generative methods are often used to extract Physically-based Rendering (PBR) textures and materials from large collections of wild images and project them back onto garment meshes. However, most image-conditioned texture generation approaches require strict topological consistency between the input image and the input 3D mesh, or rely on accurate mesh deformation to match to the image poses, which significantly constrains the texture generation quality and flexibility. To address the challenging problem of non-isometric image-based garment texture generation, we construct 3D Garment Videos, a physically simulated, garment-centric dataset that provides consistent geometry and material supervision across diverse deformations, enabling robust cross-pose texture learning. We further employ Nano Banana for high-quality non-isometric image editing, achieving reliable cross-topology texture generation between non-isometric image-geometry pairs. Finally, we propose an iterative baking method via uncertainty-guided view selection and reweighting that fuses multi-view predictions into seamless, production-ready PBR textures. Through extensive experiments, we demonstrate that our feedforward dual-branch architecture generates versatile and spatially aligned PBR materials suitable for industry-level 3D garment design.

OmniVideo-R1: Reinforcing Audio-visual Reasoning with Query Intention and Modality Attention

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

By: Zhangquan Chen · Jiale Tao · Ruihuang Li · Yihao Hu · Ruitao Chen · Zhantao Yang · Xinlei Yu · Haodong Jing · Manyuan Zhang · Shuai Shao · Biao Wang · Qinglin Lu · Ruqi Huang

17 February 2026 at 13:00

arXiv:2602.05847v2 Announce Type: replace Abstract: While humans perceive the world through diverse modalities that operate synergistically to support a holistic understanding of their surroundings, existing omnivideo models still face substantial challenges on audio-visual understanding tasks. In this paper, we propose OmniVideo-R1, a novel reinforced framework that improves mixed-modality reasoning. OmniVideo-R1 empowers models to "think with omnimodal cues" by two key strategies: (1) query-intensive grounding based on self-supervised learning paradigms; and (2) modality-attentive fusion built upon contrastive learning paradigms. Extensive experiments on multiple benchmarks demonstrate that OmniVideo-R1 consistently outperforms strong baselines, highlighting its effectiveness and robust generalization capabilities.

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