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Received — 18 September 2026 ⏭

Inhalable carrier-free self-assembled leonurine-ursolic acid nanoaggregates ameliorate acute lung injury by suppressing TLR4/MyD88-NET axis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zhen-Zhen Wang · Lihua Cao · Zhenzhu Zhao · Mengke Zhao · Yanyan Lu · Yanhui Zhu · Weijie Xie · Ping Li · Siqi Liu · Xuejiao Li · Kang Yang · Xiaobo Sun
17 September 2026 at 18:00

Mater Today Bio. 2026 Aug 18;40:103583. doi: 10.1016/j.mtbio.2026.103583. eCollection 2026 Oct.

ABSTRACT

TLR4 activation and the cascade of neutrophil extracellular trap (NET) formation exacerbate excessive inflammation and organ damage in the pathogenesis of acute lung injury (ALI), yet effective pharmacological interventions remain unavailable. Nanoaggregates derived from natural products offer promising avenue by leveraging synergistic anti-inflammatory effects. In this study, we surprisingly discovered that leonurine and ursolic acid spontaneously self-assemble into nanoparticles (LUNP) through non-covalent interactions, achieving a drug loading capacity of 100%. The LUNP platform exhibits superior biophysical properties, including enhanced mucus penetration, pH-responsive drug release, improved cellular uptake, and prolonged retention within inflamed lung tissue. Mechanistically, LUNP ameliorates ALI by dampening TLR4/MyD88/NF-κB-driven inflammatory activation, thereby remodeling the microenvironment to limit NOX4-PAD4-mediated NET formation. Notably, inhalational LUNP exhibits outstanding biosafety with minimal off-target distribution. Overall, this work introduces a synergistic self-assembled nanoplatform for precise pulmonary intervention in ALI, showcasing its ability to safely and effectively orchestrate the coordinated modulation of multiple pathological pathways. In summary, by inhibiting both TLR4 activation and NET formation, the synergistic LUNP platform offers an efficient, safe, and easily accessible therapeutic strategy for ALI, providing a promising solution for clinical translation.

PMID:42750707 | PMC:PMC13577835 | DOI:10.1016/j.mtbio.2026.103583

Synthetic transcription factors designed by domain recombination enhance CAR T cell antitumor function

✇Cell
By: Oliver Takacsi-Nagy · Sivakanthan Kasinathan · Austin Hartman · Yajie Yin · Lujing Wu · Andy Y. Chen · Laura M. Moser · Audre P. May · Gabriella C. Reeder · Emily Celallos Fuentes · Courtney Kernick · Johnathan Lu · Alison K. McClellan · Colin J. Raposo · Brendan Terrall · Nicole E. Theberath · Patrick K. Yan · Peng Xu · Elena Sotillo · Justin Eyquem · Crystal L. Mackall · Theodore L. Roth · Ansuman T. Satpathy
19 August 2026 at 08:00
Recombining domains across an entire protein family, rather than relying on natural sequences shaped by evolution, generates synthetic “DESynR” transcription factors with enhanced function. DESynR AP-1 TFs reprogram CAR T cells into non-natural, therapeutically optimized states and outperform natural AP-1 factors in antitumor immunity.

Advancing cancer detection and treatment using longitudinal routine clinical data

✇Cell
By: Fei Liu · Kai Wang · Hui Xu · Cheng Tang · Xian Shen · Meihao Wang · Lei Yang · Li Yang · Li Liu · Changxi Hu · Gen Li · Wei Wu · Zixing Zou · Bingzhou Li · Sian Liu · Jin Kang · Jungho Kong · Ting Li · Io Nam Wong · Xiaoying Huang · Gang Chen · Wenyang Lu · Ian Ziyar · Charlotte L. Zhang · Yiwen Sun · Weihong Lin · Caiwen Ou · Manson Fok · Taiwa Hou · Winston Wang · Kanmin Xue · Yun Yin · Hao Zhu · Jonathan Gootenberg · Omar O. Abudayyeh · Michael Karin · Alexandre Loupy · John E.J. Rasko · Trey Ideker · Huiyan Luo · Eric Oermann · Kang Zhang · International Consortium of Digital Twins in Healthcare and Medicine
27 July 2026 at 08:00
Liu et al. develop Oncoformer, a multimodal transformer that reads routine laboratory tests and chest X-rays already collected in everyday care. Across more than 3.6 million individuals, it detects cancer, infers tumor stage, and stratifies treatment response and recurrence risk, pointing toward risk-adapted cancer care built on data already in hand.
Received — 10 September 2026 ⏭

The DreAM-plus integrative RNA switch enhances transient AAV expression and reduces side effects of gene editing

✇Molecular Therapy
By: Yueyang Zhang · Yuhan Yang · Zhanzhao Liu · Yue Li · Yizhe Xue · Zihan Zhang · Gonglie Chen · Jin Liu · Mengying He · Tian Lu · Yaohua Zhang · Dongyu Zhao · Ke Yang · Lei Miao · Fei Gao · Yuxuan Guo
10 August 2026 at 08:00
This study developed a multi-layer inducible RNA switch that achieves transient expression of gene-delivery vectors in hepatic and non-hepatic tissues. As an exemplary application, this RNA switch triggers pulsive expression of gene editors that reduces the off-target effects and immunotoxicity of gene editing.

Repurposing base editors for targeted knockin and simultaneous multiplex knockouts to generate allo-CAR T cells with minimal translocations

✇Molecular Therapy
By: Viktor Glaser · Lily Jo Becker · Carla Fuster-García · Eric Jackson Aird · Luis Huth · Ana-Maria Nitulescu · Yaolin Pu · Isabell Kassing · Laura Marie Hartmann · Christian Luca Flugel · Roberts Kārkliņš · Shona Shaji · Marie Pouzolles · Maik Stein · Geoffroy Andrieux · Jacob Ellery Corn · Toni Cathomen · Hans-Dieter Volk · Petra Reinke · Jonas Kath · Dimitrios Laurin Wagner
20 July 2026 at 08:00
Wagner and colleagues develop BEKI (Base Editor-mediated Knock-In), a non-viral platform that combines targeted transgene insertion with simultaneous gene knockouts in a single step. BEKI-engineered CAR T cells show markedly reduced chromosomal rearrangements compared with conventional nuclease-based approaches, advancing safer manufacturing of multiplex-edited cell therapies for cancer and autoimmune diseases.
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Kernel-Managed Shared Memory for System-Wide Personalization Ryan Lum · Yongfeng Zhang
    arXiv:2609.10144v1 Announce Type: new Abstract: AI systems become more useful when they can adapt to the people using them, but in multi-agent systems, useful context learned by one agent often remains unavailable to others. We present kernel-managed shared memory, a system-level abstraction in which specialized agents write structured, tagged memories while the agent-system kernel, not individual agents, governs retrieval, privacy enforcement, and prompt injection. We implement and evaluate th
     
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Kernel-Managed Shared Memory for System-Wide Personalization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ryan Lum · Yongfeng Zhang
10 September 2026 at 12:00
arXiv:2609.10144v1 Announce Type: new Abstract: AI systems become more useful when they can adapt to the people using them, but in multi-agent systems, useful context learned by one agent often remains unavailable to others. We present kernel-managed shared memory, a system-level abstraction in which specialized agents write structured, tagged memories while the agent-system kernel, not individual agents, governs retrieval, privacy enforcement, and prompt injection. We implement and evaluate this design on AIOS and compare it against three alternatives across three assistant models (GPT-4o, Llama-3.1:8B, Qwen-2.5:7B) and 1,800 total trials. Against an unmanaged external memory backend (Mem0) using identical underlying storage, kernel-managed retrieval and injection improve personalization scores by 2.4-4.0 points on a 5-point scale (e.g., 1.05 to 4.69 profile usage on GPT-4o), with every comparison significant at p

KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Kun Feng · Ziwei Shan · Yuchen Fang · Yiyang Tan · Sihan Lu · Shuqi Gu · Xingyu Lu · Lintao Ma · Kan Ren
10 September 2026 at 12:00
arXiv:2605.30002v2 Announce Type: replace Abstract: Cross-domain multimodal time series forecasting is a challenging task, requiring models to integrate precise numerical comprehension, cross-domain semantic understanding, and effective multimodal fusion. Existing approaches either build Time Series Foundation Models (TSFMs) from scratch or leverage pretrained Large Language Models (LLMs). However, TSFMs often overlook semantic understanding and lack the ability to perform future-oriented semantic reasoning, and LLMs struggle with numerical comprehension and accurate quantitative forecasting. To overcome these limitations, we propose KairosAgent, a novel agentic framework for multimodal time series forecasting, including an LLM-based reasoner and a TSFM-based forecaster. KairosAgent unifies textual reasoning and numerical forecasting by dynamically invoking analytical tools to enhance the numerical understanding and semantic reasoning capabilities of LLMs. The reasoning results are subsequently fused into the TSFM pipeline, enabling more accurate and reliable future predictions. To further improve the reasoning, we curate a large-scale corpus of high-quality trajectories, alongside a reinforcement learning from forecasting paradigm with multi-turn refinement and turn-level credit assignment. Experiments demonstrate that KairosAgent achieves superior zero-shot forecasting performance while maximizing the utility of pretrained LLMs and TSFMs, presenting a promising direction for efficient and interpretable time series agents. The project page is at https://foundation-model-research.github.io/KairosAgent .

Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Lin Shi (Audrey) · Haowei Lin (Audrey) · Zixuan Zhu (Audrey) · Xiaoyue Zhou (Audrey) · Xiang Li (Audrey) · Xiangning Lin (Audrey) · Yaxuan Deng (Audrey) · Han Xu (Audrey) · Yuangang Li (Audrey) · Shanda Li (Audrey) · Zizhao Chen (Audrey) · Hanwen Xing (Audrey) · Harsh Raj (Audrey) · Bo Chen (Audrey) · Quan Shi (Audrey) · Steven Dillmann (Audrey) · Yipeng Gao (Audrey) · Puneesh Khanna (Audrey) · Ruofan Lu (Audrey) · Chao Beyond Zhou (Audrey) · Michael Yang (Audrey) · Robert Zhang (Audrey) · Siyuan Chai (Audrey) · Jiayu Chang (Audrey) · Yizhao Chen (Audrey) · Xiaokun Chen (Audrey) · Yiwei Dai (Audrey) · Wenting Yang (Audrey) · Hange Liu (Audrey) · Minghao Liu (Audrey) · Zihan Wang (Audrey) · Adnan El Assadi (Audrey) · Benedikt Stroebl (Audrey) · E. Kelly Buchanan (Audrey) · Han Meng (Audrey) · Junwei He (Audrey) · Longxuan Yu (Audrey) · Radin Shayanfar (Audrey) · Yukyung Lee (Audrey) · Zhikang Dong (Audrey) · Allen G Hart (Audrey) · Anjiang Wei (Audrey) · Anurag Kashyap (Audrey) · Arpandeep Khatua (Audrey) · Audrey Jixin Zheng (Audrey) · Chengrui Ma (Aud
10 September 2026 at 12:00
arXiv:2609.04298v2 Announce Type: replace Abstract: Evaluating agents on the growing number of agentic benchmarks is challenging because they often require complex environments and agent integrations. We introduce Harbor Adapters, a unified evaluation infrastructure for agentic benchmarks. Our work makes three contributions. First, we develop benchmark adapters that port more than 80 benchmarks to evaluate arbitrary agents, and validate them through rigorous code review and parity experiments. Second, we conduct a large-scale evaluation of 8 models spanning capability tiers across 54 benchmarks; every model is run with Terminus-2 and with one of 3 native harnesses. This enables a broader analysis of agent capabilities and failure modes than was previously possible. Third, we introduce Harbor-Index, a curated set of 82 difficult, diverse, and high-quality tasks spanning 29 benchmarks, refined from the adapted suite through difficulty filtering, AI and human audit, and an audit-and-fix loop. Harbor-Index preserves the challenge and breadth of large-scale agentic evaluations while being affordable to run; no evaluated model-harness configuration exceeds 30% pass rate, and the strongest (GPT-5.5 with Codex) reaches 28.0%. We release the adapters, evaluation results, in-depth analysis, and Harbor-Index as open-source artifacts to support more reliable and comprehensive evaluation of language-model agents.

Sexual dimorphism in the complete Drosophila male central nervous system connectome

✇Cell
By: Stuart Berg · Isabella R. Beckett · Marta Costa · Philipp Schlegel · Michał Januszewski · Elizabeth C. Marin · Aljoscha Nern · Stephan Preibisch · Wei Qiu · Shin-ya Takemura · Alexandra M.C. Fragniere · Andrew S. Champion · Diane-Yayra Adjavon · Michael Cook · Marina Gkantia · Kenneth J. Hayworth · Gary B. Huang · William T. Katz · Florian Kämpf · Zhiyuan Lu · Christopher Ordish · Tyler Paterson · Tomke Stürner · Eric T. Trautman · Catherine R. Whittle · Laura E. Burnett · Judith Hoeller · Feng Li · Frank Loesche · Billy J. Morris · Tobias Pietzsch · Markus W. Pleijzier · Valeria Silva · Yijie Yin · Iris Ali · Griffin Badalamente · Alexander Shakeel Bates · Rory J. Beresford · John Bogovic · Paul Brooks · Sebastian Cachero · Brandon S. Canino · Bhumpanya Chaisrisawatsuk · Jody Clements · Arthur Crowe · Inês de Haan Vicente · Georgia Dempsey · Erika Donà · Márcia Dos Santos · Marisa Dreher · Christopher R. Dunne · Katharina Eichler · Samantha Finley-May · Miriam A. Flynn · Imran Hameed · Gary Patrick Hopkins · Philip M. Hubbard · Ladann Kiassat · Julie Kovalya
3 September 2026 at 08:00
The Drosophila whole male central nervous system connectome enables end-to-end analysis of sensorimotor circuits. Comparison with existing female datasets shows that brain-wide wiring differences between the sexes are concentrated in higher centers.
Received — 27 May 2026 ⏭

SimuWoB: Simulating Real-World Mobile Apps for Fast and Faithful GUI Agent Benchmarking

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guohong Liu · Jialei Ye · Pengzhi Gao · Wei Liu · Jian Luan · Yunxin Liu · Yuanchun Li
26 May 2026 at 12:00
arXiv:2605.25160v1 Announce Type: new Abstract: Mobile GUI agents powered by large language models have progressed rapidly, creating urgent needs for realistic and comprehensive evaluation. Existing benchmarks prioritize reproducibility but are often limited to open-source apps or file-operation tasks for the difficulty of constructing rewards on real applications, leaving a gap between benchmark settings and real-world usage. Moreover, most benchmarks focus on basic grounding and navigation, with limited coverage of complex, long-horizon interactions. To address these limitations, we introduce SimuWoB, a fully synthetic benchmark for mobile GUI agents with 120 challenging tasks spanning diverse types and difficulty levels. We build a robust virtual environment generation framework that synthesizes high-fidelity tasks and environments, and automatically provides valid rewards for each task. Each environment is deployed as a backend-free webpage accessible via URL, enabling efficient and reproducible evaluation. We conduct comprehensive experiments on several state-of-the-art mobile GUI agents. The average success rate is only 27.92%, dropping to 17.82% on long-horizon tasks, which reveals substantial weaknesses in current agents under complex scenarios. Evaluation result comparison with real-world sample tasks demonstrate that agent assessments based on our synthetic environment generalize well. We further provide diagnostic insights across key capability dimensions and discuss implications for future mobile GUI agent development.

LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design

✇cs.AI, q-bio.NC updates on arXiv.org
By: Leshu Li · An Lu · Haiyu Wang · Zhibin Feng · Conghui Duan · Qing Bao · Zongmin Zhao · Sai Qian Zhang
26 May 2026 at 12:00
arXiv:2605.25250v1 Announce Type: new Abstract: Lipid nanoparticles (LNPs) are among the most clinically mature platforms for nucleic acid delivery, yet designing lipids that are both effective and biologically safe remains a major bottleneck. In practical screening, toxicity is a decision-level constraint: if a lipid is toxic, its efficiency prediction is clinically irrelevant. We propose LipoAgent, a safety-aware multi-agent LLM framework for lipid discovery. LipoAgent combines domain-specific finetuning with a conditional prediction objective that enforces toxicity as a prerequisite for efficiency prediction, and further improves reliability via multi-agent verification with lightweight human oversight when disagreement persists. Across multiple foundation models, LipoAgent achieves an average 32% relative improvement in mRNA transfection efficiency prediction compared with other reported models for lipid design. Wet-lab validation confirms that virtual screening rankings reliably translate to biological transfection outcomes. The code is publicly available at https://github.com/SAI-Lab-NYU/LipoAgent.git.

Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junyi Wu · Weijian Luo · Haoyang Zheng · Ruizhe Zhang · Guang Lin
26 May 2026 at 12:00
arXiv:2605.24001v2 Announce Type: cross Abstract: Recent advances in one-step text-to-image generation have enabled real-time synthesis with remarkable efficiency and quality. Previous reinforcement learning methods for one-step generators combine image-space reward optimization with diffusion noisy-space distribution matching. This paradigm brings challenges due to a mismatch between terminal reward optimization and the underlying generative dynamics. As a result, optimization tends to exploit stochastic degrees of freedom, often improving reward at the expense of image fidelity. To address this issue, we propose Diff-Instruct with Diffused Reward (DIDR), a data-free trajectory-level alignment framework derived from Integral KL minimization. DIDR propagates the RLHF-optimal reward-tilted clean-image distribution across all noise levels along the diffusion trajectory. We show that this objective admits the same minimizer as clean-image RLHF, while naturally inducing the Diffused Reward Score (DRS), which acts as a reward-driven correction to the reference score function. To make this practical, we further introduce the Diffused Reward Proxy (DRP), an efficient estimator of DRS based on differentiable short-step denoising. Extensive experiments demonstrate that DIDR consistently Pareto-dominates existing one-step SDXL baselines. Moreover, when transferred to a 6B DiT backbone (Z-Image), DIDR surpasses its 50-step teacher in preference alignment while requiring only a single generation step.

VEN-VL: A Visual Ensemble MoE Framework for Effective and Efficient Multi-Modal Understanding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yinghao Wu · Zhuoyan Luo · Yiyao Yu · Zhaojian Yu · Yujiu Yang · Xiao-Ping Zhang
26 May 2026 at 12:00
arXiv:2605.25952v1 Announce Type: cross Abstract: Despite the remarkable progress achieved by recent efficient methods in accelerating multimodal understanding, they still suffer from noticeable performance degradation. Their emphasis on the high compression ratio of a single visual clue and reliance on the heuristic pruning strategy with coarse attention alignment incurs a bottleneck on the information capacity and density of visual tokens. Addressing this limitation, we propose VEN-VL, a visual ensemble MoE framework for effective and efficient perception following the enrich then compact principle. Specifically, we first enrich the information capacity by unifying the visual representations of different perspectives, and then progressively compact it with adaptive routers in specialized visual experts to enhance the information density. Furthermore, we incorporate the reconstruction ability of vanilla structure via explicit visual supervision, facilitating crucial information preservation. Experimental results demonstrate our superiority in complex visual tasks with few information-condensed tokens, which effectively bridges the gap between performance and efficiency.

A framework for building a synthetic cell from the SynCell Asia Initiative

✇Nature Biotechnology - Issue - nature.com science feeds
By: Zhuojun Dai · Wataru Aoki · Pimchai Chaiyen · Matthew Wook Chang · Xixian Chen · Zibo Chen · Byung-Kwan Cho · Nan-Nan Deng · Shishen Du · Jee Loon Foo · Julius Fredens · Meifang Fu · Xiaojun Han · Tomohisa Hasunuma · Norikazu Ichihashi · Nurulfiza Mat Isa · Daisuke Kiga · Dong-Myung Kim · Jongmin Kim · Akihiko Kondo · Yuishin Kosaka · Kobkul Laoteng · Joongoo Lee · Jian Li · Junbai Li · Ming Li · Sierin Lim · Jianbo Liu · Xingguo Liu · Yuan Lu · Nan Luo · Tomoaki Matsuura · Wataru Mizunashi · Yan Qiao · Ahmad Bazli Ramzi · Sang Woo Seo · Yoshihiro Shimizu · Kwanwoo Shin · Liangfei Tian · Wanwipa Vongsangnak · Baojun Wang · Cheng-Cai Zhang · Xian-En Zhang · Guoping Zhao · Bo Zheng · Yaoqi Zhou · Chenli Liu
26 May 2026 at 08:00

Nature Biotechnology, Published online: 26 May 2026; doi:10.1038/s41587-026-03153-w

Building a living cell from scratch requires overcoming a bottleneck that has remained unresolved despite decades of progress: orchestrating the spatiotemporal integration of core functional modules. To tackle this barrier, the SynCell Asia Initiative outlines a strategy for developing core functional modules followed by their systems-level integration through the establishment of a centralized, artificial intelligence (AI)-driven biofoundry.

Liver-specific <i>SIRT1</i> knockout-induced hyperglycemia promotes spontaneous lung adenocarcinomas through HSF1-MDM2

✇Oncogene - Issue - nature.com science feeds
By: Yiwei Cao · Jingyi Wei · Qiang Chen · Haitao Wang · Jiao Mu · Shuhan Lu · Menghui Yuan · Mohammed A. El-Magd · Bichan Xu · Ruihong Wang · Peng Yuan
24 May 2026 at 08:00

Oncogene, Published online: 24 May 2026; doi:10.1038/s41388-026-03826-5

Liver-specific SIRT1 knockout-induced hyperglycemia promotes spontaneous lung adenocarcinomas through HSF1-MDM2

Integrating clinical and multiomics evidence based on disease module theory: deciphering the comorbidity network of psoriasis vulgaris via the Ising model for mechanistic insights

✇Pulmonary nodule
By: Dongwen Shi · Yuan Luo · Ping Zhang · Bin Liang · Mengmeng Wang · Hong Cai · Xiaowen Pang
30 April 2026 at 18:00

Front Immunol. 2026 Apr 14;17:1744789. doi: 10.3389/fimmu.2026.1744789. eCollection 2026.

ABSTRACT

Psoriasis vulgaris (PV), a chronic immune-mediated inflammatory dermatosis, is associated with a significant burden of systemic comorbidities. Traditional comorbidity research methods struggle to reveal its complex interconnectedness. Based on large-scale retrospective cohort data, we constructed a PV comorbidity network using the Ising model from statistical physics. Weighted network centrality analysis was used to identify core and hub nodes and elucidate shared molecular mechanisms at the multiomics level (nontargeted proteomics and lipid peroxidation metabolomics). Finally, the impact of IL-17A inhibition (IL-17Ai) on PV and atherosclerosis (assessed by carotid Doppler color ultrasound) was evaluated using a prospective intervention study. The Ising model identified atherosclerosis- coronary heart disease (CHD) as the core comorbidity (degree centrality >10), with pulmonary nodules, hypertension, and fatty liver serving as key hub nodes (betweenness centrality >60). Multiomics analysis revealed a core molecular mechanism in PV, involving immune inflammation, oxidative stress, lipid metabolism disorder, and coagulation abnormalities, where the oxidative stress molecule GPX3 acts as a critical hub. Following IL-17Ai intervention, both skin lesions and early atherosclerosis markers significantly improved, accompanied by downregulation of the proinflammatory peripheral blood factor S100A9 and upregulation of anti-inflammatory lipid peroxidation metabolites (e.g., 17(R)-RVD1). This study systematically revealed the modular hierarchical structure of PV comorbidities at the network topology and molecular mechanism levels, confirming the central role of the IL-17 signaling pathway in driving the comorbidity network. This conclusion was further clinically validated by IL-17Ai intervention outcomes. This research provides theoretical and clinical evidence for early identification, prioritized management, and "one drug, multiple targets" therapeutic strategies for treating PV comorbidities.

PMID:42058202 | PMC:PMC13121148 | DOI:10.3389/fimmu.2026.1744789

EBV strain interacts with host HLA to drive nasopharyngeal carcinoma risk

✇Nature - Issue - nature.com science feeds
By: Yanhong Chen · Jingtong Liang · Wanlin Zhang · Xinyu Zhang · Xinyi Zhang · Ching-Yuan Wang · Fei Yao · Shanshan Zhang · Xiang Zhou · Weimin Ye · Ruimei Feng · Yonglin Cai · Zhe Zhang · Mingfang Ji · Qian Cui · Xihong Lin · Jiesen Li · Jialei Xu · Qiuting Zhang · Qinyao Huang · Yingying Cheng · Yanran Luo · Xiaoping Ye · Qisheng Feng · Minzhong Tang · Mu-Sheng Zeng · Yi-Xin Zeng · Zhonghua Liu · Weiwei Zhai · Jianjun Liu · Miao Xu
15 April 2026 at 08:00

Nature, Published online: 15 April 2026; doi:10.1038/s41586-026-10416-8

A genome-to-genome association study identifies host and viral risk factors that interact to drive nasopharyngeal carcinoma endemicity in southern China.
Received — 13 April 2026 ⏭
Received — 8 April 2026 ⏭

3D-IDE: 3D Implicit Depth Emergent

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chushan Zhang · Ruihan Lu · Jinguang Tong · Yikai Wang · Hongdong Li
7 April 2026 at 12:00
arXiv:2604.03296v1 Announce Type: cross Abstract: Leveraging 3D information within Multimodal Large Language Models (MLLMs) has recently shown significant advantages for indoor scene understanding. However, existing methods, including those using explicit ground-truth 3D positional encoding and those grafting external 3D foundation models for implicit geometry, struggle with the trade-off in 2D-3D representation fusion, leading to suboptimal deployment. To this end, we propose 3D-Implicit Depth Emergence, a method that reframes 3D perception as an emergent property derived from geometric self-supervision rather than explicit encoding. Our core insight is the Implicit Geometric Emergence Principle: by strategically leveraging privileged geometric supervision through mechanisms like a fine-grained geometry validator and global representation constraints, we construct an information bottleneck. This bottleneck forces the model to maximize the mutual information between visual features and 3D structures, allowing 3D awareness to emerge naturally within a unified visual representation. Unlike existing approaches, our method enables 3D perception to emerge implicitly, disentangling features in dense regions and, crucially, eliminating depth and pose dependencies during inference with zero latency overhead. This paradigm shift from external grafting to implicit emergence represents a fundamental rethinking of 3D knowledge integration in visual-language models. Extensive experiments demonstrate that our method surpasses SOTA on multiple 3D scene understanding benchmarks. Our approach achieves a 55% reduction in inference latency while maintaining strong performance across diverse downstream tasks, underscoring the effectiveness of meticulously designed auxiliary objectives for dependency-free 3D understanding. Source code can be found at github.com/ChushanZhang/3D-IDE.
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