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Received today — 7 October 2026 ⏭

KDM4A drives TGCT metastasis by inducing focal adhesion disassembly via STAT1-mediated <i>CCL3</i> transcriptional activation

✇Oncogene - Issue - nature.com science feeds
By: Xiang Liu · Zixin Wang · Xinchang Zou · Tao Chen · Song Xiao · Songhui Xu · Xiang Sun · Tao Zeng
4 October 2026 at 08:00

Oncogene, Published online: 04 October 2026; doi:10.1038/s41388-026-04002-5

KDM4A drives TGCT metastasis by inducing focal adhesion disassembly via STAT1-mediated CCL3 transcriptional activation
Received — 10 September 2026 ⏭

Hyperbolic Geometry for Open-World Object Detection in Remote Sensing Imagery

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wuzhou Li · Jiawei Zhou · Shenghang Wang · Xiang Li
10 September 2026 at 12:00
arXiv:2609.09626v1 Announce Type: cross Abstract: Open-world object detection (OWOD) extends closed-set detection by requiring models to identify unknown objects and incrementally learn them once annotations become available. In remote sensing imagery, object categories often exhibit latent hierarchical relationships that may be inadequately represented in the Euclidean spaces commonly adopted by existing methods, limiting unknown-object recall and incremental-learning performance. To address this issue, we investigate hyperbolic geometry for OWOD in remote sensing imagery and propose HyRS-OWOD. To improve unknown object recall, we design a two-step unknown-object discovery mechanism: a Decoupled Objectness Learning (DOL) module that disentangles foreground perception from semantic information to separate foreground proposals from background regions, followed by a Hyperbolic Uncertainty Learning (HUL) component that leverages the radius of hyperbolic embeddings as an uncertainty-aware cue for known-unknown discrimination. For incremental learning, we develop a Hyperbolic Metric Learning (HML) strategy that enhances inter-class separability, facilitating the incorporation of novel categories while mitigating catastrophic forgetting. Experiments on three remote sensing benchmarks demonstrate consistent improvements in unknown recall and incremental learning over state-of-the-art OWOD methods.

EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jie Mao · Changlun Li · Xiang Li · Qiqi Duan · Jinhui Yuan · Xiang Liu · Yuyu Luo · Jing Tang · Xiaowen Chu · Nan Tang
10 September 2026 at 12:00
arXiv:2607.12455v2 Announce Type: replace Abstract: Quantitative strategy optimization remains largely manual, requiring domain experts to identify weak signals, tune risk-control rules, and repeatedly validate iterative revisions. Large language models can accelerate this process, but directly relying on them to rewrite trading strategies often introduces hallucinated edits, strategy drift, and backtest overfitting. We propose EVOQUANT, a self-Evolving Verifier-guided framework for strategy Optimization in Quantitative trading. Our method utilizes LLMs to deeply diagnose performance bottlenecks, generates semantically controlled candidate edits, selects the best strategy through a multi-stage verification pipeline, and distills optimization experience into reusable knowledge for continual self-improvement. We evaluate our method using seven representative strategies: four from the A-share market and three from the Crypto market. Experimental results show that our method significantly improves the Sharpe ratio across all tested strategies: the average test Sharpe increases from -0.298 to 0.538, and the best-performing strategy achieves a 199% relative improvement. Ablation studies and stress tests under stricter conditions further validate the effectiveness and robustness of the framework. Overall, this work transforms quantitative strategy optimization from costly manual trial and error into an automated and verifiable iterative paradigm, offering a new path for applying large language models to financial strategy research.

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.

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.

Instance-Aware Algorithm Selection for Maximum Clique via a Dual-Channel Graph Neural Architecture

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiang Li · Shanshan Wang · Chenglong Xiao
10 September 2026 at 12:00
arXiv:2508.08005v5 Announce Type: replace-cross Abstract: Although the Maximum Clique Problem (MCP) has been extensively studied and features a rich ecosystem of exact solvers, empirical evidence shows that solver performance varies substantially across graph families. Consequently, selecting an appropriate algorithm for a given instance remains an open and practically important challenge that has received little systematic attention. We address this gap by developing an instance-aware selection framework that systematically combines global statistical descriptors with learned topological representations. We construct a comprehensive benchmark by evaluating four state-of-the-art exact solvers on a diverse collection of graph instances and deriving both global statistical and local structural features. An evaluation of conventional classifiers establishes Random Forest as a strong baseline and reveals that connectivity and topological features are key predictors of performance. Motivated by these observations, we introduce a dual-channel architecture that jointly leverages a Graph Attention Network for capturing local neighborhood patterns and a Multilayer Perceptron for modeling global statistical features. Extensive experiments show that the proposed dual-channel model consistently surpasses classical baselines and the single-best solver, achieving 90.43% test accuracy. These findings demonstrate the value of integrating local topological encoding with global statistical cues for combinatorial algorithm selection. Code and models are available at: https://anonymous.4open.science/r/GAT-MLP-7E5F.

Author Correction: A mouse brain stereotaxic topographic atlas with isotropic 1-μm resolution

✇Nature - Issue - nature.com science feeds
By: Zhao Feng · Xiangning Li · Yue Luo · Xin Liu · Ben Long · Tao Jiang · Xueyan Jia · Xiaowei Chen · Jie Luo · Xiaokang Chai · Zhen Wang · Miao Ren · Xin Lu · Gang Yao · Mengting Zhao · Yuxin Li · Zhixiang Liu · Hong Ni · Chuhao Dou · Shengda Bao · Shicheng Yang · Zoutao Zhang · Jiandong Zhou · Lingyi Cai · Qi Zhang · Ayizuohere Tudi · Chaozhen Tan · Zhengchao Xu · Siqi Chen · Wenxiang Ding · Wenjuan Shi · Anan Li · Hong-wei Dong · Hui Gong · Qingming Luo
7 September 2026 at 08:00

Nature, Published online: 07 September 2026; doi:10.1038/s41586-026-11094-2

Author Correction: A mouse brain stereotaxic topographic atlas with isotropic 1-μm resolution

Genomics and social practices at Mogou and other Gansu sites during prehistoric trans-Eurasian exchange

✇Cell
By: Han Shi · E. Andrew Bennett · Guoke Chen · Hui Wang · Tianyi Wang · Peng Cao · Ruilin Mao · Fan Bai · Qingyan Dai · Feng Liu · Xiaotian Feng · Yongan Wang · Chong Yao · Jing Wu · Wenjun Wang · Ming Zhang · Tianxiang Liu · Jingkun Ran · Yue-Chen Liu · Yuan Liu · Dan Ju · Chuyan Li · Hongwei Hou · Xinyi Liu · Qiaomei Fu
3 September 2026 at 08:00
Ancient DNA from 149 individuals at 11 sites in Gansu, China, dated to around 4,700–3,000 years ago, reveals human population history during early transcontinental exchanges of agriculture and technology, as well as contemporary social practices, at the large Mogou cemetery.

SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types

✇Nature Cancer
By: Ying Chen · Chenglong Ma · Qiongqiong Li · Fang Yan · Yirong Chen · Tianbin Li · Jin Ye · Ming Hu · Yuxiang Lin · Yanjun Li · Guoan Wang · Huihui Xu · Hui Dong · Xiang Wang · Xiaoxiao Xu · Yanyan Zhou · Xia Zhu · Sen Yang · Xiyue Wang · Lu Zhang · Yu Qiao · Rongshan Yu · Junjun He · Yuanfeng Ji
1 September 2026 at 08:00

Nature Cancer, Published online: 01 September 2026; doi:10.1038/s43018-026-01220-4

Chen et al. have developed SlideChat, a multimodal generative artificial intelligence assistant, which they benchmark on 27 pathology tasks across 33 cancer types. Expert pathologists rated the assistant as clinically relevant and accurate.
Received — 27 May 2026 ⏭

GlobalDentBench: A Multinational Benchmark for Evaluating LLM Clinical Reasoning in Dentistry with Expert Calibration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junjie Zhao · Jingyi Liang · Zhenyang Cai · Jiaming Zhang · Zhenwei Wen · Shuzhi Deng · Wenjing Yi · Chunfeng Luo · Hexian Zhang · Junying Chen · Tianrui Liu · Zhuhui Bai · Zixu Zhang · Pradeep Singh · Xiang Liu · Jianquan Li · Nhan L Tran · Falk Schwendicke · Zuolin Jin · Lijian Jin · Liangyi Chen · Wei-fa Yang · Benyou Wang · Junwen Wang · Shan Jiang
26 May 2026 at 12:00
arXiv:2605.24636v2 Announce Type: new Abstract: While large language models (LLMs) hold transformative potential for medicine, their reasoning robustness and safety in real-world clinical scenarios remain critically underexplored, particularly in dentistry. Here we introduce GlobalDentBench, the first multinational dental benchmark, featuring a taxonomy that encompasses 14 dental specialties across 88 countries and regions spanning six continents. The benchmark comprises 8,978 expert-validated questions across three formats (multiple-choice, short-answer, and case-based questions) and assesses three progressive reasoning levels: knowledge recall (L1), routine reasoning (L2), and individualized reasoning (L3). To ensure data quality, the automated construction framework was calibrated by six senior dentists, achieving expert agreement rates of 99.98% for multiple-choice and short-answer questions and 96.78% for the more complex case-based questions. Evaluation of 12 frontier LLMs on GlobalDentBench revealed a sharp, stepwise performance degradation with increasing reasoning complexity. Specifically, accuracy plummeted from 81.34% on multiple-choice to 64.53% on short-answer and 22.34% on case-based questions, while declining markedly from 74.01% at L1 to 55.64% at L2 and 35.71% at L3. More critically, risk analysis of real-world dental cases demonstrated an alarming overall unsafe rate of 31.01% in LLM-generated clinical recommendations, with 4.51% posing risks of irreversible patient harm and risks particularly pronounced in specialties such as orthodontics. These findings expose fundamental limitations in the medical reasoning and safety of current LLMs. Consequently, GlobalDentBench provides a scalable foundation for trustworthy clinical AI evaluation, underscoring the urgent need for rigorous validation before the safe deployment of these models in healthcare.

Unlocking the Future of Hepatocellular Carcinoma Early Diagnosis: The Promise of Extracellular Vesicle Biomarkers

✇Omics in Hepatocellular
By: Kunxiang Li · Zhihua Zuo · Xinyi Ou · Miyuan Yang · Yirui Qin · Bing Zhang · Yongcan Guo
25 May 2026 at 18:00

J Clin Transl Hepatol. 2026 Apr 28;14(4):462-477. doi: 10.14218/JCTH.2025.00589. Epub 2026 Apr 8.

ABSTRACT

Hepatocellular carcinoma (HCC) is one of the most prevalent and aggressive malignant tumors globally, with a notably low five-year survival rate. Its high mortality is largely attributed to challenges in early detection. Extracellular vesicles (EVs) are naturally occurring nanoparticles secreted by nearly all cell types and carry a diverse array of bioactive molecules, including proteins, nucleic acids (particularly non-coding RNAs), and lipids. EVs play pivotal roles in remodeling the tumor microenvironment and driving cancer progression through intercellular communication. Accumulating evidence has established that EVs are critically involved in the pathogenesis of HCC and are emerging as promising biomarkers for its early detection. With advances in EV isolation technologies, these vesicles have garnered considerable attention in the field of liquid biopsy for HCC. This review provides a comprehensive overview of the diagnostic potential of EV-derived biomarkers in HCC, including DNA, RNA, proteins, and lipids. Additionally, it discusses the advantages of integrating multi-omics approaches for HCC diagnosis. Furthermore, the review highlights the technical challenges in EV isolation and characterization, as well as the crucial role of reference genes in the standardization of EV data. These insights underscore the potential of EVs as novel, minimally invasive liquid biopsy biomarkers for the early diagnosis of HCC.

PMID:42181837 | PMC:PMC13195390 | DOI:10.14218/JCTH.2025.00589

ATP2B4 driven chromatin compaction exacerbates pancreatic cancer radiotherapy resistance

✇Cell Death Discovery nature.com science feeds
By: Yuyu Luo · Wei Jiang · Yanfang Liu · Qinghua Li · Yuanfei Chen · Shiwan Lin · Hsiang-i Tsai · Lirong Zhang · Dongqing Wang · Xiang Liao · Haitao Zhu
25 May 2026 at 08:00

Cell Death Discovery, Published online: 25 May 2026; doi:10.1038/s41420-026-03142-7

ATP2B4 driven chromatin compaction exacerbates pancreatic cancer radiotherapy resistance

Advancing solar and wind penetration in China through energy complementarity

✇Nature - Issue - nature.com science feeds
By: Yuan Hu · Hou Jiang · Chuan Zhang · Jianlong Yuan · Mengting Zhang · Ling Yao · Qiang Chen · Jichao Wu · Hualong Zhang · Subin Ma · Xiang Li · Weiyu Zhang · Quanhua Dong · Congcong Wen · Gege Yin · Fan Zhang · Chaohui Yu · Zhijun Jin · Yu Liu
20 May 2026 at 08:00

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10570-z

Using high-resolution satellite imagery combined with a deep-learning-based framework to build a national energy inventory enables a data-driven assessment of solar–wind complementarity strategies to reduce power variability and enhance renewable energy penetration across China.

CD300ld on pathologically activated neutrophils promotes tumor immune suppression by binding phosphatidylserine on CD8<sup>+</sup> T cells

✇Nature Cancer
By: Chaoxiong Wang · Peixuan Zheng · Anhui Wang · Xinrui Fan · Tianxiang Li · Jingjing Hong · Runze Wang · Chunyan Zhang · Chaogui Luo · Weiwei Yang · Dong Gao · Adili Salai · Min Luo · Zhigang Lu · Dianfan Li · Moubin Lin · Jiayin Peng · Guohui Li · Shuo Han · Yun Zhao
15 May 2026 at 08:00

Nature Cancer, Published online: 15 May 2026; doi:10.1038/s43018-026-01169-4

Zhao and colleagues show that CD300ld, upregulated in pathologically activated neutrophils, mediates contact-dependent suppression of cytotoxic CD8+ T cells by binding to phosphatidylserine, inhibiting antitumor immune responses.

Linear RAG scanning mediates editing of Igκ variable region repertoires

✇Nature - Issue - nature.com science feeds
By: Xiang Li · Hongli Hu · Yiwen Zhang · Tammie Zhu · Ying Guan · Kai Xu · Xin Lin · Camille Hebert · Himanshu Batra · Jenny Zhou · Zhaoqing Ba · Duane R. Wesemann · Adam Yongxin Ye · Frederick W. Alt
15 April 2026 at 08:00

Nature, Published online: 15 April 2026; doi:10.1038/s41586-026-10362-5

Studies explaining the secondary Igk recombination mechanism are described and Cer/Sis deletion and/or displacement is implicated as a developmental switch converting the rearrangement mechanisms from two-loop-based diffusional primary Igk into one-loop-based linear scanning secondary mechanisms.
Received — 13 April 2026 ⏭

Chuanminshen violaceum (Apiaceae) as a medicinal-and-edible resource: phytochemical diversity, bioactivities, and routes to standardized products

✇Omics In Lung
By: Bin Zhu · Yu-Jia Zhou · Xiang Li · Zhong-Qi Shi · An-Guo Wu · Lu Yu · Jian-Feng Ye · Jian-Ming Wu · Fei-Hong Huang · Xiao-Gang Zhou
8 April 2026 at 18:00

J Ethnopharmacol. 2026 Apr 6:121628. doi: 10.1016/j.jep.2026.121628. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Chuanminshen violaceum Sheh et Shan is a medicinal-and-edible Apiaceae plant in China recorded for yin nourishment, lung/spleen tonification, and phlegm resolution, and used for cough and chronic respiratory complaints.

STUDY AIM: To synthesize current evidence on botanical resources, chemistry, pharmacology, and applications of C. violaceum, and to define priorities for standardized and safe development.

MATERIALS AND METHODS: This review integrates studies on resource distribution and ecological adaptability, multi-fraction phytochemistry, extraction-purification and formulation technologies, preclinical pharmacology, and quality, safety, and regulatory considerations.

RESULTS: C. violaceum contains structurally diverse polysaccharides plus volatile oils (often polyacetylene-rich), phenolics (e.g., chlorogenic acid and rutin), PUFA-rich lipids, and newly reported minor constituents. Polysaccharides show variable monosaccharide profiles, molecular-weight ranges, and linkage/branching patterns, strongly influenced by extraction-purification; derivatization (e.g., sulfation/selenization) and delivery systems can further tune physicochemical properties. Preclinical studies report antioxidant, anti-inflammatory, immunomodulatory, cardioprotective, and antiviral effects, commonly linked to Nrf2/Keap1 redox defense, inflammatory signaling control, TLR2/4-related immune regulation, gut-barrier reinforcement with microbiota remodeling, and anti-ferroptotic protection in myocardial ischemia-reperfusion models. Applications span traditional dosage forms and functional foods, but translation is limited by origin/process variability, incomplete long-term safety and ADME data, and regulatory uncertainty.

CONCLUSIONS: C. violaceum is a promising ethnomedicinal resource with clear part-specific features and polysaccharide-centered potential. Future work should combine multi-omics with target validation, fingerprint-guided QC and traceability, greener scalable processing, and regulatory-aligned safety packages to enable reproducible products.

PMID:41951195 | DOI:10.1016/j.jep.2026.121628

Received — 8 April 2026 ⏭

Discrete Prototypical Memories for Federated Time Series Foundation Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Liwei Deng · Qingxiang Liu · Xinhe Niu · Shengchao Chen · Sheng Sun · Yuankai Wu · Guodong Long · Yuxuan Liang
7 April 2026 at 12:00
arXiv:2604.04475v1 Announce Type: cross Abstract: Leveraging Large Language Models (LLMs) as federated learning (FL)-based time series foundation models offers a promising way to transfer the generalization capabilities of LLMs to time series data while preserving access to private data. However, the semantic misalignment between time-series data and the text-centric latent space of existing LLMs often leads to degraded performance. Meanwhile, the parameter-sharing mechanism in existing FL methods model heterogeneous cross-domain time-series data into a unified continuous latent space, which contradicts the fact that time-series semantics frequently manifest as discrete and recurring regimes. To address these limitations, we propose \textsc{FeDPM}, a federated framework for time-series foundation models based on discrete prototypical memories. Specifically, we learn local prototypical memory priors for intra-domain time-series data. We then align cross-domain memories to promote a unified discrete latent space and introduce a domain-specific memory update mechanism to balance shared and personalized prototypical knowledge. Extensive experiments demonstrate the efficiency and effectiveness of \textsc{FeDPM}. The code is publicly available at https://anonymous.4open.science/r/FedUnit-64D1.
Received — 4 April 2026 ⏭

Improving Ensemble Forecasts of Abnormally Deflecting Tropical Cyclones with Fused Atmosphere-Ocean-Terrain Data

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qixiang Li · Yuan Zhou · Shuwei Huo · Chong Wang · Xiaofeng Li
3 April 2026 at 12:00
arXiv:2603.29200v2 Announce Type: replace-cross Abstract: Deep learning-based tropical cyclone (TC) forecasting methods have demonstrated significant potential and application advantages, as they feature much lower computational cost and faster operation speed than numerical weather prediction models. However, existing deep learning methods still have key limitations: they can only process a single type of sequential trajectory data or homogeneous meteorological variables, and fail to achieve accurate forecasting of abnormal deflected TCs. To address these challenges, we present two groundbreaking contributions. First, we have constructed a multimodal and multi-source dataset named AOT-TCs for TC forecasting in the Northwest Pacific basin. As the first dataset of its kind, it innovatively integrates heterogeneous variables from the atmosphere, ocean, and land, thus obtaining a comprehensive and information-rich meteorological dataset. Second, based on the AOT-TCs dataset, we propose a forecasting model that can handle both normal and abnormally deflected TCs. This is the first TC forecasting model to adopt an explicit atmosphere-ocean-terrain coupling architecture, enabling it to effectively capture complex interactions across physical domains. Extensive experiments on all TC cases in the Northwest Pacific from 2017 to 2024 show that our model achieves state-of-the-art performance in TC forecasting: it not only significantly improves the forecasting accuracy of normal TCs but also breaks through the technical bottleneck in forecasting abnormally deflected TCs.
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