Princeton Digital Group, a data center operator in Asia backed by Warburg Pincus, plans to invest $700 million in its first South Korean campus to power artificial intelligence development in the country.

تخطط مجموعة برينستون الرقمية، وهي مشغل لمراكز البيانات في آسيا مدعومة من واربورغ بينكوس، لاستثمار 700 مليون دولار في حرمها الجامعي الأول في كوريا الجنوبية. يهدف هذا الاستثمار إلى تعزيز تطوير الذكاء الاصطناعي في البلاد، مما يمثل خطوة مهمة للشركة في السوق الآسيوية.

Princeton Digital Group, un operador de centros de datos en Asia respaldado por Warburg Pincus, planea invertir 700 millones de dólares en su primer campus en Corea del Sur. Esta inversión tiene como objetivo potenciar el desarrollo de la inteligencia artificial en el país, marcando un paso significativo para la empresa en el mercado asiático.

Princeton Digital Group, un opérateur de centres de données en Asie soutenu par Warburg Pincus, prévoit d'investir 700 millions de dollars dans son premier campus en Corée du Sud. Cet investissement vise à renforcer le développement de l'intelligence artificielle dans le pays, marquant une étape significative pour l'entreprise sur le marché asiatique.

Princeton Digital Group, a data center operator in Asia supported by Warburg Pincus, is set to invest $700 million in its inaugural campus in South Korea. This investment aims to enhance the development of artificial intelligence in the country, marking a significant step for the company in the Asian market.

Warburg-Backed Princeton Enters Korea With First Data Center

arXiv:1906.07172v5 Announce Type: replace-cross 
Abstract: Equivariant neural networks are a class of neural networks designed to preserve symmetries inherent in the data. In this paper, we introduce a general method for modifying a neural network to enforce equivariance, a process we refer to as equivarification. We further show that group convolutional neural networks (G-CNNs) arise as a special case of our framework.

Equivariant neural networks and equivarification

arXiv:2311.13745v4 Announce Type: replace-cross 
Abstract: Diffusion models have become the most popular approach to deep generative modeling of images, largely due to their empirical performance and reliability. From a theoretical standpoint, a number of recent works have studied the iteration complexity of sampling, assuming access to an accurate diffusion model. In this work, we focus on understanding the sample complexity of training such a model; how many samples are needed to learn an accurate diffusion model using a sufficiently expressive neural network? Prior work showed bounds polynomial in the dimension, desired Total Variation error, and Wasserstein error. We show an exponential improvement in the dependence on Wasserstein error and depth, along with improved dependencies on other relevant parameters.

Improved Sample Complexity Bounds for Diffusion Model Training

arXiv:2410.22995v2 Announce Type: replace-cross 
Abstract: A hallmark of advanced artificial intelligence is the capacity to progress from passive visual perception to the strategic modification of visual information to facilitate complex reasoning. This advanced capability, however, remains critically underdeveloped in current Large Multi-modal Models (LMMs). The deficiency is often masked by evaluation metrics that prioritize final-answer accuracy, creating an illusion of competence where genuine reasoning is absent. Using the domain of geometric problem-solving as a precise instrument, we probe this issue through tasks that require constructing visual aids. To this end, we introduce \textbf{VisAidMath}, a challenging benchmark, and our novel Three-Layered Funnel Evaluation Framework. This framework moves beyond simple accuracy (ACCU) to scrutinize the generation of valid visual aids (PVA) and the soundness of subsequent reasoning steps (SPRS). Our extensive experiments on state-of-the-art models, including Doubao-Seed-1.6 and o4, reveal a profound ``Reasoning Illusion''. We observe that high surface-level accuracy conceals a catastrophic failure in the models' ability to produce valid visual aids or to reason from them. Our findings expose a fundamental schism between visual perception and logical deduction in modern LMMs. We host an evaluation platform at CodaBench for testing publicly. Homepage: https://nlp2ct.github.io/VisAidMathHomepage/ Evaluation: https://www.codabench.org/competitions/7634/

VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning

arXiv:2508.01249v3 Announce Type: replace-cross 
Abstract: Large Language Model (LLM) agents offer a powerful new paradigm for solving various problems by combining natural language reasoning with the execution of external tools. However, their dynamic and non-transparent behavior introduces critical security risks, particularly in the presence of prompt injection attacks. In this work, we propose a novel insight that treats the agent runtime traces as structured programs with analyzable semantics. Thus, we present AgentArmor, a program analysis framework that converts agent traces into graph intermediate representation-based structured program dependency representations (e.g., CFG, DFG, and PDG) and enforces security policies via a type system. AgentArmor consists of three key components: (1) a graph constructor that reconstructs the agent's runtime traces as graph-based intermediate representations with control and data flow described within; (2) a property registry that attaches security-relevant metadata of interacted tools \& data, and (3) a type system that performs static inference and checking over the intermediate representation. By representing agent behavior as structured programs, AgentArmor enables program analysis for sensitive data flow, trust boundaries, and policy violations. We evaluate AgentArmor on the AgentDojo benchmark, the results show that AgentArmor can reduce the ASR to 3\%, with the utility drop only 1\%.

AgentArmor: Enforcing Program Analysis on Agent Runtime Trace to Defend Against Prompt Injection

arXiv:2511.14691v1 Announce Type: cross 
Abstract: Attention is the brain's ability to selectively focus on a few specific aspects while ignoring irrelevant ones. This biological principle inspired the attention mechanism in modern Transformers. Transformers now underpin large language models (LLMs) such as GPT, but at the cost of massive training and inference energy, leading to a large carbon footprint. While brain attention emerges from neural circuits, Transformer attention relies on dot-product similarity to weight elements in the input sequence. Neuromorphic computing, especially spiking neural networks (SNNs), offers a brain-inspired path to energy-efficient intelligence. Despite recent work on attention-based spiking Transformers, the core attention layer remains non-neuromorphic. Current spiking attention (i) relies on dot-product or element-wise similarity suited to floating-point operations, not event-driven spikes; (ii) keeps attention matrices that suffer from the von Neumann bottleneck, limiting in-memory computing; and (iii) still diverges from brain-like computation. To address these issues, we propose the Spiking STDP Transformer (S$^{2}$TDPT), a neuromorphic Transformer that implements self-attention through spike-timing-dependent plasticity (STDP), embedding query--key correlations in synaptic weights. STDP, a core mechanism of memory and learning in the brain and widely studied in neuromorphic devices, naturally enables in-memory computing and supports non-von Neumann hardware. On CIFAR-10 and CIFAR-100, our model achieves 94.35\% and 78.08\% accuracy with only four timesteps and 0.49 mJ on CIFAR-100, an 88.47\% energy reduction compared to a standard ANN Transformer. Grad-CAM shows that the model attends to semantically relevant regions, enhancing interpretability. Overall, S$^{2}$TDPT illustrates how biologically inspired attention can yield energy-efficient, hardware-friendly, and explainable neuromorphic models.

Attention via Synaptic Plasticity is All You Need: A Biologically Inspired Spiking Neuromorphic Transformer

arXiv:2511.08955v2 Announce Type: replace-cross 
Abstract: Simulating microstructure evolution (MicroEvo) is vital for materials design but demands high numerical accuracy, efficiency, and physical fidelity. Although recent studies on deep learning (DL) offer a promising alternative to traditional solvers, the field lacks standardized benchmarks. Existing studies are flawed due to a lack of comparing specialized MicroEvo DL models with state-of-the-art spatio-temporal architectures, an overemphasis on numerical accuracy over physical fidelity, and a failure to analyze error propagation over time. To address these gaps, we introduce MicroEvoEval, the first comprehensive benchmark for image-based microstructure evolution prediction. We evaluate 14 models, encompassing both domain-specific and general-purpose architectures, across four representative MicroEvo tasks with datasets specifically structured for both short- and long-term assessment. Our multi-faceted evaluation framework goes beyond numerical accuracy and computational cost, incorporating a curated set of structure-preserving metrics to assess physical fidelity. Our extensive evaluations yield several key insights. Notably, we find that modern architectures (e.g., VMamba), not only achieve superior long-term stability and physical fidelity but also operate with an order-of-magnitude greater computational efficiency. The results highlight the necessity of holistic evaluation and identify these modern architectures as a highly promising direction for developing efficient and reliable surrogate models in data-driven materials science.

MicroEvoEval: A Systematic Evaluation Framework for Image-Based Microstructure Evolution Prediction

South Korea’s biggest conglomerates have pledged to invest more than $550 billion at home, in a move meant to show that the country’s industrial engine will remain firmly rooted even as Seoul deepens economic ties with Washington.

أعلنت أكبر التكتلات في كوريا الجنوبية عن خطط لاستثمار أكثر من 550 مليار دولار محليًا. يهدف هذا الاستثمار الكبير إلى تعزيز القاعدة الصناعية للبلاد بينما تعزز العلاقات الاقتصادية مع واشنطن. تعكس هذه الخطوة التزامًا بالنمو والاستقرار في الاقتصاد الكوري الجنوبي وسط ديناميكيات عالمية متغيرة.

Los mayores conglomerados de Corea del Sur han anunciado planes para invertir más de 550 mil millones de dólares en el país. Esta inversión significativa tiene como objetivo reforzar la base industrial del país mientras se profundizan los lazos económicos con Washington. El movimiento refleja un compromiso con el crecimiento y la estabilidad de la economía surcoreana en medio de dinámicas globales cambiantes.

Les plus grands conglomérats de Corée du Sud ont annoncé des projets d'investissement de plus de 550 milliards de dollars sur le marché national. Cet investissement significatif vise à renforcer la base industrielle du pays tout en améliorant les liens économiques avec Washington. Ce mouvement reflète un engagement envers la croissance et la stabilité de l'économie sud-coréenne dans un contexte mondial en évolution.

South Korea's largest conglomerates have announced plans to invest over $550 billion domestically. This significant investment aims to reinforce the country's industrial base while enhancing economic ties with Washington. The move reflects a commitment to growth and stability in South Korea's economy amidst evolving global dynamics.

Korean Giants Pledge Record Investments to Spur Growth at Home

Samsung Group and SK Group were among four of South Korea’s biggest companies that pledged to invest about $550 billion in the country after meeting with President Lee Jae Myung, who is seeking to shore up the economy following a trade deal with the US.

تعهدت مجموعة سامسونج ومجموعة SK، جنبًا إلى جنب مع شركتين كبيرتين أخريين في كوريا الجنوبية، باستثمار حوالي 550 مليار دولار في البلاد. يأتي هذا التعهد بعد اجتماع مع الرئيس لي جاي ميونغ، الذي يسعى لتعزيز الاقتصاد بعد اتفاق تجاري مع الولايات المتحدة.

El Grupo Samsung y el Grupo SK, junto con otras dos grandes empresas de Corea del Sur, se han comprometido a invertir aproximadamente 550 mil millones de dólares en el país. Este compromiso se produce tras una reunión con el presidente Lee Jae Myung, quien busca fortalecer la economía tras un reciente acuerdo comercial con Estados Unidos.

Le groupe Samsung et le groupe SK, ainsi que deux autres grandes entreprises sud-coréennes, se sont engagés à investir environ 550 milliards de dollars en Corée du Sud. Cet engagement fait suite à une rencontre avec le président Lee Jae Myung, qui vise à renforcer l'économie après un récent accord commercial avec les États-Unis.

Samsung Group and SK Group, along with two other major South Korean companies, have committed to invest approximately $550 billion in South Korea. This pledge follows a meeting with President Lee Jae Myung, who is focused on strengthening the economy in the wake of a recent trade agreement with the United States.

Korean Conglomerates Pledge $550 Billion Domestic Investment

California-based TP-Link Systems Inc. says it may take a sales hit of more than $1 billion because of erroneous reports that the networking company’s technology has been used to launch Chinese cyber attacks under Beijing’s control.

Netgear Accused by Rival of ‘China’ Smear to Stoke Security Fear

NASA is planning to divest or demolish nearly half of its footprint at the Goddard Spaceflight Center in Greenbelt, Maryland, after President Donald Trump’s efforts to shrink the federal workforce led to a mass employee exodus, according to an email seen by Bloomberg News.

NASA to Divest, Demolish Buildings at Maryland’s Goddard Center

AI developers’ ambitious spending plans and rapid user adoption indicate the global rally in their stocks will persist despite bubble concerns, fund managers at Fidelity International and Allianz Global Investors said.

Warburg-Backed Princeton Enters Korea With First Data Center

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