arXiv:2511.08625v1 Announce Type: cross 
Abstract: Multiphase flow systems, with their complex dynamics, field discontinuities, and interphase interactions, pose significant computational challenges for traditional numerical solvers. While neural operators offer efficient alternatives, they often struggle to achieve high-resolution numerical accuracy in these systems. This limitation primarily stems from the inherent spatial heterogeneity and the scarcity of high-quality training data in multiphase flows. In this work, we propose the Interface Information-Aware Neural Operator (IANO), a novel framework that explicitly leverages interface information as a physical prior to enhance the prediction accuracy. The IANO architecture introduces two key components: 1) An interface-aware multiple function encoding mechanism jointly models multiple physical fields and interfaces, thus capturing the high-frequency physical features at the interface. 2) A geometry-aware positional encoding mechanism further establishes the relationship between interface information, physical variables, and spatial positions, enabling it to achieve pointwise super-resolution prediction even in the low-data regimes. Experimental results demonstrate that IANO outperforms baselines by $\sim$10\% in accuracy for multiphase flow simulations while maintaining robustness under data-scarce and noise-perturbed conditions.

تم اقتراح إطار جديد يسمى مشغل الشبكة العصبية المدرك لمعلومات الواجهة (IANO) لتحسين محاكاة أنظمة التدفق المتعدد الأطوار، التي تعتبر معقدة وتحديًا للمحللات العددية التقليدية. يحسن IANO دقة التنبؤ بنحو 10% ويحافظ على متانته حتى في ظروف نقص البيانات، مما يجعله تقدمًا كبيرًا في ديناميات السوائل الحاسوبية.

Se ha propuesto un nuevo marco llamado Interface Information-Aware Neural Operator (IANO) para mejorar la simulación de sistemas de flujo multifásico, que son complejos y desafiantes para los solucionadores numéricos tradicionales. IANO mejora la precisión de las predicciones en aproximadamente un 10% y mantiene su robustez incluso en condiciones de escasez de datos, lo que representa un avance significativo en la dinámica de fluidos computacional.

Un nouveau cadre appelé l'Interface Information-Aware Neural Operator (IANO) a été proposé pour améliorer la simulation des systèmes d'écoulement multiphasique, qui sont complexes et difficiles pour les solveurs numériques traditionnels. IANO améliore la précision des prédictions d'environ 10 % et maintient sa robustesse même dans des conditions de données rares, ce qui en fait une avancée significative en dynamique des fluides computationnelle.

A new framework called the Interface Information-Aware Neural Operator (IANO) has been proposed to enhance the simulation of multiphase flow systems, which are complex and challenging for traditional numerical solvers. IANO improves prediction accuracy by approximately 10% and maintains robustness even in data-scarce conditions, making it a significant advancement in computational fluid dynamics.

Cross-Field Interface-Aware Neural Operators for Multiphase Flow Simulation

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

arXiv:2511.10829v1 Announce Type: new 
Abstract: Although neural operators are widely used in data-driven physical simulations, their training remains computationally expensive. Recent advances address this issue via downstream learning, where a model pretrained on simpler problems is fine-tuned on more complex ones. In this research, we investigate transformer-based neural operators, which have previously been applied only to specific problems, in a more general transfer learning setting. We evaluate their performance across diverse PDE problems, including extrapolation to unseen parameters, incorporation of new variables, and transfer from multi-equation datasets. Our results demonstrate that advanced neural operator architectures can effectively transfer knowledge across PDE problems.

تناقش المقالة البحثية بعنوان 'نحو مشغلات عصبية عالمية من خلال التدريب المسبق متعدد الفيزياء' التقدم في المشغلات العصبية المستخدمة في المحاكاة الفيزيائية المعتمدة على البيانات. تبرز المقالة التكلفة الحاسوبية العالية لتدريب هذه النماذج وتستكشف تطبيق المشغلات العصبية المعتمدة على المحولات في سياق أوسع للتعلم بالنقل. تقيم الدراسة أدائها عبر مشاكل متنوعة من المعادلات التفاضلية الجزئية، مما يظهر أن الهياكل المتقدمة يمكن أن تنقل المعرفة بفعالية عبر سيناريوهات مختلفة من المعادلات التفاضلية الجزئية.

El artículo de investigación titulado 'Hacia operadores neuronales universales a través del preentrenamiento multiphísico' discute los avances en los operadores neuronales utilizados para simulaciones físicas basadas en datos. Destaca el alto costo computacional del entrenamiento de estos modelos y explora la aplicación de operadores neuronales basados en transformadores en un contexto más amplio de aprendizaje por transferencia. El estudio evalúa su rendimiento en diversos problemas de ecuaciones diferenciales parciales (EDP), demostrando que las arquitecturas avanzadas pueden transferir efec…

L'article de recherche intitulé 'Vers des opérateurs neuronaux universels grâce à un pré-entraînement multiphysique' aborde les avancées des opérateurs neuronaux utilisés pour les simulations physiques basées sur les données. Il met en lumière le coût computationnel élevé de l'entraînement de ces modèles et explore l'application des opérateurs neuronaux basés sur des transformateurs dans un contexte d'apprentissage par transfert plus large. L'étude évalue leur performance sur divers problèmes d'équations différentielles partielles (EDP), démontrant que des architectures avancées peuvent transf…

The research article titled 'Towards Universal Neural Operators through Multiphysics Pretraining' discusses advancements in neural operators used for data-driven physical simulations. It highlights the computational expense of training these models and explores the application of transformer-based neural operators in a broader transfer learning context. The study evaluates their performance across various partial differential equation (PDE) problems, demonstrating that advanced architectures can effectively transfer knowledge across different PDE scenarios.

Cross-Field Interface-Aware Neural Operators for Multiphase Flow Simulation

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