Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generation with Hard Constraints
arXiv:2602.16954v1 Announce Type: new Abstract: We challenge black-box purely deep neural approaches for molecules and graph generation, which are limited in controllability and lack formal guarantees. We introduce Neuro-Symbolic Graph Generative Modeling (NSGGM), a neurosymbolic framework that reapproaches molecule generation...
Dynamic Delayed Tree Expansion For Improved Multi-Path Speculative Decoding
arXiv:2602.16994v1 Announce Type: new Abstract: Multi-path speculative decoding accelerates lossless sampling from a target model by using a cheaper draft model to generate a draft tree of tokens, and then applies a verification algorithm that accepts a subset of these....
AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation
arXiv:2602.17071v1 Announce Type: new Abstract: Graph neural networks frequently encounter significant performance degradation when confronted with structural noise or non-homophilous topologies. To address these systemic vulnerabilities, we present AdvSynGNN, a comprehensive architecture designed for resilient node-level representation learning. The proposed...
Adam Improves Muon: Adaptive Moment Estimation with Orthogonalized Momentum
arXiv:2602.17080v1 Announce Type: new Abstract: Efficient stochastic optimization typically integrates an update direction that performs well in the deterministic regime with a mechanism adapting to stochastic perturbations. While Adam uses adaptive moment estimates to promote stability, Muon utilizes the weight...
A Locality Radius Framework for Understanding Relational Inductive Bias in Database Learning
arXiv:2602.17092v1 Announce Type: new Abstract: Foreign key discovery and related schema-level prediction tasks are often modeled using graph neural networks (GNNs), implicitly assuming that relational inductive bias improves performance. However, it remains unclear when multi-hop structural reasoning is actually necessary....
Mitigating Gradient Inversion Risks in Language Models via Token Obfuscation
arXiv:2602.15897v1 Announce Type: new Abstract: Training and fine-tuning large-scale language models largely benefit from collaborative learning, but the approach has been proven vulnerable to gradient inversion attacks (GIAs), which allow adversaries to reconstruct private training data from shared gradients. Existing...
A Koopman-Bayesian Framework for High-Fidelity, Perceptually Optimized Haptic Surgical Simulation
arXiv:2602.15834v1 Announce Type: new Abstract: We introduce a unified framework that combines nonlinear dynamics, perceptual psychophysics and high frequency haptic rendering to enhance realism in surgical simulation. The interaction of the surgical device with soft tissue is elevated to an...
Distributed physics-informed neural networks via domain decomposition for fast flow reconstruction
arXiv:2602.15883v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) offer a powerful paradigm for flow reconstruction, seamlessly integrating sparse velocity measurements with the governing Navier-Stokes equations to recover complete velocity and latent pressure fields. However, scaling such models to large...
Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort
arXiv:2602.15955v1 Announce Type: new Abstract: A P300 ERP-based Brain-Computer Interface (BCI) speller is an assistive communication tool. It searches for the P300 event-related potential (ERP) elicited by target stimuli, distinguishing it from the neural responses to non-target stimuli embedded in...
Anatomy of Capability Emergence: Scale-Invariant Representation Collapse and Top-Down Reorganization in Neural Networks
arXiv:2602.15997v1 Announce Type: new Abstract: Capability emergence during neural network training remains mechanistically opaque. We track five geometric measures across five model scales (405K-85M parameters), 120+ emergence events in eight algorithmic tasks, and three Pythia language models (160M-2.8B). We find:...
MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching
arXiv:2602.16020v1 Announce Type: new Abstract: Molecular crystal structure prediction represents a grand challenge in computational chemistry due to large sizes of constituent molecules and complex intra- and intermolecular interactions. While generative modeling has revolutionized structure discovery for molecules, inorganic solids,...
AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models
arXiv:2602.16042v1 Announce Type: new Abstract: As machine learning (ML) continues its rapid expansion, the environmental cost of model training and inference has become a critical societal concern. Existing benchmarks overwhelmingly focus on standard performance metrics such as accuracy, BLEU, or...
Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning
arXiv:2602.16167v1 Announce Type: new Abstract: Physics-informed neural networks and neural operators often suffer from severe optimization difficulties caused by ill-conditioned gradients, multi-scale spectral behavior, and stiffness induced by physical constraints. Recently, the Muon optimizer has shown promise by performing orthogonalized...
ModalImmune: Immunity Driven Unlearning via Self Destructive Training
arXiv:2602.16197v1 Announce Type: new Abstract: Multimodal systems are vulnerable to partial or complete loss of input channels at deployment, which undermines reliability in real-world settings. This paper presents ModalImmune, a training framework that enforces modality immunity by intentionally and controllably...
Geometric Neural Operators via Lie Group-Constrained Latent Dynamics
arXiv:2602.16209v1 Announce Type: new Abstract: Neural operators offer an effective framework for learning solutions of partial differential equations for many physical systems in a resolution-invariant and data-driven manner. Existing neural operators, however, often suffer from instability in multi-layer iteration and...
Graph neural network for colliding particles with an application to sea ice floe modeling
arXiv:2602.16213v1 Announce Type: new Abstract: This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ice, where nodes represent individual ice pieces, and edges model the physical interactions,...
Prescriptive Scaling Reveals the Evolution of Language Model Capabilities
arXiv:2602.15327v1 Announce Type: cross Abstract: For deploying foundation models, practitioners increasingly need prescriptive scaling laws: given a pre training compute budget, what downstream accuracy is attainable with contemporary post training practice, and how stable is that mapping as the field...
Learning Data-Efficient and Generalizable Neural Operators via Fundamental Physics Knowledge
arXiv:2602.15184v1 Announce Type: new Abstract: Recent advances in scientific machine learning (SciML) have enabled neural operators (NOs) to serve as powerful surrogates for modeling the dynamic evolution of physical systems governed by partial differential equations (PDEs). While existing approaches focus...
Scaling Laws for Masked-Reconstruction Transformers on Single-Cell Transcriptomics
arXiv:2602.15253v1 Announce Type: new Abstract: Neural scaling laws -- power-law relationships between loss, model size, and data -- have been extensively documented for language and vision transformers, yet their existence in single-cell genomics remains largely unexplored. We present the first...
FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learning
arXiv:2602.15337v1 Announce Type: new Abstract: Asynchronous Federated Learning (AFL) has emerged as a significant research area in recent years. By not waiting for slower clients and executing the training process concurrently, it achieves faster training speed compared to traditional federated...
Fractional-Order Federated Learning
arXiv:2602.15380v1 Announce Type: new Abstract: Federated learning (FL) allows remote clients to train a global model collaboratively while protecting client privacy. Despite its privacy-preserving benefits, FL has significant drawbacks, including slow convergence, high communication cost, and non-independent-and-identically-distributed (non-IID) data. In...
ExLipBaB: Exact Lipschitz Constant Computation for Piecewise Linear Neural Networks
arXiv:2602.15499v1 Announce Type: new Abstract: It has been shown that a neural network's Lipschitz constant can be leveraged to derive robustness guarantees, to improve generalizability via regularization or even to construct invertible networks. Therefore, a number of methods varying in...
On the Geometric Coherence of Global Aggregation in Federated GNN
arXiv:2602.15510v1 Announce Type: new Abstract: Federated Learning (FL) enables distributed training across multiple clients without centralized data sharing, while Graph Neural Networks (GNNs) model relational data through message passing. In federated GNN settings, client graphs often exhibit heterogeneous structural and...
Accelerated Predictive Coding Networks via Direct Kolen-Pollack Feedback Alignment
arXiv:2602.15571v1 Announce Type: new Abstract: Predictive coding (PC) is a biologically inspired algorithm for training neural networks that relies only on local updates, allowing parallel learning across layers. However, practical implementations face two key limitations: error signals must still propagate...
A unified theory of feature learning in RNNs and DNNs
arXiv:2602.15593v1 Announce Type: new Abstract: Recurrent and deep neural networks (RNNs/DNNs) are cornerstone architectures in machine learning. Remarkably, RNNs differ from DNNs only by weight sharing, as can be shown through unrolling in time. How does this structural similarity fit...
GTS: Inference-Time Scaling of Latent Reasoning with a Learnable Gaussian Thought Sampler
arXiv:2602.14077v1 Announce Type: new Abstract: Inference-time scaling (ITS) in latent reasoning models typically introduces stochasticity through heuristic perturbations, such as dropout or fixed Gaussian noise. While these methods increase trajectory diversity, their exploration behavior is not explicitly modeled and can...
Character-aware Transformers Learn an Irregular Morphological Pattern Yet None Generalize Like Humans
arXiv:2602.14100v1 Announce Type: new Abstract: Whether neural networks can serve as cognitive models of morphological learning remains an open question. Recent work has shown that encoder-decoder models can acquire irregular patterns, but evidence that they generalize these patterns like humans...
Exploring the Performance of ML/DL Architectures on the MNIST-1D Dataset
arXiv:2602.13348v1 Announce Type: new Abstract: Small datasets like MNIST have historically been instrumental in advancing machine learning research by providing a controlled environment for rapid experimentation and model evaluation. However, their simplicity often limits their utility for distinguishing between advanced...
High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator
arXiv:2602.13416v1 Announce Type: new Abstract: The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating exceptional skill in medium-range forecasting. However, these models are often limited by long-term instabilities, climatological...
Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability
arXiv:2602.13485v1 Announce Type: new Abstract: Networks of modern industrial systems are increasingly monitored by distributed sensors, where each system comprises multiple subsystems generating high dimensional time series data. These subsystems are often interdependent, making it important to understand how temporal...