Modular Foundation Models for Time-Series Perception in Digital Twins
Modular foundation models for time-series perception in digital twins and prognostics health management systems.
Modular foundation models for time-series perception in digital twins and prognostics health management systems.
Graph classification framework using Network Usable Information paradigm with permutation-invariant representations and structural descriptors.
Analyzes implicit bias of noisy SGD in training wide two-layer ReLU networks via Wasserstein gradient flow convergence.
Advances generative modeling for Schrödinger bridges with reflecting dynamics to ensure generated samples remain in data domain.
LLM agent framework for transportation hub capacity planning that iteratively proposes decisions guided by natural-language business context.
Physics-informed transformer for estimating above-ground biomass in tropical forests using optical and SAR data.
Shows fine-tuned RoBERTa matches specialized detectors for AI-generated text detection; challenges recent architectural complexity in detection methods.
Physics-informed neural representation learning framework using spectral methods with learnable scaling for supervised learning.
Studies how populations of LLM agents form collective beliefs and whether they aggregate genuine knowledge or collapse into false consensus.
Adaptive CT image reconstruction using stochastic variance-driven exploration for low-dose clinical diagnostics.
Presents FedACT algorithm for federated transformer training that addresses coordinate trust mismatch in AdamW optimizer under heterogeneous data.
Develops conservative multi-objective learning framework for cross-subject generalization in EMG-based gesture recognition using deep learning.
Proposes tensor-train joint modeling to improve discrete diffusion models for faster sequential generation compared to autoregressive approaches.
Extends geometric deep learning with order-equivariant neural networks that generalize graph message passing and sheaf neural networks using equivariant bundle theory.
Study of classification-head fine-tuning for tiny language models (under 3B parameters) on multiple-choice reasoning tasks, comparing LoRA paradigms.
Two-stage framework for normalizing flow mixtures using simplex exponential moving average for stable weighting across heterogeneous posterior geometries.
Adversarial training approach for robust feature selection in high-dimensional learning, improving stability of sparse feature supports under noise.
Unified theoretical framework for strong lottery ticket hypothesis in both quantized and continuous settings using random subset sum problem.
Runtime-adaptive speculative decoding framework for CPU-constrained LLM inference, using multi-policy orchestration to optimize small quantized model performance.
Conditional diffusion model framework for multi-task offline safe reinforcement learning, handling safety constraints and out-of-distribution actions.
Transformer with physics-informed encodings for gravitational wave detection in pulsar timing array data using simulation-based inference.
Framework combining pretraining with online adaptation for EEG foundation models to handle distribution shifts and task-specific requirements.
Prompt-type selection framework for fine-tuning to improve concept unlearning in LLMs, removing biased/harmful concepts across diverse prompt variations.
Manifold-aware approach to concept erasure from neural representations, addressing preservation of correlated information during target concept removal.
Convergence prediction method for Text-to-SQL pipelines using lightweight models to determine when repeated LLM calls reach sufficient consistency.
Low-cost method to measure loss sharpness via Armijo backtracking for calibrating Adam learning rates without Hessian computations.
Empirical study comparing tabular foundation models to conventional methods for few-shot learning on crowd-state classification at religious gatherings.
Unified algebraic framework for classification metrics covering binary, multiclass, multilabel, and other evaluation settings in single formalism.
Unified survey and benchmark of 100+ optimizers for large-scale model training, providing taxonomy and selection guidance for compute-constrained scenarios.
On-policy distillation method using reward gating to improve teacher supervision reliability when transferring reasoning from strong to smaller student models.
Statistical framework analyzing in-context learning in both causal and masked language models, extending theoretical understanding beyond autoregressive models.
Federated learning framework with routing mechanism addressing dual heterogeneity using semiparametric mixtures. Handles both inter-client and intra-client latent subpopulation variations.
Reinforcement learning approach for black-box node injection attacks on GNNs. Jointly optimizes malicious node features and edge connections for adversarial attacks.
Comparison of evolutionary LLM-based scientific discovery versus dictionary selection for equation discovery. Shows independent sampling outperforms parent-conditioned evolution under matched budgets.
Schema-guided world model for multimodal LLMs to predict visual dynamics. DynaVieW models temporal evolution of videos across multiple hierarchical levels of visual change.
Theoretical analysis of diffusion and flow-matching samplers treating terminal noise scale as singular perturbation. Determines asymptotic-preserving properties of fixed-step samplers.
Multi-modal spatiotemporal forecasting system predicting glacier retreat. Combines Landsat satellite imagery with ERA5 climate variables for boundary prediction.
Zero-shot routing method for LoRA-based external parametric memory. Eliminates need for additional routing component in modular LLM solutions.
Self-supervised learning approach combining masked and contrastive learning for EEG emotion recognition. Improves cross-dataset transfer with spatiotemporal dependency modeling.
Method for generating non-targeted adversarial attacks via binary iteration. Exposes piecewise linearity in deep learning models for robustness validation.
Self-supervised learning method using mask prediction for vision-based reinforcement learning. Addresses sample efficiency in high-dimensional image inputs.
Analysis of how language models represent ordinal information geometrically. Studies attention heads performing geometric transformations across bracket depth, indentation, and numeric tasks in Gemma and Qwen models.
Federated learning approach combining Sharpness-Aware Minimization with spectral perturbation filtering. Addresses client drift and convergence problems in decentralized training.
Graph learning method for fault diagnosis in rotating machinery. Combines physics-informed approaches with uncertainty awareness for open-set domain generalization.
Federated learning framework addressing statistical heterogeneity via spectral gradient filtering. Uses frequency-domain analysis to mitigate client drift in non-IID data scenarios.
Applies Convolutional Conditional Neural Processes to weather downscaling. Uses neural processes to increase ERA5-Land resolution from 11km to 1km for temperature prediction in mountainous regions.
Federated learning approach for over-the-air wireless aggregation in heterogeneous networks. Addresses noise and fading in privacy-preserving IoT and autonomous systems.
Efficient inference method for Qwen3.5-4B using quantization and speculative decoding. Combines quantized target model with block-diffusion drafter for low-latency GPU serving.
Research on generative models for sensor time series data. Studies how generative models handle continuous, high-dimensional, noisy sensor data across different modalities and tasks.
Claude Fable 5 performance degradation after July release. Users report coding and agentic capabilities decline; Anthropic attributes to safety updates.