Efficient Federated Search for Retrieval-Augmented Generation using Lightweight Routing
Federated search mechanism for retrieval-augmented generation across distributed knowledge sources to reduce LLM hallucinations.
Federated search mechanism for retrieval-augmented generation across distributed knowledge sources to reduce LLM hallucinations.
ShuffleGate: unified gating mechanism for feature selection, model compression, and importance estimation in recommender systems.
AEQ-RVAE-ST: recurrent variational autoencoder with progressive training for quasi-periodic time series generation.
Study of adversarial robustness in tabular foundation models like TabPFN and TabICL, examining test-time attacks and in-context defenses.
DiffGradCAM improves class activation maps for CNNs by addressing adversarial vulnerabilities in gradient-based explanation methods.
BTC-LLM: sub-1-bit quantization framework for LLMs using learnable transformations and binary codebooks for extreme compression.
Data-driven interpolation method for functions on smooth manifolds using Laplace-Beltrami operators and Voronoi tessellations with diffusion processes.
Predicts case suffix in business processes with start/end timestamps for resource capacity planning using sequence models.
Investigates Kolmogorov-Arnold Networks as interpretable alternatives to black-box models for clinical tabular data classification.
Neural diffusion method for estimating transfer entropy in time series addressing curse of dimensionality and convergence issues.
Adaptive privacy budget allocation framework for mobile edge crowdsensing balancing privacy, utility, and device overhead.
Applies XAI techniques (Grad-CAM, SHAP) to interpret PhaseNet deep neural network for microseismic event detection.
Quantitative bounds analysis for permutation-invariant embeddings using sorting-based projections relevant to graph deep learning.
Theoretical analysis of interactions between chicken swarm optimization-based particle rejuvenation and KLD-adaptive sampling in particle filters.
Generative model combining adversarial and flow-based families with native one-step/multi-step generation trained via adversarial objective.
Research on high-dimensional Bayesian optimization showing simple Bayesian linear regression outperforms complex BO methods after geometric transformation.
Neural operator framework for solving PDEs on spherical domains using Green's function formulation preserving rotational geometry.
Case study applying LLMs to structured financial fraud detection data with focus on interpretability and feature analysis.
Comparative evaluation of embedding techniques for financial news sentiment analysis in resource-constrained environments.
First empirical study of machine unlearning in hybrid quantum-classical neural networks with adaptation of classical unlearning methods.
Empirical study of tabular foundation models versus classical ML for healthcare applications under class imbalance in critical care.
Tree-structured advantage redistribution method for group-based RL improving sample efficiency in LLM alignment on reasoning tasks.
Sample-efficient reinforcement learning algorithm for Value-at-Risk constrained optimization with safety guarantees during training.
Benchmark for evaluating multimodal LLMs on multi-criteria route planning reasoning tasks in heterogeneous graphs.
Extension of TabPFN foundation model to handle multimodal tabular data combining images, text and structured features.
Theoretical analysis explaining Adam optimizer's empirical advantages over SGD through second-moment normalization properties.
JAX framework for gradient-based training of spiking neural networks using differentiable ODE solving with exact gradients.
Theoretical analysis of classifier-free guidance in diffusion models with bounds on score discrepancy for controlled guidance weights.
Theoretical analysis proving attention sinks are functionally necessary in softmax Transformers for trigger-conditional tasks.
Framework for identifying symbolic physical laws from noisy data by minimizing action functional with sparsity and energy conservation.
vLLM Semantic Router architecture for optimizing LLM inference with routing mechanisms, semantic caching, and safety classification.
Computationally efficient classification algorithm with frequentist uncertainty bounds for safety-critical applications.
Theoretical framework unifying classifier-free guidance with alignment objectives in diffusion models for generative modeling.
Theoretical analysis of sample complexity bounds for multi-group learning using one-inclusion graph prediction strategy.
Multi-agent reinforcement learning framework addressing robustness to data corruption in preference-based learning from human feedback.
Analysis of AI scaling requiring repeated efficiency doublings, distinguishing logical compute from physical resource implementation efficiency.
Biomimetic physics-informed neural networks for modeling microstructure-forming phase transitions in cellular matrices.
Physics-informed label-free pretraining method for coupled multiphysics simulation surrogates using operator-split latent prediction.
Bayesian information-theoretic approach to training data attribution that traces model predictions to influential training examples for interpretability.
STQuant framework for adaptive spatio-temporal quantization of optimizer states during large multimodal model training to reduce memory costs.
Quantum computing approach for option pricing using tensor networks to prepare quantum states encoding asset price distributions.
Adaptive neural networks for autonomous micro-drones with computational constraints via dynamic slimmable network architecture.
Theoretical analysis of offset noise in diffusion models to address brightness value generation challenges in large-scale models.
DMin framework for scalable training data influence estimation in diffusion models, enabling identification of influential training samples on generated outputs.
VAE-diffusion framework for generating high-quality SVG graphics from text with structural understanding.
Comprehensive privacy attack analysis on image autoregressive models, identifying membership inference and extraction vulnerabilities.
Method for enforcing syntactic and semantic constraints in LLM decoding through MCTS-guided token-level control.
Large-scale corpus of 324,843 Python classes from open-source projects for training and evaluating LLMs on code generation.
RAG-based LLM workflow using domain-specific knowledge graph for automated single-cell type annotation in biology.
Study evaluating sparse autoencoders for detecting bugs in Java code, addressing software vulnerability detection.