All papers

The paper introduces an adversarial training framework that hardens models against indirect prompt injection across retrieval and tool interaction channels.

The paper extends undirected graphical models to non-Gaussian data using copula densities and provides an estimation algorithm.

The paper introduces an online tokenization algorithm that processes text streams in linear time with minimal algorithmic delay.

The paper shows how graph databases can execute graph neural network sampling, retrieval, and inference directly without separate in-memory pipelines.

The paper examines teacher interventions along student generation paths to identify how teacher guidance drives performance gains in on-policy distillation.

The paper analyzes packet routing throughput when selfish queue agents use no-regret learning algorithms to select buffered servers.

The paper proposes a Bayesian full waveform inversion framework that uses learned diffusion models as regularizers for geological reconstruction.

The paper develops an adaptive computation framework offering conformal uncertainty guarantees for processing large scientific and medical imagery efficiently.

The paper proposes a directed graph neural network approach to forecast twelve-hour wildfire spread using spatial and environmental attributes.

The paper introduces a method to convert dense models into mixture-of-experts by dynamically allocating different numbers of active experts across layers.

The paper develops a continuous forecasting method using speech representations to anticipate stuttering events shortly before they occur.

The paper demonstrates that language models use modular abstract representations to generalize code generation across different programming languages and strategies.

The paper presents a framework using Wasserstein geometry and Shapley values to audit and decompose intersectional fairness disparities across multiple attributes.

The paper introduces a neural solver architecture to accurately compute solutions and sensitivities for parametric stochastic volatility partial differential equations.

The paper presents an outcome-routed distillation and reinforcement learning method tailored for training mixture-of-experts diffusion language models.

The paper derives theoretical scaling laws for prediction regret in a capacity-constrained bigram model with power-law token frequencies.

The paper introduces an agentic reinforcement learning approach that uses formal program verifier diagnostics as intermediate rewards to produce verified code.

The paper proposes an acquisition-time residual adaptation method that uses historical features to improve hit rates in sequential screening.

The paper examines why molecular graph transformers avoid attention sink patterns seen in language models and highlights attention design impacts.

The paper introduces a framework to translate and decompose clinical measurement records across heterogeneous assessment systems within predefined error tolerances.

The paper presents a dynamic graph framework to jointly forecast evolving node and relation representations from historical graph contexts.

The paper proposes a latent-space unlearning method that preserves model performance on benign tasks conceptually adjacent to forgotten domains.

The paper presents a sequential Gaussian process method and sampling policy for continuous optimization based on pairwise preference comparisons.

The paper applies a lightweight post-hoc classifier to evaluate confidence and filter out incorrect next-location predictions from recurrent models.

The paper introduces an experimental benchmark to evaluate active learning and Bayesian optimization strategies for synthesizing metal-organic frameworks.

The paper introduces a self-supervised wavelet predictive architecture to extract multiscale motion features from wearable activity sensors.

The paper benchmarks four reinforcement learning algorithms on safety, data efficiency, and stability for autonomous vehicle intersection navigation.

The paper introduces a progress-aware distillation method to prevent small language model agents from stalling in repetitive loops during long tasks.

The paper establishes theoretical last-iterate convergence rates for random-reshuffling stochastic gradient descent using larger stepsizes.

The paper proposes a sequential conformal prediction algorithm that groups calibration scores by state similarity rather than pure temporal recency.

The paper applies transformer neural operators to model interference channels and cancel digital self-interference in full-duplex radio systems.

The paper presents a mathematical framework unifying symplectic geometry, kernel methods, and physical conservation laws for physics-constrained learning.

The paper introduces a heterogeneous graph variational autoencoder to model design and state variables jointly for analog circuit analysis.

The paper establishes theoretical high-probability regret bounds for feel-good Thompson sampling algorithms in contextual bandit problems.

The paper proposes a neural network using physics-inspired Hankel convolution kernels for lightweight path loss estimation in wireless networks.

The paper introduces a two-stage residual learning framework to detect local inaccuracies and guide the optimization of thermal systems.

The paper develops methods to evaluate and recalibrate binary predictors using noisy, multi-annotator labels without requiring clean ground truth.

The paper uses recurrent gradient descent iterations during test time to enable foundation models to forecast unseen dynamical systems.

The paper introduces a continuous, monotonic geometric kernel measure and algorithms to efficiently compute diversity over Euclidean points.

The paper develops a theoretical Rényi-divergence framework to measure inherent differential privacy guarantees obtained directly from Bayesian posterior sampling.

The paper proposes a variational method using Wasserstein-Fisher-Rao geometry to estimate the background intensity of Hawkes processes.

The paper introduces a model for de novo protein sequence generation guided by natural language instructions and biological context encoders.

The paper applies conformal risk control to decide when to trust machine-learned interatomic potentials versus running expensive physics calculations.

The paper develops a graph-to-sequence pretraining framework using rule-generated synthetic reactions to predict chemical biodegradation pathways.

The paper proves theoretical joint lower bounds on oracle complexity for zeroth-order nonconvex stochastic optimization.

The paper proposes a generative diffusion framework that anneals heavy-tailed noise into Gaussian noise to combine exploration with stable refinement.

The paper introduces a two-stage low-rank fine-tuning method that uses residual gradients to add complementary adaptation directions without expanding the rank.

The paper presents an agentic system that iteratively writes and adjusts forecasting pipelines around frozen time-series models to improve predictions.

Introduces an adaptive spectral gradient clipping method to stabilize and accelerate large language model pretraining.

Investigates why looped transformers fail to retain or reuse earlier correct intermediate states when extended past training depth.

Evaluates whether dependency graphs improve automated mapping between source code files and system architectures.

Provides theoretical convergence bounds for score-based generative models driven by heavy-tailed stable processes.

Proposes a sequential string notation to represent and generate valid graphs using standard sequence models.

Formulates clinical population health stratification as a multi-treatment causal ranking problem to prioritize interventions.

Uses supervised advantage conditioning to train reinforcement learning policies with improved stability.

Applies positive-unlabeled learning to establish mathematically exact negative guidance in diffusion generative models.

Presents an efficient regression-based algorithm for contextual partial monitoring games with side information.

Studies statistical calibration techniques to bound risk when fast language models make automated decisions under uncertainty.

Analyzes layerwise inference across tabular foundation models to understand how depth and intermediate representations affect predictions.

Presents a serving system that dynamically manages GPU memory and prefetches weights for multi-model agent workflows.