research

Near-Optimal Convergence of Accelerated Gradient Methods under Generalized and (L_0, L_1)-Smoothness

arXiv:2508.06884v2 Announce Type: replace-cross Abstract: We study first-order methods for convex optimization problems with functions f satisfying the recently proposed ell-smoothness condition ||nab

Neural Acceleration for Graph Partitioning

DGX agent

arXiv:2605.21519v1 Announce Type: cross Abstract: Graph Partitioning is a critical problem in numerous scientific and engineering domains including social network analysis, VLSI design, and many more.

researcharxiv-cs-lg

23 May 2026

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approcimations

arXiv:2605.22557v1 Announce Type: new Abstract: We introduce an abstract neural flow framework for neural networks and neural operators. The framework contains two continuous-depth models, namely neur

Neuro-Symbolic AI for Analytical Solutions of Differential Equations

arXiv:2502.01476v4 Announce Type: replace Abstract: Analytical solutions to differential equations offer exact, interpretable insight but are rarely available because discovering them requires expert

Noise Schedule Design for Diffusion Models: An Optimal Control Perspective

arXiv:2605.21911v1 Announce Type: new Abstract: We develop a principled framework for analyzing and designing noise schedules in diffusion models. We show that one can recast this design problem as an

On Statistical Estimation of Edge-Reinforced Random Walks

arXiv:2503.06115v2 Announce Type: replace-cross Abstract: Reinforced random walks (RRWs), including vertex-reinforced random walks (VRRWs) and edge-reinforced random walks (ERRWs), model random walks

Optimal Guarantees for Auditing Renyi Differentially Private Machine Learning

arXiv:2605.21938v1 Announce Type: new Abstract: We study black-box auditing for machine learning algorithms that claim R 'enyi differential privacy (RDP) guarantees. We introduce an auditing framework

Optimization over the intersection of manifolds

arXiv:2605.22736v1 Announce Type: cross Abstract: Optimization over the intersection of two manifolds arises in a broad range of applications, but is hindered by the coupled geometry of the feasible r

Partial Fusion of Neural Networks: Efficient Tradeoffs Between Ensembles and Weight Aggregation

arXiv:2605.22350v1 Announce Type: new Abstract: Ensembles of neural networks typically outperform individual networks but incur large computational costs, whereas weight aggregation produces less cost

PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting

arXiv:2605.21550v1 Announce Type: new Abstract: Electricity load peak forecasting (ELPF), simultaneously predicting peak timing and intensity, is a prerequisite for effective grid scheduling and risk

Physics Priors Offer Useful Accuracy-Carbon Trade-Offs in Spatio-Temporal Forecasting

arXiv:2509.24517v2 Announce Type: replace Abstract: Development of modern deep learning methods has been driven primarily by the push for improving model efficacy (accuracy metrics). This sole focus o

Predicting Performance of Symbolic and Prompt Programs with Examples

arXiv:2605.21515v1 Announce Type: new Abstract: LLM prompting is widely used for naturally stated tasks, yet it is unreliable it may succeed on a few test cases but fail at deployment time. We study p

Prior shift estimation for positive unlabeled data through the lens of kernel embedding

arXiv:2502.21194v3 Announce Type: replace-cross Abstract: We study estimation of a class prior for unlabeled target samples which possibly differs from that of source population. Moreover, it is assum

Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series

arXiv:2605.22055v1 Announce Type: new Abstract: Time Series Classification (TSC) is a long-standing research problem that has gained increasing attention in recent years with the rapid growth of large

Provably Protecting Fine-Tuned LLMs from Training Data Extraction while Preserving Utility

arXiv:2602.00688v2 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) on sensitive datasets raises privacy concerns, as training data extraction (TDE) attacks can expose highly

Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices

arXiv:2605.22097v1 Announce Type: cross Abstract: Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challen

Quantitative coronary calcification analysis for prediction of myocardial ischemia using non-contrast CT calcium scoring

arXiv:2605.21745v1 Announce Type: new Abstract: Non-contrast computed tomography calcium scoring (CTCS) is widely recognized as an effective tool for cardiovascular risk stratification. This study aim

RADAR: Defending RAG Dynamically against Retrieval Corruption

arXiv:2605.22041v1 Announce Type: cross Abstract: While RAG systems are increasingly deployed in dynamic web search, temporal volatility amplifies their vulnerability to adversarial attacks. Existing

Regret-Based (epsilon,elta)-optimal Stopping Criteria for Bayesian Optimization

arXiv:2605.22561v1 Announce Type: new Abstract: Bayesian optimization (BO) is a widely used iterative black-box optimization method that utilizes Gaussian process (GP) surrogate models. In practice, B

Reinforced Graph of Thoughts: RL-Driven Adaptive Prompting for LLMs

arXiv:2605.22195v1 Announce Type: new Abstract: Graph of Thoughts (GoT), a generalized form of recent prompting paradigms for large language models (LLMs), has been shown to be useful for elaborate pr

Reinforcement learning for ion shuttling on trapped-ion quantum computers

arXiv:2605.22463v1 Announce Type: cross Abstract: Scalable trapped-ion quantum computing is commonly realized with modular chips that feature distinct zones with specific functionalities, such as stor

Relational Linear Properties in Language Models: An Empirical Investigation

arXiv:2605.22532v1 Announce Type: new Abstract: Linear properties are ubiquitous in the representations of language models; however, testing them experimentally remains a challenging task. This work f

Richer Bayesian Last Layers with Subsampled NTK Features

arXiv:2602.01279v2 Announce Type: replace Abstract: Bayesian Last Layers (BLLs) provide a convenient and computationally efficient way to estimate uncertainty in neural networks. However, they underes

Same Architecture, Different Capacity: Optimizer-Induced Spectral Scaling Laws

arXiv:2605.21803v1 Announce Type: new Abstract: Scaling laws have made language-model performance predictable from model size, data, and compute, but they typically treat the optimizer as a fixed trai

SCI-Defense: Defending Manipulation Attacks from Generative Engine Optimization

arXiv:2605.21948v1 Announce Type: new Abstract: LLM-based ranking systems are vulnerable to Generative Engine Optimization (GEO) attacks, where adversaries inject semantic signals into product descrip

SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis

arXiv:2605.22776v1 Announce Type: new Abstract: Survival analysis aims to estimate a time-to-event distribution from data with censored observations. Many existing methods either impose structural ass

Self-orthogonalizing attractor neural networks emerging from the free energy principle

arXiv:2505.22749v2 Announce Type: replace-cross Abstract: Attractor dynamics are a hallmark of many complex systems, including the brain. Understanding how such self-organizing dynamics emerge from fi

SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection

arXiv:2605.22331v1 Announce Type: new Abstract: Despite strong predictive results in the clinical machine learning literature, the translation of these models into bedside use remains limited by syste

Soft Bayesian Context Tree Models for Real-Valued Time Series

arXiv:2601.11079v2 Announce Type: replace Abstract: This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers

Sparse Orthogonal Parameters Tuning for Continual Learning

arXiv:2411.02813v3 Announce Type: replace Abstract: Continual learning methods based on pre-trained models (PTM) have recently gained attention which adapt to successive downstream tasks without catas

SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents

arXiv:2602.11210v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a key paradigm for training software engineering (SWE) agents, but existing pipelines typically rely on

TBP-mHC: full expressivity for manifold-constrained hyper connections through transportation polytopes

arXiv:2605.21724v1 Announce Type: new Abstract: Hyper-Connections (HC) improve residual networks by introducing learnable mixing across multiple residual streams, but unconstrained mixing leads to tra

Ternary Decision Trees with Locally-Adaptive Uncertainty Zones

arXiv:2605.22740v1 Announce Type: new Abstract: Decision trees partition the feature space using hard binary thresholds, assigning identical confidence to instances far from a decision boundary and to

The Distillation Game: Adaptive Attacks & Efficient Defenses

arXiv:2605.22737v1 Announce Type: new Abstract: Distillation attacks create a deployment trade-off for model providers: the same outputs that make a model more useful can also make it easier to imitat

The Journal of the @americanacad just published a new issue of Daedalus on AI and Science, edited by James Manyika. It has terrific line-up …

The Journal of the @americanacad just published a new issue of Daedalus on AI and Science, edited by James Manyika. It has terrific line-up of contributors, including @demishassabis, @ylecun, Josh Ten

researchyann-lecun--x

The Neural Compiler: Program-to-Network Translation for Hybrid Scientific Machine Learning

arXiv:2605.22498v1 Announce Type: new Abstract: Scientific machine learning often requires combining known physics with unknown parameters or correction terms learned from data. Existing approaches ei

The Value of Covariance Matching in Gaussian DDPMs and the Lanczos Sampler

arXiv:2605.22723v1 Announce Type: new Abstract: A central error measure in Gaussian DDPMs is the path-space KL divergence between the exact reverse chain and the learned Gaussian reverse process. This

Three Costs of Amortizing Gaussian Process Inference with Neural Processes

arXiv:2605.21798v1 Announce Type: new Abstract: Neural processes amortize Gaussian process inference, replacing the exact O(n^3) posterior with a learned O(n) map from context sets to predictive distr

TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting

arXiv:2605.22365v1 Announce Type: cross Abstract: Time Series Forecasting (TSF) plays a critical role across many domains, yet it is vulnerable to backdoor attacks. However, backdoor defenses tailored

TONIC: Token-Centric Semantic Communication for Task-Oriented Wireless Systems

arXiv:2605.21553v1 Announce Type: new Abstract: Tokens are becoming the basic units through which foundation models represent and process information for understanding and inference. However, traditio

Toward Understanding Adversarial Distillation: Why Robust Teachers Fail

arXiv:2605.21999v1 Announce Type: new Abstract: Adversarial Distillation aims to enhance student robustness by guiding the student with a robust teacher's soft labels within the min-max adversarial tr

Towards Explainability of SLMs by investigating Token Level Activation

arXiv:2605.22377v1 Announce Type: new Abstract: Transformer-based language models such as BERT having 110M+ parameters have revolutionized natural language understanding, yet their internal mechanisms

Towards Solving the Gilbert-Pollak Conjecture via Large Language Models

arXiv:2601.22365v2 Announce Type: replace-cross Abstract: The Gilbert-Pollak Conjecture itep{gilbert1968steiner}, also known as the Steiner Ratio Conjecture, states that for any finite point set in th

Turning Trust to Transactions: Tracking Affiliate Marketing and FTC Compliance in YouTube's Influencer Economy

arXiv:2603.04383v2 Announce Type: replace-cross Abstract: YouTube has evolved into a powerful platform where creators monetize their influence through affiliate marketing, raising concerns about trans

Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imaging

arXiv:2603.21717v4 Announce Type: replace Abstract: Distribution-to-distribution generative models support scientific imaging tasks ranging from modeling cellular perturbation responses to translating

Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation

arXiv:2605.22765v1 Announce Type: new Abstract: Discrete diffusion models are often trained through clean-data prediction, but the prediction can be used in different ways to define the reverse dynami

Uniform-in-Time Weak Propagation-of-Chaos in Shallow Neural Networks

arXiv:2605.22010v1 Announce Type: cross Abstract: We consider one-hidden layer neural networks trained in the feature-learning regime using gradient descent, and relate the output of the finite-width

VRPRM: Process Reward Modeling via Visual Reasoning

arXiv:2508.03556v3 Announce Type: replace Abstract: Process Reward Model (PRM) is widely used in the post-training of Large Language Model (LLM) because it can perform fine-grained evaluation of the r