Search: “papers”

RT-NeRV: Rethinking Hybrid Neural Representations for Video via Residual Tokenization

arXiv:2403.12401v2 Announce Type: replace Abstract: Neural Representations for Videos(NeRV) have emerged as a promising paradigm for video compression by representing videos as compact neural networks

researcharxiv-cs-cv

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Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids

DGX agent

arXiv:2605.23194v1 Announce Type: cross Abstract: Fast and reliable optimal power flow (OPF) approximation is essential for reliable smart-grid operation, yet many learning-based surrogates either fla

researcharxiv-cs-ai

25 May 2026

Agents

Scene Reconstruction as Mapping Priors for 3D Detection

arXiv:2605.22997v1 Announce Type: new Abstract: In autonomous driving, mapping is critical for motion planning but remains an under-utilized resource for perception tasks such as 3D object detection.

agentsarxiv-cs-cv

SciAtlas: A Large-Scale Knowledge Graph for Automated Scientific Research

model-releasesarxiv-cs-ai

arXiv:2605.22878v1 Announce Type: new Abstract: The exponential growth of global academic output has confronted researchers and AI agents with an unprecedented ``information explosion,'' where fragmen

Security of LLM-generated Code: A Comparative Analysis

arXiv:2605.23091v1 Announce Type: cross Abstract: The majority of software developers use or are planning to use Artificial Intelligence (AI) tools in their development processes. Their top reasons in

applicationsarxiv-cs-ai

SEG-JPEG: Simple Visual Semantic Communications for Remote Operation of Automated Vehicles over Unreliable Wireless Networks

arXiv:2602.15258v2 Announce Type: replace Abstract: Remote Operation is touted as being key to the rapid deployment of automated vehicles. Streaming imagery to control connected vehicles remotely curr

applicationsarxiv-cs-ro

SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction

arXiv:2605.23440v1 Announce Type: cross Abstract: Joint Entity and Relation Extraction (JERE) is highly susceptible to weak generalization due to low-quality training data. Data augmentation is a comm

researcharxiv-cs-ai

Steered Generation via Gradient-Based Optimization on Sparse Query Features

arXiv:2605.23040v1 Announce Type: new Abstract: Latent steering exploits internal representations of Large Language Models (LLMs) to guide generation, yet interventions on dense states can entangle di

tutorialsarxiv-cs-lg

Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts

arXiv:2605.23597v1 Announce Type: new Abstract: Matching person names across heterogeneous records is a core challenge in entity resolution, especially within linguistically and culturally complex env

applicationsarxiv-cs-cl

Hardware

The Cognitive Kardashev Scale: Quantifying the Material Envelope of Civilisational Computation

arXiv:2605.22840v1 Announce Type: cross Abstract: How much thinking can a civilisation do? Kardashev's (1964) typology ranks civilisations by total power: planetary (Type I, ~10^16 W), stellar (Type I

hardwarearxiv-cs-ai

The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Management

arXiv:2605.23071v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on long-context processing, but expanding context windows introduces substantial computational and financ

researcharxiv-cs-cl

// The Efficiency Frontier in LLMs // (bookmark this one) How much are you overpaying for context you do not need? It turns out that context…

// The Efficiency Frontier in LLMs // (bookmark this one) How much are you overpaying for context you do not need? It turns out that context costs dominate production LLM bills, and the right strategy

tutorialsdair-ai--x

The TIME Machine: On The Power of Motion for Efficient Perception

arXiv:2605.23045v1 Announce Type: cross Abstract: Video representation learning has seen tremendous progress in recent years. This has been driven by many factors, including the scale of training and

tutorialsarxiv-cs-ai

Understanding and Improving Noisy Embedding Techniques in Instruction Finetuning

model-releasesarxiv-cs-ai

arXiv:2605.23171v1 Announce Type: cross Abstract: Recent advancements in instructional fine-tuning have injected noise into embeddings, with NEFTune (Jain et al., 2024) setting benchmarks using unifor

Safety

UniEmo: Unifying Emotional Understanding and Generation with Learnable Expert Queries

arXiv:2507.23372v2 Announce Type: replace Abstract: Emotional understanding and generation are often treated as separate tasks, yet they are inherently complementary and can mutually enhance each othe

safetyarxiv-cs-cv

Universal Matrix Multiplication on Quantum Computer

arXiv:2408.03085v3 Announce Type: replace-cross Abstract: As the most central and computationally intensive component of deep neural networks, the execution efficiency of matrix multiplication directl

Using Ensemble Diffusion to Estimate Uncertainty for End-to-End Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2506.00560v2 Announce Type: replace-cross Abstract: End-to-end planning systems for autonomous driving are rapidly improving, especially in closed-loop simulation environments like CARLA. Many s

Vector Retrieval with Similarity and Diversity: How Hard Is It?

model-releasesarxiv-cs-cl

arXiv:2407.04573v4 Announce Type: replace-cross Abstract: Dense vector retrieval is an important building block of modern machine learning systems, underlying applications ranging from semantic search

Vision-Based Agile Landing on Turbulent Waters

arXiv:2605.23717v1 Announce Type: new Abstract: Autonomous landing of Unmanned Aerial Vehicles on maritime vessels is challenging due to the coupled motion of the vehicle and landing platform in open-

local-aiarxiv-cs-ro

Agents

X-TRACK: Physics-Aware xLSTM for Realistic Vehicle Trajectory Prediction

arXiv:2511.00266v2 Announce Type: replace Abstract: Accurate trajectory prediction is crucial for safe and reliable autonomous driving systems, requiring models that capture long-term temporal depende

agentsarxiv-cs-lg

XAttnMark: Learning Robust Audio Watermarking with Cross-Attention

model-releasesarxiv-cs-ai

arXiv:2502.04230v3 Announce Type: replace-cross Abstract: The rapid proliferation of generative audio synthesis and editing technologies has raised serious concerns about copyright infringement, data

Hardware

XWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms

arXiv:2605.23348v1 Announce Type: cross Abstract: AI power demand is growing at an unprecedented rate while power grids are often ailing and struggle to keep up. Grid expansion comes with high capital

hardwarearxiv-cs-ai

ZipMoE: Efficient On-Device MoE Serving via Lossless Compression and Cache-Affinity Scheduling

arXiv:2601.21198v2 Announce Type: replace-cross Abstract: While Mixture-of-Experts (MoE) architectures substantially bolster the expressive power of large-language models, their prohibitive memory foo

local-aiarxiv-cs-ai

As more people come to recognize the tells of AI, which mostly happens as you start to work with AI a lot, the scales are going to fall from…

applicationsethan-mollick--x

As more people come to recognize the tells of AI, which mostly happens as you start to work with AI a lot, the scales are going to fall from their eyes and they are going to realize what some of us al

24 May 2026

A Mechanistic Explanatory Strategy for XAI

arXiv:2411.01332v5 Announce Type: replace Abstract: Despite significant advancements in XAI, scholars note a persistent lack of solid conceptual foundations and integration with broader scientific dis

local-aiarxiv-cs-lg

A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting

arXiv:2605.22390v1 Announce Type: new Abstract: Accurate wind power forecasting requires reliable uncertainty quantification, yet most existing methods report a single predictive uncertainty that conf

applicationsarxiv-cs-lg

Hardware

AutoMCU: Feasibility-First MCU Neural Network Customization via LLM-based Multi-Agent Systems

arXiv:2605.21560v1 Announce Type: new Abstract: Deploying neural networks on microcontroller units (MCUs) is critical for edge intelligence but remains challenging due to tight memory, storage, and co

hardwarearxiv-cs-lg

Beyond Single Slot: Joint Optimization for Multi-Slot Guaranteed Display Advertising

arXiv:2605.21556v1 Announce Type: new Abstract: Guaranteed display advertising is crucial for platform monetization, yet existing methods often operate under a single-slot assumption, limiting their a

applicationsarxiv-cs-lg

CacheClip: Accelerating RAG with Effective KV Cache Reuse

arXiv:2510.10129v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems suffer from severe time-to-first-token (TTFT) bottlenecks due to long input sequences. Existing KV cach

local-aiarxiv-cs-lg

Agents

Can frontier models forecast scientific progress? Mostly no, but here is why. This work looks at 4,760 scientific events across disciplines.…

Can frontier models forecast scientific progress? Mostly no, but here is why. This work looks at 4,760 scientific events across disciplines. Frontier models can identify plausible research directions

agentsdair-ai--x

Safety

CCLab: Adversarial Testing of Learning- and Non-Learning-Based Congestion Controllers

arXiv:2605.21915v1 Announce Type: cross Abstract: Congestion controllers (CCs) are critical to network performance, and yet their robustness under adverse conditions remains insufficiently understood.

safetyarxiv-cs-lg

CoFEH: LLM-driven Feature Engineering Empowered by Collaborative Bayesian Hyperparameter Optimization

arXiv:2602.09851v2 Announce Type: replace Abstract: Feature Engineering (FE) is pivotal in automated machine learning (AutoML) but remains a bottleneck for traditional methods, which operate within ri

CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation

arXiv:2605.22774v1 Announce Type: new Abstract: Real-time cognitive load assessment is essential for adaptive human-computer interaction but remains challenging due to limited labeled data and poor cr

Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction

arXiv:2605.21499v1 Announce Type: cross Abstract: Grid-based neural networks such as convolutional autoencoders are widely used in dimension reduction-based surrogate models for computational fluid dy

Cross-Subject EEG Emotion Recognition Based on Temporal Asynchronous Alignment Contrastive Learning

arXiv:2605.22379v1 Announce Type: cross Abstract: With the advancement of science and technology, the importance of emotion research has become increasingly evident. Electroencephalography (EEG)-based

local-aiarxiv-cs-lg

Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary

arXiv:2510.03271v2 Announce Type: replace Abstract: Decision boundary, the subspace of inputs where a machine learning model assigns equal classification probabilities to two classes, is pivotal in re

Departure from Regularity: Degree Heterogeneity and Eigengap as the Structural Drivers of ASE-LSE Latent Subspace Disagreement

arXiv:2605.22346v1 Announce Type: cross Abstract: Two of the most widely used methods for analysing graph data, Adjacency Spectral Embedding and Laplacian Spectral Embedding, often produce different r

Event-Aware Prompt Learning for Dynamic Graphs

arXiv:2510.11339v2 Announce Type: replace Abstract: Real-world graph typically evolve via a series of events, modeling dynamic interactions between objects across various domains. For dynamic graph le

applicationsarxiv-cs-lg

Exploring the Effectiveness of Using LLMs for Automated Assessment of Student Self Explanations in Programming Education

arXiv:2605.21614v1 Announce Type: cross Abstract: Worked examples are step-by-step solutions to problems in a specific domain, offered to students to acquire domain-specific problem-solving skills. Th

tutorialsarxiv-cs-lg

FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation

model-releasesarxiv-cs-lg

arXiv:2505.20349v2 Announce Type: replace-cross Abstract: Data-driven modeling of fluid dynamics has advanced rapidly with neural PDE solvers, yet a fair and strong benchmark remains fragmented due to

Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants

arXiv:2511.02043v4 Announce Type: replace Abstract: Attention is a fundamental building block of large language models (LLMs), so there have been many efforts to implement it efficiently. For example,

Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs

model-releasesarxiv-cs-lg

arXiv:2605.21541v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) remain vulnerable to transfer-based targeted attacks, where perturbations optimized on open-source surrogate

How Sparsity Allocation Shapes Label-Free Post-Pruning Recoverability

arXiv:2605.21972v1 Announce Type: new Abstract: Unstructured magnitude pruning at high sparsity can reduce neural network accuracy to near-random performance, while labeled retraining may be unavailab

Hybrid Kolmogorov-Arnold Network and XGBoost Framework for Week-Ahead Price Forecasting in Australia's National Electricity Market

model-releasesarxiv-cs-lg

arXiv:2605.22387v1 Announce Type: new Abstract: Accurate electricity price forecasting (EPF) is essential for market participants to support operational planning and risk management, yet remains chall

Industry

Image diffusion models like Flux natively output at 1k resolution, but what if we want to generate much higher resolution images (6k+)? SEGA…

Image diffusion models like Flux natively output at 1k resolution, but what if we want to generate much higher resolution images (6k+)? SEGA modifies the RoPE encodings during the diffusion process to

industryemad-mostaque--x

Innovations in Cardless Artificial Intelligence Banking: A Comprehensive Framework for Cyber Secure and Fraud Mitigation using Machine Learning Algorithms

arXiv:2605.22604v1 Announce Type: cross Abstract: The advent of cardless artificial intelligence (AI) banking heralds a paradigm shift in the financial landscape, offering users unprecedented security

Safety

Kernel-Based Safe Exploration in Deep Reinforcement Learning

arXiv:2605.22207v1 Announce Type: cross Abstract: Safety has been a major concern when deploying deep reinforcement learning algorithms in the real world. A promising direction that ensures that the l

safetyarxiv-cs-lg

Large-scale Score-based Variational Posterior Inference for Bayesian Deep Neural Networks

arXiv:2602.05873v2 Announce Type: replace Abstract: Bayesian (deep) neural networks (BNN) are often more attractive than the vanilla point-estimate deep learning in various aspects including uncertain