All entries

Layer-wise Lipschitz-Product Control for Deep Kolmogorov--Arnold Network Representations of Compositionally Structured Functions

arXiv:2604.26444v1 Announce Type: new Abstract: We prove that any continuous function f from [0,1]^n to R representable by a finite computation tree with N internal nodes and compositional sparsity s

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Learning Neural Operator Surrogates for the Black Hole Accretion Code

DGX agent

arXiv:2604.25985v1 Announce Type: cross Abstract: General-relativistic magnetohydrodynamic (GR-MHD) simulations are essential for studying black hole accretion, relativistic jets, and magnetic reconne

model-releasesarxiv-cs-lg

30 Apr 2026

Learning Over-Relaxation Policies for ADMM with Convergence Guarantees

model-releasesarxiv-cs-lg

arXiv:2604.26932v1 Announce Type: cross Abstract: The Alternating Direction Method of Multipliers (ADMM) is a widely used method for structured convex optimization, and its practical performance depen

Tutorials

Learning Sparse BRDF Measurement Samples from Image

arXiv:2604.26740v1 Announce Type: new Abstract: Accurate BRDF acquisition is important for realistic rendering, but dense gonioreflectometer measurements are slow and expensive. We study how to select

tutorialsarxiv-cs-cv

Learning to Ask: When LLM Agents Meet Unclear Instruction

arXiv:2409.00557v4 Announce Type: replace-cross Abstract: Equipped with the capability to call functions, modern large language models (LLMs) can leverage external tools for addressing a range of task

Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use

arXiv:2602.20426v2 Announce Type: replace Abstract: While most efforts to improve LLM-based tool-using agents focus on the agent itself - through larger models, better prompting, or fine-tuning - agen

Learning to Route Electric Trucks Under Operational Uncertainty

arXiv:2604.26566v1 Announce Type: cross Abstract: Electric truck operations require routing decisions that remain feasible under limited battery range, long charging times, travel and energy consumpti

Learning Vision-Based Omnidirectional Navigation: A Teacher-Student Approach Using Monocular Depth Estimation

arXiv:2603.01999v2 Announce Type: replace-cross Abstract: Reliable obstacle avoidance in industrial settings demands 3D scene understanding, but widely used 2D LiDAR sensors perceive only a single hor

safetyarxiv-cs-cv

Lifting Embodied World Models for Planning and Control

arXiv:2604.26182v1 Announce Type: cross Abstract: World models of embodied agents predict future observations conditioned on an action taken by the agent. For complex embodiments, action spaces are hi

Lights Out: A Nighttime UAV Localization Framework Using Thermal Imagery and Semantic 3D Maps

arXiv:2604.26201v1 Announce Type: new Abstract: Reliable backup localization for unmanned aerial vehicles (UAVs) operating in GNSS-denied nighttime conditions remains an open challenge due to the seve

safetyarxiv-cs-ro

Lightweight Quantum Agent for Edge Systems: Joint PQC and NOMA Resource Allocation

arXiv:2604.25980v1 Announce Type: cross Abstract: In the context of quantum secure scenarios, existing research on mobile edge devices and intelligent computing and edge (ICE) systems based on the Non

LIT-RAGBench: Benchmarking Generator Capabilities of Large Language Models in Retrieval-Augmented Generation

arXiv:2603.06198v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a framework in which a Generator, such as a Large Language Model (LLM), produces answers by retrieving docum

LLM-Flax : Generalizable Robotic Task Planning via Neuro-Symbolic Approaches with Large Language Models

model-releasesarxiv-cs-ro

arXiv:2604.26569v1 Announce Type: new Abstract: Deploying a neuro-symbolic task planner on a new domain today requires significant manual effort: a domain expert must author relaxation and complementa

LLM Psychosis: A Theoretical and Diagnostic Framework for Reality-Boundary Failures in Large Language Models

arXiv:2604.25934v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) as interactive agents has exposed a category of behavioral failure that prevailing terminology, princip

LLMs Generate Kitsch

arXiv:2604.25929v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to generate pictures, texts, music, videos, and other works that have traditionally required human cr

researcharxiv-cs-cl

LogitSpec: Accelerating Retrieval-based Speculative Decoding via Next Next Token Speculation

arXiv:2507.01449v3 Announce Type: replace Abstract: Speculative decoding (SD), where a small draft model is employed to propose draft tokens in advance and then the target model validates them in para

researcharxiv-cs-cl

Lunguage: A Benchmark for Structured and Sequential Chest X-ray Interpretation

arXiv:2505.21190v2 Announce Type: replace-cross Abstract: Radiology reports convey detailed clinical observations and capture diagnostic reasoning that evolves over time. However, existing evaluation

Lyapunov-Guided Self-Alignment: Test-Time Adaptation for Offline Safe Reinforcement Learning

arXiv:2604.26516v1 Announce Type: cross Abstract: Offline reinforcement learning (RL) agents often fail when deployed, as the gap between training datasets and real environments leads to unsafe behavi

M2R2: MultiModal Robotic Representation for Temporal Action Segmentation

arXiv:2504.18662v3 Announce Type: replace-cross Abstract: Temporal action segmentation (TAS) has long been a key area of research in both robotics and computer vision. In robotics, algorithms have pri

Mapping the maturation of TCM as an adjuvant to radiotherapy

arXiv:2601.11923v2 Announce Type: replace Abstract: The integration of complementary medicine into oncology represents a paradigm shift that has seen to increasing adoption of Traditional Chinese Medi

safetyarxiv-cs-cl

MappingEvolve: LLM-Driven Code Evolution for Technology Mapping

arXiv:2604.26591v1 Announce Type: cross Abstract: Technology mapping is a critical yet challenging stage in logic synthesis. While Large Language Models (LLMs) have been applied to generate optimizati

MARVIS: Modality Adaptive Reasoning over VISualizations

model-releasesarxiv-cs-lg

arXiv:2507.01544v2 Announce Type: replace Abstract: Predictive applications of machine learning often rely on small (sub 1 Bn parameter) specialized models tuned to particular domains or modalities. S

MATH-PT: A Math Reasoning Benchmark for European and Brazilian Portuguese

arXiv:2604.25926v1 Announce Type: new Abstract: The use of large language models (LLMs) for complex mathematical reasoning is an emergent area of research, with fast progress in methods, models, and b

MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution

arXiv:2604.26283v1 Announce Type: cross Abstract: High-precision medical diagnosis relies not only on static imaging features but also on the implicit diagnostic memory experts instantly invoke during

MemOVCD: Training-Free Open-Vocabulary Change Detection via Cross-Temporal Memory Reasoning and Global-Local Adaptive Rectification

arXiv:2604.26774v1 Announce Type: cross Abstract: Open-vocabulary change detection aims to identify semantic changes in bi-temporal remote sensing images without predefined categories. Recent methods

Hardware

MesonGS++: Post-training Compression of 3D Gaussian Splatting with Hyperparameter Searching

arXiv:2604.26799v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) achieves high-quality novel view synthesis with real-time rendering, but its storage cost remains prohibitive for practical

hardwarearxiv-cs-cv

Meta-Learning and Targeted Differential Privacy to Improve the Accuracy-Privacy Trade-off in Recommendations

arXiv:2604.26390v1 Announce Type: cross Abstract: Balancing differential privacy (DP) with recommendation accuracy is a key challenge in privacy-preserving recommender systems, since DP-noise degrades

Tutorials

MetaSR: Content-Adaptive Metadata Orchestration for Generative Super-Resolution

arXiv:2604.26244v1 Announce Type: cross Abstract: We study generative super-resolution (SR) in real-world scenarios where content and degradations vary across domains, genres, and segments. For exampl

tutorialsarxiv-cs-ai

Mini-Batch Class Composition Bias in Link Prediction

arXiv:2604.25978v1 Announce Type: cross Abstract: Prior work on node classification has shown that Graph Neural Networks (GNNs) can learn representations that transfer across graphs, when underlying g

Mining Negative Sequential Patterns to Improve Viral Genomic Feature Representation and Classification

arXiv:2604.25968v1 Announce Type: cross Abstract: Viruses represent the most abundant biological entities on Earth and play a pivotal role in microbial ecosystems, yet, as prominent human pathogens, t

MINOS: A Multimodal Evaluation Model for Bidirectional Generation Between Image and Text

arXiv:2506.02494v2 Announce Type: replace-cross Abstract: Evaluation is important for multimodal generation tasks, while traditional multimodal evaluation metrics suffer from several limitations. With

MixerCA: An Efficient and Accurate Model for High-Performance Hyperspectral Image Classification

model-releasesarxiv-cs-cv

arXiv:2604.26138v1 Announce Type: new Abstract: Over the past decade, hyperspectral image (HSI) classification has drawn considerable interest due to HSIs' ability to effectively distinguish terrestri

Tutorials

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations

arXiv:2604.26143v1 Announce Type: cross Abstract: First-principles atomistic simulations are essential for understanding complex material phenomena but are fundamentally limited by their computational

tutorialsarxiv-cs-lg

Momentum-Conserving Graph Neural Networks for Deformable Objects

arXiv:2604.26097v1 Announce Type: cross Abstract: Graph neural networks (GNNs) have emerged as a versatile and efficient option for modeling the dynamic behavior of deformable materials. While GNNs ge

MoRFI: Monotonic Sparse Autoencoder Feature Identification

arXiv:2604.26866v1 Announce Type: new Abstract: Large language models (LLMs) acquire most of their factual knowledge during the pre-training stage, through next token prediction. Subsequent stages of

Motion-Driven Multi-Object Tracking of Model Organisms in Space Science Experiments

arXiv:2604.26321v1 Announce Type: new Abstract: Automated animal behavior analysis relies on long-term, interpretable individual trajectories; however, multi-animal tracking in space science experimen

researcharxiv-cs-cv

MTCurv: Deep learning for direct microtubule curvature mapping in noisy fluorescence microscopy images

arXiv:2604.26517v1 Announce Type: new Abstract: Accurate quantification of the geometry of curvilinear biological structures is essential for understanding cellular mechanics and disease-related morph

researcharxiv-cs-cv

Multi-Periodogram Velocity Estimation with Irregular Reference Signals for Robot-Aided ISAC

arXiv:2604.25974v1 Announce Type: new Abstract: This paper addresses velocity estimation within robot-aided integrated sensing and communications (ISAC), where mobile robots act as sensing nodes but c

researcharxiv-cs-ro

Multi-Stage Bi-Atrial Segmentation Framework from 3D Late Gadolinium-Enhanced MRI using V-Net Family Models

arXiv:2604.26251v1 Announce Type: cross Abstract: We report our multi-stage framework designed for the problem of multi-class bi-atrial segmentation from 3D late gadolinium-enhanced (LGE) MRI of the h

Multimodal LLMs are not all you need for Pediatric Speech Language Pathology

arXiv:2604.26568v1 Announce Type: new Abstract: Speech Sound Disorders (SSD) affect roughly five percent of children, yet speech-language pathologists face severe staffing shortages and unmanageable c

Multiple Additive Neural Networks for Structured and Unstructured Data

arXiv:2604.26888v1 Announce Type: new Abstract: This paper extends and explains the Multiple Additive Neural Networks (MANN) methodology, an enhancement to the traditional Gradient Boosting framework,

Local Ai

Multiple Consistent 2D-3D Mappings for Robust Zero-Shot 3D Visual Grounding

arXiv:2604.26261v1 Announce Type: new Abstract: Zero-shot 3D Visual Grounding (3DVG) is a critical capability for open-world embodied AI. However, existing methods are fundamentally bottlenecked by th

local-aiarxiv-cs-cv

Naamah: A Large Scale Synthetic Sanskrit NER Corpus via DBpedia Seeding and LLM Generation

arXiv:2604.26456v1 Announce Type: cross Abstract: The digitisation of classical Sanskrit literature is impeded by a scarcity of annotated resources, particularly for Named Entity Recognition. While re

Near-Optimal Cryptographic Hardness of Learning With Homogeneous Halfspaces Under Gaussian Marginals

arXiv:2604.26446v1 Announce Type: new Abstract: We study three problems that involve identifying homogeneous halfspaces under Gaussian distributions: agnostic learning, one-sided reliable learning, an

safetyarxiv-cs-lg

Networks of Causal Abstractions: A Sheaf-theoretic Framework

arXiv:2509.25236v3 Announce Type: replace Abstract: A core challenge in causal artificial intelligence is the principled coordination of multiple, imperfect, and subjective causal perspectives arising

Neural-Geometric Tunnel Traversal: Localization-free UAV Flight with Tilted LiDARs

arXiv:2404.09688v2 Announce Type: replace Abstract: Navigation of UAVs in challenging environments like tunnels or mines, where it is not possible to use GNSS methods to self-localize, illumination ma

researcharxiv-cs-ro

NeuralEmu: in situ Measurement-Driven, ML-based, High-Fidelity 5G Network Emulation

arXiv:2604.26080v1 Announce Type: cross Abstract: Current and future applications demand ultra-low latency and consistent throughput, yet frequently traverse 5G cellular networks, so cope with volatil

NeuralFLoC: Neural Flow-Based Joint Registration and Clustering of Functional Data

arXiv:2602.03169v2 Announce Type: replace-cross Abstract: Clustering functional data in the presence of phase variation is challenging, as temporal misalignment can obscure intrinsic shape differences