All entries

Generating Graph-like Rules for Knowledge Graph Reasoning via Diffusion Models

arXiv:2605.30747v1 Announce Type: new Abstract: Logical rules constitute a cornerstone of knowledge graph (KG) reasoning, valued for their interpretability and ability to model relational patterns. Ho

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Generating Reports or Repeating Templates? Measuring and Mitigating Template Collapse in 3D CT Report Generation

DGX agent

arXiv:2605.30984v1 Announce Type: cross Abstract: Modern 3D medical vision-language models (VLMs) can generate fluent radiology-style text while exhibit critically low pathology detection and output d

model-releasesarxiv-cs-ai

1 Jun 2026

Generative Drifting is Secretly Score Matching: a Spectral and Variational Perspective

arXiv:2603.09936v2 Announce Type: replace Abstract: Generative Modeling via Drifting~itep{deng2026drifting} has recently achieved state-of-the-art one-step image generation through a kernel-based drif

tutorialsarxiv-cs-lg

Generative Models and Statistical Validation

arXiv:2605.30453v1 Announce Type: cross Abstract: Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and den

Generative Quantum Data Embeddings for Supervised Learning

arXiv:2605.30866v1 Announce Type: cross Abstract: Many practically relevant applications of quantum machine learning involve classical data, for which performance depends critically on how inputs are

tutorialsarxiv-cs-lg

Geometry-Aware Control Barrier Functions for Collision Avoidance via Bernstein Polynomial Approximations

arXiv:2605.30696v1 Announce Type: new Abstract: Safe navigation often relies on well-defined conditions based on the shape of robots and obstacles, and can be challenging when they have irregular geom

safetyarxiv-cs-ro

Applications

Geometry-based Schrodinger Bridges for Trustworthy Multimodal Fusion

arXiv:2605.31193v1 Announce Type: new Abstract: Real-world multimodal systems must be robust against low-quality data, such as sensor noise, incomplete multimodal data and conflicting inputs. However,

applicationsarxiv-cs-lg

GETA: Generalized Encrypted Traffic Analysis

arXiv:2605.31277v1 Announce Type: cross Abstract: Traditional traffic analysis is being fundamentally challenged by the rapid adoption of encryption, tunnelling, and privacy-preserving protocols, whic

Applications

GGT-100K: Generative Ground Truth for Generalizable Real-World Image Restoration

arXiv:2605.31039v1 Announce Type: new Abstract: Real-world image restoration (IR) is bottlenecked by the scarcity of high-quality paired training data. Synthetic datasets are abundant but often fail t

applicationsarxiv-cs-cv

Giving Sensors a Voice: Multimodal JEPA for Semantic Time-Series Embeddings

arXiv:2605.31580v1 Announce Type: new Abstract: Transformer-based architectures have advanced sequence modeling in language and vision, yet general-purpose representation learning for heterogeneous mu

Local Ai

Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink

arXiv:2601.19966v2 Announce Type: replace-cross Abstract: We introduce ELECTRAFI, a fast, end-to-end differentiable model for predicting periodic charge densities in crystalline materials. ELECTRAFI c

local-aiarxiv-cs-lg

GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring

arXiv:2605.30865v1 Announce Type: new Abstract: Continuous glucose monitoring (CGM) provides a dense view of daily metabolic physiology, yet existing generic time-series and CGM-specific foundation mo

safetyarxiv-cs-lg

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go

model-releasesarxiv-cs-lg

arXiv:2511.10868v2 Announce Type: replace Abstract: Training data imbalance poses a major challenge for code LLMs. Most available data heavily over represents raw opensource code while underrepresenti

Goldfish: Monolingual Language Models for 350 Languages

model-releasesarxiv-cs-cl

arXiv:2408.10441v3 Announce Type: replace Abstract: For many low-resource languages, the only available language models are large multilingual models trained on many languages simultaneously. Despite

Local Ai

GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization

arXiv:2605.31464v1 Announce Type: cross Abstract: GPU kernels are the workhorse of modern deep learning, and optimizing them (via evolutionary search or coding agents) usually requires repeated measur

local-aiarxiv-cs-ai

Gradient-Free Training of Spiking Neural Networks via Low-Rank Evolution Strategies

arXiv:2605.30361v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) offer compelling energy efficiency on neuromorphic hardware, yet their training remains challenging because the discret

researcharxiv-cs-ai

GradMem: Learning to Write Context into Memory with Test-Time Gradient Descent

arXiv:2603.13875v2 Announce Type: replace Abstract: Many large language model applications require conditioning on long contexts. Transformers typically support this by storing a large per-layer KV-ca

researcharxiv-cs-cl

Graph-Conditioned Mixture of Graph Neural Network Experts for Traffic Forecasting

arXiv:2605.30486v1 Announce Type: cross Abstract: Spatio-temporal forecasting on sensor graphs is commonly tackled with a single backbone architecture applied uniformly across all nodes, although grap

Local Ai

Graph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation

arXiv:2603.23398v2 Announce Type: replace-cross Abstract: Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and

local-aiarxiv-cs-ai

Graph Machine Learning in the Era of Large Language Models (LLMs)

arXiv:2404.14928v3 Announce Type: replace-cross Abstract: Graphs play an important role in representing complex relationships in various domains like social networks, knowledge graphs, and molecular d

researcharxiv-cs-ai

Graph Neural Networks Are Not Continuous Across Graph Resolutions

arXiv:2605.31315v1 Announce Type: new Abstract: We show that contrary to conventional wisdom in the community, graph neural networks (GNNs) are not continuous with respect to all natural modes of grap

GraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning

arXiv:2605.31031v1 Announce Type: new Abstract: Relational reasoning lies at the heart of intelligence, but existing benchmarks are typically confined to formats such as grids or text. We introduce Gr

Graphical einops: bridging tensor networks and computation graphs

arXiv:2605.31485v1 Announce Type: new Abstract: Architecture diagrams are ubiquitous in deep learning, but they are usually only representational: the tensor-program identities they suggest are still

GRKV: Global Regression for Training-Free KV Cache Compression in Long-Context LLMs

arXiv:2605.31105v1 Announce Type: new Abstract: Large language models (LLMs) with extended context lengths rely on the key-value (KV) cache to support attention over prior tokens. However, maintaining

researcharxiv-cs-cl

Group Entropies and Mirror Duality: A Class of Flexible Mirror Descent Updates for Machine Learning

arXiv:2603.08651v2 Announce Type: replace Abstract: We introduce a comprehensive theoretical and algorithmic framework that bridges formal group theory and group entropies with modern machine learning

GSAM: A Generalizable and Safe Robotic Framework for Articulated Object Manipulation

arXiv:2605.30740v1 Announce Type: cross Abstract: Articulated object manipulation is a unique challenge for service robots. Existing methods employ end-to-end policy learning, visionmotion planning, a

GUI-C^2: Coarse-to-Fine GUI Grounding via Difficulty-Aware Reinforcement Learning

model-releasesarxiv-cs-cv

arXiv:2605.30884v1 Announce Type: new Abstract: Existing agentic reinforcement learning methods for GUI grounding have limitations at two levels. At the data level, current approaches typically treat

Guidance for Low-Level Perceptual Editing in Unconditional Diffusion Models

arXiv:2605.31162v1 Announce Type: new Abstract: Unconditional diffusion models offer powerful generative priors, yet steering them toward aesthetically enhanced outputs remains largely unexplored. We

tutorialsarxiv-cs-cv

Agents

HADT: A Heterogeneous Multi-Agent Differential Transformer for Autonomous Earth Observation Satellite Cluster

arXiv:2605.31023v1 Announce Type: new Abstract: This work addresses the problem of autonomous resource management in heterogeneous satellite cluster conducting Earth Observation (EO) missions includin

agentsarxiv-cs-ai

Hamiltonian-Inspired Attention Mechanism for Scalable RF Transmitter Fingerprinting

arXiv:2605.30364v1 Announce Type: cross Abstract: Radio-frequency (RF) fingerprinting identifies wire-less transmitters using hardware-induced imperfections present in baseband I/Q signals. However, d

Haptic Sorter: A Unified Planning Framework for Online Shape Estimation and Real-Time Pose Inference

arXiv:2605.31352v1 Announce Type: new Abstract: Robotics manipulation usually assumes that the shape and pose of the object are known to the robot prior to motion planning. However, precise geometric

tutorialsarxiv-cs-ro

Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents

arXiv:2605.30621v1 Announce Type: new Abstract: LLM agents are increasingly deployed as systems built around editable external harnesses, including prompts, skills, memories and tools, that shape task

HARP-VLA: Human-Robot Aligned Representation Learning for Vision-Language-Action Model

arXiv:2605.31234v1 Announce Type: new Abstract: Learning generalizable vision-language-action (VLA) models from large-scale human videos is promising but challenging due to cross-embodiment discrepanc

safetyarxiv-cs-ro

Healthcare Mechanisms from Policy-as-Code Search under Strategic Provider Response

arXiv:2605.30680v1 Announce Type: new Abstract: Healthcare mechanisms are inseparable from the strategic provider response they induce: existing healthcare AI benchmarks hold this response fixed and s

Hedging on the Frontier: Learning New Tasks with Few Samples

model-releasesarxiv-cs-lg

arXiv:2605.30997v1 Announce Type: cross Abstract: When a learner faces a new task with few samples, it must leverage any available side information. In practice, this often comes in the form of model

HERMES: Towards Efficient and Verifiable Mathematical Reasoning in LLMs

arXiv:2511.18760v2 Announce Type: replace Abstract: Informal mathematics has been central to modern large language model (LLM) reasoning, offering flexibility and efficient construction of arguments.

HetCCL: Enabling Collective Communication For Mixed-Vendor Heterogeneous Clusters

arXiv:2605.31000v1 Announce Type: cross Abstract: Training Large Language Models (LLMs) on heterogeneous clusters presents significant challenges for collective communication, as hardware from multipl

Applications

Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring

arXiv:2605.30834v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models enable robots to follow natural language instructions and generalize across diverse tasks, but they remain vulnera

applicationsarxiv-cs-ai

HiERO-StepG @ Ego4D Step Grounding Challenge: hierarchical activity understanding enables zero-shot step grounding

arXiv:2605.31227v1 Announce Type: new Abstract: Procedural activities follow well-defined structures: whether we consider a cooking recipe or a mechanic repairing a car, these activities naturally dec

researcharxiv-cs-cv

High-Load-Density Electro-Permanent Magnetic Foot with Controllable Adhesion for Quadruped Wall-Climbing Robots

arXiv:2605.30849v1 Announce Type: new Abstract: To enable reliable climbing locomotion of quadruped robots on ferromagnetic surfaces, this paper presents a high-load-density electro-permanent magnetic

researcharxiv-cs-ro

HiPER: Hierarchical Reinforcement Learning with Explicit Credit Assignment for Large Language Model Agents

arXiv:2602.16165v2 Announce Type: replace-cross Abstract: Training LLMs as interactive agents for multi-turn decision-making remains challenging, particularly in long-horizon tasks with sparse and del

HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models

arXiv:2602.21340v2 Announce Type: replace Abstract: Representing the past in a compressed, efficient, and informative manner is a central problem for systems trained on sequential data. The HiPPO fram

How can embedding models bind concepts?

arXiv:2605.31503v1 Announce Type: new Abstract: Humans easily determine which color belongs to which shape in multi-object scenes, an ability known as concept binding. Vision-language embedding models

tutorialsarxiv-cs-cv

How Early Adopters Used Generative AI Worldwide: Variation by Country Income and Language

arXiv:2605.30685v1 Announce Type: cross Abstract: AI is being used by people globally, but not everyone is using it in the same ways. Using a large-scale dataset of anonymized, de-identified, and priv

researcharxiv-cs-ai

How Far Can You Grow? Characterizing the Extrapolation Frontier of Graph Generative Models for Materials Science

model-releasesarxiv-cs-lg

arXiv:2602.09309v2 Announce Type: replace-cross Abstract: Every generative model for crystalline materials harbors a critical structure size beyond which its outputs become unreliable; we call this th

How Much Do LLMs Know About Chinese Zero Pronouns?

arXiv:2605.31056v1 Announce Type: new Abstract: Zero Pronouns (ZPs) are a pervasive linguistic phenomenon in pro-drop languages such as Chinese and have long posed a challenge for natural language pro

researcharxiv-cs-cl

How well does Classification Accuracy capture Concept Drift Detection Quality? An overview of Concept Drift Detection evaluation

arXiv:2605.31186v1 Announce Type: new Abstract: Data streams are nowadays among the most frequently analyzed data structures, with the concept drift posing a major challenge encountered by processing

HQ-JEPA: Hybrid Quantum Joint-Embedding Predictive Architecture for Cross-Modal Remote Sensing Representation Learning

arXiv:2605.31068v1 Announce Type: new Abstract: We introduce HQ-JEPA, a hybrid quantum-classical joint-embedding predictive architecture for cross-modal remote sensing representation learning. The pro

safetyarxiv-cs-cv