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Gaussian Process Decorrelation for Spatiotemporal Deep Learning-Based Snow Water Equivalent Prediction

arXiv:2609.22182v1 Announce Type: new Abstract: In the Western United States, snowmelt is essential to the agricultural industry in addition to being a key source of municipal drinking water. Conseque

safetyarxiv-cs-lg

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GDN Tree-Scan: Served Tree Verification for Recurrent-Hybrid Language Models

DGX agent

arXiv:2609.23900v1 Announce Type: new Abstract: Tree speculative decoding verifies multiple candidate continuations in one target forward pass. For attention-only transformers, the verifier mainly nee

safetyarxiv-cs-lg

22 Sept 2026

GeCCo -- a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots

arXiv:2509.17582v2 Announce Type: replace Abstract: Most modern approaches to quadruped locomotion focus on using Deep Reinforcement Learning (DRL) to learn policies from scratch, in an end-to-end man

General Collaborative Intelligence: Architecting Cognition for Resilient Multi-Agent Ecosystems

arXiv:2609.22967v1 Announce Type: new Abstract: Multi-agent unmanned systems are moving from isolated, ego-centric sensing toward collaborative intelligence, in which distributed agents exchange compa

Generalizable Optimal Control with Transformers: One Policy Across Diverse Systems

model-releasesarxiv-cs-ro

arXiv:2603.14910v2 Announce Type: replace-cross Abstract: Classical optimal control designs a separate controller for each plant. Even for the Linear Quadratic Regulator (LQR), every new model must be

Applications

Generalized Multimodal Foundation Model

arXiv:2609.22107v1 Announce Type: cross Abstract: Making prediction with multimodal data is widely used in diverse scenarios. Existing multimodal fusion models, once deployed, can only handle predefin

applicationsarxiv-cs-cl

Generating Chest X-Ray Counterfactuals by Specialising Foundation Image Models

arXiv:2609.24879v1 Announce Type: new Abstract: Counterfactual image generation answers questions about how a subject would have looked under retrospective, hypothetical scenarios. Recent methods have

GenVoid: Uncertainty-Aware Learning of Subsurface Material Defects with an Experimentally Validated Physics-Informed Generative Model

arXiv:2609.23761v1 Announce Type: new Abstract: Internal voids are ubiquitous defects in manufactured structures, yet their characterization remains challenging because their geometry is hidden and ca

GeoBalance: Geometry-Aware Monitoring and Reconstruction with Asymmetric Optimization for Balanced Multimodal Learning

arXiv:2609.23533v1 Announce Type: new Abstract: Multimodal classifiers can converge to modality-dominant solutions in which one modality dominates the joint prediction, suppressing the learning of oth

Glass-Box Deep Learning for FDIA Detection in Nonlinear Automatic Generation Control: A Kolmogorov-Arnold Network Approach

model-releasesarxiv-cs-lg

arXiv:2509.05259v2 Announce Type: replace Abstract: Automatic Generation Control (AGC) plays a critical role in maintaining power balance across multi-area power systems. However, its complete relianc

Global Ranks Survive, Selected Heads Shift: BOS-Sink Topology under 4-bit Weight-Only Quantization

model-releasesarxiv-cs-cl

arXiv:2609.23585v1 Announce Type: cross Abstract: Sink-aware deployment may identify important first-token attention heads before a model is quantized, then reuse that map at the edge. We test when th

GLR-MM: Graph-Based Global-Local Reconstruction for Robust Multimodal Chest X-ray and EHR Representation Learning under Missing Modalities

arXiv:2609.23876v1 Announce Type: new Abstract: Clinical multimodal models must often predict before all chest X-ray (CXR) and electronic health record (EHR) inputs are available. Existing approaches

GNN-based Path-aware multi-view Circuit Learning for Technology Mapping

arXiv:2601.14286v2 Announce Type: replace-cross Abstract: Traditional technology mapping suffers from systemic inaccuracies in delay estimation due to its reliance on abstract, technology-agnostic del

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers

arXiv:2506.11784v2 Announce Type: replace Abstract: Vision Transformers (ViTs) are essential in computer vision but are computationally intensive, too. Model quantization, particularly to low bit-widt

GRACE: Grounded Adversarial Reasoning over Canadian Law

arXiv:2609.23726v1 Announce Type: new Abstract: Large language models have shown strong performance across a range of legal tasks, but existing benchmarks rarely evaluate the ability to take and defen

safetyarxiv-cs-cl

Graded-Relevance Composed Multimodal Retrieval for E-commerce Visual Search at Scale

arXiv:2609.24152v1 Announce Type: cross Abstract: Visual search on large e-commerce catalogs must serve both 'similarity' queries that ask for items resembling an uploaded image and 'modifier' queries

Hardware

Gradient-estimator design overcomes trainability barriers in neural-network-based variational optimization

arXiv:2609.22342v1 Announce Type: cross Abstract: Neural networks provide expressive representations for scientific computing. However, even sufficiently expressive networks can suffer training failur

hardwarearxiv-cs-lg

GrapeSplat: Geometry-Grounded Reconstruction via Amalgamated Pose-Free Encoding for Feed-Forward 3D Gaussian Splatting

arXiv:2609.23182v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting now reconstructs renderable scenes from unposed, uncalibrated images. Yet, most models supervise only photometric con

Graph Learning for Cross-Subject, Cross-Population EEG Emotion Decoding and Model-Derived Spatial-Spectral Neural Signatures

arXiv:2609.22103v1 Announce Type: cross Abstract: Electroencephalography (EEG) provides a noninvasive means of capturing emotion-related neural dynamics, yet reliable EEG emotion decoding lacks models

Graph-to-Grid (G2G): Continuous-Coordinate Feature Painting for Soccer Pass Surfaces

arXiv:2609.24040v1 Announce Type: cross Abstract: Dense pass surfaces give, for every pitch cell, whether a pass played there would arrive, whether the carrier would choose it, and what the possession

GraphSVR: q-Space--Aware Graph-Based Slice-to-Volume Registration for Diffusion MRI

arXiv:2609.24732v1 Announce Type: new Abstract: Diffusion-weighted imaging (DWI) remains highly vulnerable to subject motion, particularly in time-efficient protocols and in motion-prone populations.

GraphToolbox: A Configurable Python Framework for Graph Neural Network Forecasting

arXiv:2609.24609v1 Announce Type: new Abstract: Electricity forecasting often involves spatially related signals observed over regions, substations, and feeders, and Graph Neural Networks (GNNs) provi

GraspTune: Tactile-Driven Execution Refinement for Robust Grasping

arXiv:2609.24180v1 Announce Type: new Abstract: Visual grasp proposal generation has advanced rapidly, yet converting a selected proposal into a stable physical grasp remains a central execution-stage

Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs

model-releasesarxiv-cs-cl

arXiv:2511.04473v3 Announce Type: replace-cross Abstract: Retrieval of information from graph-structured knowledge bases represents a promising direction for improving the factuality of LLMs. While va

Grounded Action Model: 3D Grounding as a Foundation for Robotics

arXiv:2609.23863v1 Announce Type: new Abstract: Manipulation policies must know which objects matter and where they are, yet the pretrained backbones that current robot foundation models build on, fro

GRRR: The Geometry of Reshaping, Rotation, and Routing in Decoder LLM post-training

arXiv:2609.22146v1 Announce Type: cross Abstract: We study how post-training changes the weights of Large Language Models (LLMs) relative to their pretrained weights. Across 12 post-training chains wi

researcharxiv-cs-cl

Guaranteed Low-Rank Tensor Recovery from Modewise Measurements via Normalized Block-Weighted Riemannian Gradient Descent

arXiv:2609.24679v1 Announce Type: new Abstract: We consider the recovery of low-multilinear-rank tensors from linear measurements and propose an adaptive block-weighted modewise Riemannian gradient de

Guiding the coarse levels of semantic IDs makes the fine levels learnable

arXiv:2609.22227v1 Announce Type: cross Abstract: Generative retrieval represents each item by a short Semantic ID and casts recommendation as autoregressive generation of that sequence. Because the t

safetyarxiv-cs-cl

H-Spec: Parallel Speculative Decoding Without a Drafter-Side KV Cache

arXiv:2609.24197v1 Announce Type: new Abstract: Speculative decoding losslessly accelerates large language model inference by having a lightweight draft model predict future tokens for verification by

H-VLA: Hierarchical Vision-Language-Action Model with Key-Action Reasoning and Motion Planning in a Unified Action Space

arXiv:2609.22895v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown strong potential for robotic manipulation, but many existing methods still rely on direct mappings from l

researcharxiv-cs-ro

H2LooP Telecom Model v1: From Telecom Comprehension to Autonomous Issue and PR Resolution

model-releasesarxiv-cs-cl

arXiv:2609.22241v1 Announce Type: new Abstract: We present H2LooP Telecom Model v1, a domain-specialized large language models fine-tuned for the telecommunications industry. We release two domain-ada

H2RBench: A Real-to-Sim Benchmark for Evaluating Human-to-Robot Transfer

model-releasesarxiv-cs-ro

arXiv:2609.24778v1 Announce Type: new Abstract: Learning robot manipulation policies from human video demonstrations constitutes a promising avenue for scalable robot learning. However, comparing diff

HaikuS2S: A Cascaded System For Responding In Verse

arXiv:2609.23951v1 Announce Type: new Abstract: Expressive speech synthesis has advanced through prosody modeling, yet generating structured poetic speech, such as haiku, remains challenging. Prior wo

safetyarxiv-cs-cl

HappyWorld-Bench

arXiv:2609.24308v1 Announce Type: new Abstract: Evaluating world models requires assessing both the quality of the worlds they generate and their consistency and responsiveness under exploration, inte

HapticWAM: Distilling Imagined Touch into a World-Action Model without Inference-Time Tactile Sensing

arXiv:2609.23888v1 Announce Type: new Abstract: Contact-rich manipulation requires estimating forces, slip and contact geometry that can remain ambiguous in scene images. Optical tactile sensors provi

Agents

Harness-Zero: Harness Distillation via Agent-as-Harness

arXiv:2609.24974v1 Announce Type: cross Abstract: Agent harnesses, the external systems that mediate model-environment interaction, can substantially improve agent performance, but their gains remain

agentsarxiv-cs-cl

Local Ai

HDMamba-YOLO: Efficient State-Space Perception and Local Spatial Reconstruction for UAV Small Object

arXiv:2609.23061v1 Announce Type: new Abstract: Small-object detection in UAV imagery is challenged by weak visual evidence, ambiguous boundaries, dense object distributions, and complex backgrounds.

local-aiarxiv-cs-cv

HDND: Hierarchical Dynamic Neural Decoding for Multilingual Word/Character Retrieval from Non-Invasive Brain Recordings

arXiv:2609.24095v1 Announce Type: new Abstract: While deep learning has enabled language decoding from intracranial brain recordings, extending this capability to non-invasive recordings remains an un

HEARTH: An Object-Centric RGB-Thermal-3D Dataset for Temperature-Aware Robot Manipulation

arXiv:2609.23418v1 Announce Type: new Abstract: Language-guided manipulation can depend on physical properties that visible appearance does not reveal. Temperature is one such property, but object dat

researcharxiv-cs-ro

Helix-FNO: Spectral-Domain Operator Learning Coupled with a High-Fidelity Mechanistic Model for Fast Surrogate Simulation

arXiv:2609.22129v1 Announce Type: new Abstract: Mechanistic simulation models of full-scale treatment processes remain the only trustworthy, extrapolative description of the underlying physico-chemica

Applications

Hessian Rank Constraint for Learning Structure of Nonlinear Latent Variable Models

arXiv:2609.24241v1 Announce Type: new Abstract: Uncovering latent variables and their causal relations from observed data is a fundamental yet challenging problem. Existing methods often rely on restr

applicationsarxiv-cs-lg

Applications

Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence

arXiv:2510.26915v2 Announce Type: replace Abstract: While heterogeneous teams have typically been designed for well-specified missions with known semantics, generative intelligence, i.e., large langua

applicationsarxiv-cs-ro

HFEMCNet: A Compact Hybrid Frequency Enriched Multi Channel Network for Automatic Modulation Classification

model-releasesarxiv-cs-lg

arXiv:2609.22139v1 Announce Type: cross Abstract: Automatic modulation classification (AMC) of received radio signals is prudent for further signal processing tasks such as communication monitoring, c

Hi-Singers: A Comprehensive High-Quality Dataset for Expressive Audio-Driven Singing Head Synthesis

arXiv:2609.22264v1 Announce Type: cross Abstract: State-of-the-art models for audio-driven digital human generation have achieved photo-realistic results in talking-head synthesis. However, extending

Agents

HiBerNAC: Hierarchical Brain-inspired Robotic Neural Agent Collective for Disentangling Complex Manipulation

arXiv:2506.08296v3 Announce Type: replace Abstract: Recent advances in multimodal vision-language-action (VLA) models have revolutionized traditional robot learning, enabling systems to interpret visi

agentsarxiv-cs-ro

Hierarchical Aggregation of Semantic Uncertainty in 3D Scene Graphs

arXiv:2609.22351v1 Announce Type: new Abstract: Open-vocabulary 3D Scene Graphs (3DSGs) ground each object node in a vision-language embedding, yet they record every entry as equally certain, so a rob

Agents

Hierarchical Bayesian optimization of an aircraft-based multi-agent system-of-systems

arXiv:2609.22130v1 Announce Type: new Abstract: Developing innovative system architectures increasingly relies on advanced modeling and optimization techniques to frame the architecting process and de

agentsarxiv-cs-lg

Hierarchical Prompt Learning for Hyperbolic Vision-Language Models

arXiv:2609.24276v1 Announce Type: new Abstract: Hyperbolic vision-language models (VLMs) represent image and text features in a geometry naturally suited to hierarchy, but their adaptation to downstre