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GR-LIO: A Local Ground-Aware LiDAR-Inertial Odometry System Using Body-to-Ground Height

model-releasesarxiv-cs-ro

arXiv:2610.05546v1 Announce Type: new Abstract: LiDAR-inertial odometry (LIO) is widely used for state estimation in ground-based autonomous mobile robots. However, the geometric constraints provided

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Research

Grad-CAM for Visualizing Attention Regions of PCA and SVM Layers in Convolutional Neural Networks

DGX agent

arXiv:2508.11880v2 Announce Type: replace Abstract: Convolutional Neural Networks (CNNs) are an effective approach for classification tasks, particularly when the training dataset is large. Although C

researcharxiv-cs-lg

6 Oct 2026

Gradient descent dynamics for deep equilibrium models

arXiv:2511.16976v3 Announce Type: replace Abstract: Deep equilibrium models (DEQs) have recently emerged as a powerful paradigm for training infinitely deep weight-tied neural networks that achieve st

Gradient-Free Sampling from Generative Models via Stochastic Bounded Extremum Seeking

arXiv:2610.04568v1 Announce Type: cross Abstract: We introduce a sampling approach for energy- and score-based generative models that requires no gradient evaluations of the model. Replacing the drift

GRAM: Correcting Frozen Time-Series Foundation Models via Graph-Retrieved Amplitude Memory

arXiv:2610.04827v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) enable zero-shot forecasting through large-scale cross-domain pretraining, while retrieval augmentation further im

Graph-Based Floor Separation Using Node Embeddings and Clustering of WiFi Trajectories

arXiv:2505.08088v4 Announce Type: replace-cross Abstract: Vertical localization, particularly floor separation, remains a major challenge in indoor positioning systems operating in GPS-denied multisto

Applications

Graph Data Augmentation via Contrastive Generator Inversion (exttt{DCBA})

arXiv:2610.05653v1 Announce Type: new Abstract: Graphs provide a natural representation of many complex systems, ranging from social platforms to ecosystems. However, the development of graph-based ma

applicationsarxiv-cs-lg

Hardware

Great report from Google and MIT. Co-designing ML models and the accelerators is an exciting research direction. This report introduces Coco…

Great report from Google and MIT. Co-designing ML models and the accelerators is an exciting research direction. This report introduces Coco, an agent platform TPU architects use for hardware and mode

hardwaredair-ai--x

Local Ai

Greedy Local Learning for Language Model Pretraining: Gaps and Objective Design

arXiv:2610.04867v1 Announce Type: new Abstract: Greedy block-wise local learning splits a network into gradient-isolated blocks trained by local auxiliary losses, deleting the backward pass between bl

local-aiarxiv-cs-lg

Greedy Multi-Path Block Verification for Faster Decoding in Speculative Sampling

arXiv:2602.16961v2 Announce Type: replace-cross Abstract: The goal of L-step speculative decoding is to accelerate autoregressive decoding of a target model by using a cheaper draft model to generate

Green-Routed Neural Operators: Physics Determines Where the Network Reads

arXiv:2610.05337v1 Announce Type: new Abstract: We identify a mismatch between the physical role of transport fields in many PDEs and their usual role in neural operators: PDEs use them to select read

Industry

Grok 4.7 is excellent at dealing with large code repositories

Grok 4.7 is excellent at dealing with large code repositories Grok 4.7 takes the TOP 3 spots on VulcanBench Frontier v4, outperforming Fable 5.1, Opus 5.5, GPT-6 Astra, GPT-6.1 Sol and other leading f

industryelon-musk--x

Grounded in Time: A Multi-Source Dataset and Benchmark for Temporal Grounding in Robotic Manipulation

model-releasesarxiv-cs-cv

arXiv:2610.04255v1 Announce Type: cross Abstract: Robotic manipulation often requires inferring task-relevant states from past interactions when the current observation alone is insufficient to determ

Groupwise Distortion Guarantees for Preference-Based Alignment

arXiv:2610.05450v1 Announce Type: new Abstract: Preference-based alignment methods such as reinforcement learning from human feedback (RLHF) and Nash learning from human feedback (NLHF) aggregate pair

GS-Codec: A Gaussian-Splatting Bottleneck for Neural Audio Coding

arXiv:2610.04651v1 Announce Type: cross Abstract: Neural audio codecs compress waveforms into compact discrete tokens that underpin speech language models, real-time communication, and large-scale aud

GS-Pool: Object-Level Change Detection in 3D Gaussian Splatting

arXiv:2610.06688v1 Announce Type: new Abstract: Factories, museums and surveyors photograph the same space months apart and need to know which objects changed. When each visit is reconstructed with 3D

GUIDE: Reinforcement Learning for Behavioral Action Support in Type 1 Diabetes

arXiv:2604.00385v2 Announce Type: replace Abstract: Type 1 diabetes (T1D) management requires continuous adjustment of insulin and lifestyle behaviors to maintain blood glucose within a safe target ra

H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Planning

arXiv:2610.06805v1 Announce Type: new Abstract: Long-horizon planning with latent world models requires reasoning across timescales and levels of abstraction. Existing task-agnostic JEPA world models

Hack The Box’s AI Range Enterprise Edition tests whether AI agents can do their jobs

Cybersecurity training company Hack The Box Ltd. today introduced AI Range Enterprise Edition to let security teams test whether their own artificial intelligence agents can do the jobs they have been

agentssiliconangle

Hadrian, an agentic AI offensive security service, raised 40M co-led by Forgepoint Capital International and SmartFin, taking its total funding to 65M (Tamara Djurickovic/Tech.eu)

Tamara Djurickovic / Tech.eu: Hadrian, an agentic AI offensive security service, raised 40M co-led by Forgepoint Capital International and SmartFin, taking its total funding to 65M — Hadrian will use

agentstechmeme

Hadrian raises $40M for AI agents that find the weaknesses attackers can exploit

Dutch offensive security startup Hadrian Security B.V. today announced that it has raised 40 million in new funding to expand its business internationally. Hadrian’s software points artificial intelli

agentssiliconangle

Hamiltonian Metric Learning and Energy-Based Training: A Dissipative Geometric Framework for Optimization

arXiv:2610.04969v1 Announce Type: new Abstract: Optimization in machine learning is usually expressed through iterative rules that update model parameters using information from the loss landscape. In

HardCore Generation: Generating Hard UNSAT Problems for Data Augmentation

arXiv:2409.18778v2 Announce Type: replace Abstract: Efficiently determining the satisfiability of a boolean equation -- known as the SAT problem for brevity -- is crucial in various industrial problem

Harmful Content Generation in Text-to-Image Models: Capabilities and Moderation Limitations

arXiv:2610.06503v1 Announce Type: new Abstract: Text-to-image generative models can produce highly realistic imagery but also raise concerns about harmful misuse. While safety mechanisms exist, system

safetyarxiv-cs-cv

Harnessing Multimodal Large Language Models for Training-Free Human-Object Interaction Detection

model-releasesarxiv-cs-cv

arXiv:2610.06394v1 Announce Type: new Abstract: Human-object interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Traditional supervised methods perform st

Here's a benchmark that uses Lilypond https://x.com/aug5thmusic/status/2096030719156089029

model-releasessimon-willison--x

Here's a benchmark that uses Lilypond https://x.com/aug5thmusic/status/2096030719156089029 GPT-6 Astra has the best result yet on the Bach Benchmark. Its chorale contains no voice-leading errors, and

HeuFouFT: Task-Guided Metaheuristic Coordinate Search for Fourier Fine-Tuning

arXiv:2610.06437v1 Announce Type: new Abstract: We introduce Heuristic-Guided Fourier Fine-Tuning (HeuFouFT), a task-guided framework for selecting trainable frequency coordinates in Fourier fine-tuni

agentsarxiv-cs-lg

Hidden in the Comments: A Context-Injection Attack Surface in Code LLMs

arXiv:2610.05139v1 Announce Type: new Abstract: Code large language model (Code LLM) assistants generate code from heterogeneous development contexts, including open files, imported modules, pasted sn

Tutorials

HiER-BLS: A Hierarchy-Guided and Error-Correcting Robust Incremental Broad Learning System

arXiv:2610.05834v1 Announce Type: new Abstract: Broad Learning System (BLS) supports analytical training and incremental expansion, but its growth needs guidance on which inputs new blocks should lear

tutorialsarxiv-cs-lg

Local Ai

Hierarchical Credit Assignment for RLVR on Fused Gromov-Wasserstein Geometry

arXiv:2610.04344v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has been shown to improve the reasoning capability of large language models (LLMs) across diverse

local-aiarxiv-cs-lg

Hierarchical Reinforcement Learning for Collision-Free Locomotion of an Underactuated Biped

arXiv:2610.05855v1 Announce Type: new Abstract: A bipedal robot cannot deviate from its path to avoid an obstacle without disturbing its balance, and this coupling is most severe on underactuated plat

safetyarxiv-cs-ro

Hierarchical Time-aware Bootstrapping for Off-Policy Subgoal Value Learning

arXiv:2610.05446v1 Announce Type: new Abstract: Off-policy hierarchical reinforcement learning must estimate the values of high-level decisions while the low-level policy changes. HIRO adapts replay d

High dimensional theory of two-phase optimizers

arXiv:2603.26954v2 Announce Type: replace Abstract: The trend towards larger training setups has brought a renewed interest in partially asynchronous two-phase optimizers which optimize locally and th

HLA-WM: Hybrid Linear Attention for Long-Horizon Video World Models

arXiv:2610.05739v1 Announce Type: new Abstract: Long-horizon video world models require persistent memory to preserve scene consistency over extended rollouts. Softmax attention retains the full gener

Homogeneous Semantic Alignment and Hierarchical Expert Routing for Radiology Report Generation

model-releasesarxiv-cs-cv

arXiv:2610.04499v1 Announce Type: new Abstract: Radiology report generation (RRG) aims to convert medical images into diagnostic texts to assist in clinical decision-making and alleviate the workload

Applications

How (and How Not) to Use Data Augmentation in VLA Post-Training

arXiv:2610.05994v1 Announce Type: cross Abstract: Vision-language-action (VLA) models currently demonstrate strong performance in a wide range of real-world robotics tasks. However, they often still l

applicationsarxiv-cs-lg

Hardware

How DOCA GPUNetIO Unifies GPU-Initiated Networking Across the NVIDIA Software Stack

DOCA GPUNetIO delivers a unified GPU‑directed networking (GDA‑KI) foundation that lets CUDA kernels drive Ethernet, RDMA, Verbs and DMA operations without the CPU entering the application path. It shi

hardwarenvidia-developer

How Does Geometry Enter Generated Motion?

arXiv:2610.05135v1 Announce Type: new Abstract: Under a fixed physical law, the visible geometry of a scene determines how motion must change. We ask how video generators realize this relationship. We

How Execution Assumptions Change Short-Horizon Sharpe Rankings: Evidence from a Synthetic Trading Benchmark

arXiv:2610.05077v1 Announce Type: new Abstract: Backtests of LLM trading agents often assume that every order fills at the closing price. We ask whether this choice changes only reported returns or al

How Long, Not How Close: A Learned Temporal Metric for Planning in Latent World Models

arXiv:2610.04988v1 Announce Type: new Abstract: Latent world models plan by rolling a frozen predictor forward under candidate action sequences and ranking the candidates by the latent distance betwee

How RL Reshapes LLM Reasoning: Transferability, Coverage, and Scaling Laws

arXiv:2610.04158v1 Announce Type: new Abstract: Recent studies on reinforcement learning (RL) report seemingly conflicting evidence about large language model (LLM) reasoning. Training on mathematics

How Should Teachers Be Prepared? RL on Student-Induced States for On-Policy Distillation

arXiv:2610.04950v1 Announce Type: new Abstract: On-policy distillation (OPD) improves the reasoning capabilities of small language models through token-level teacher supervision on student-generated t

Tools

How Small Can Google's New EmbeddingGemma 2 Get?

Google’s new EmbeddingGemma 2 can run on a phone. The model travels light. The vectors need their own suitcase. At 10 million documents, full-size float32 vectors alone take 30.7 GB of RAM. We got ear

toolsqdrant

Tutorials

How Sparse Probability Maps Shape Mixture-of-Experts Routing

arXiv:2610.06677v1 Announce Type: new Abstract: Mixture-of-experts (MoE) routers typically apply softmax to the router scores and keep the top-K experts, making every token use exactly K experts. Spar

tutorialsarxiv-cs-lg

Tutorials

How to scale your HEP ML models: A recipe for robust architecture comparisons at scale

arXiv:2610.06784v1 Announce Type: cross Abstract: Much of the recent progress in machine learning domains such as language models has come from scaling laws that predict performance as a function of t

tutorialsarxiv-cs-lg

How well do routinely collected demographic and clinical variables aid point-of-care lung ultrasound TB classification

arXiv:2610.06034v1 Announce Type: cross Abstract: We consider the fusion of lung ultrasound images with routinely-collected clinical and demographic data for the purpose of automated tuberculosis (TB)

https://claude.ai/chat/834491a2-0f6c-44be-9cc9-53211c8b8405

A small thing I noticed: full-size screenshots use far more AI tokens than the model needs, so usage limits run out sooner. I built a free Firefox add-on, TokenSaver, that shrinks images before they a

model-releasesr-ollama

Human Behavior-Informed Crash Scenario Generation with Real-World Crash Priors for Autonomous Vehicle Safety Evaluation

arXiv:2610.04366v1 Announce Type: new Abstract: Reliable safety evaluation of autonomous vehicles (AVs) is essential to improving road safety, yet it depends critically on realistic simulation of rare

safetyarxiv-cs-ro