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Future Validity is the Missing Statistic: From Impossibility to Phi-Estimation for Grammar-Faithful Speculative Decoding

arXiv:2605.07698v1 Announce Type: new Abstract: Grammar-constrained generation is often combined with local vocabulary masking and speculative decoding, but the resulting sampling law is not the gramm

local-aiarxiv-cs-lg

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GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges

DGX agent

arXiv:2605.07133v1 Announce Type: cross Abstract: Graph Anomaly Detection (GAD) is a critical task in graph machine learning with vital applications in financial fraud detection and social platform go

model-releasesarxiv-cs-ai

11 May 2026

GameGen-Verifier: Parallel Keypoint-Based Verification for LLM-Generated Games via Runtime State Injection

arXiv:2605.07442v1 Announce Type: new Abstract: LLM-based game generation promises to turn natural-language specifications into executable games, but progress is limited by the lack of reliable automa

agentsarxiv-cs-lg

gamma-weakly heta-up-concavity: A Unified Framework for Non-Convex Optimization Beyond DR-Submodular and OSS Functions

arXiv:2602.13506v2 Announce Type: replace-cross Abstract: Optimizing non-convex functions is a fundamental challenge across machine learning and combinatorial optimization. We introduce and study gamm

researcharxiv-cs-ai

GASim: A Graph-Accelerated Hybrid Framework for Social Simulation

arXiv:2605.07692v1 Announce Type: new Abstract: Large-scale social simulators are essential for studying complex social patterns. Prior work explores hybrid methods to scale up simulations, combining

safetyarxiv-cs-ai

Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning

arXiv:2605.06734v1 Announce Type: cross Abstract: Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states. Quantum FWPs (

Hardware

GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control

arXiv:2510.07625v2 Announce Type: replace Abstract: While Model Predictive Control (MPC) delivers strong performance across robotics applications, solving the underlying (batches of) nonlinear traject

hardwarearxiv-cs-ro

GazeVLM: Active Vision via Internal Attention Control for Multimodal Reasoning

arXiv:2605.07817v1 Announce Type: cross Abstract: Human visual reasoning is governed by active vision, a process where metacognitive control drives top-down goal-directed attention, dynamically routin

GC-ART: Global Learnable Second-Order Rational Tone Curves for Illumination Robustness

model-releasesarxiv-cs-cv

arXiv:2605.07329v1 Announce Type: new Abstract: We introduce GC-ART (Global Curve Adaptive Rational Tone-mapping), a lightweight differentiable pre-processing module for robust image classification. G

GEM: Generating LiDAR World Model via Deformable Mamba

arXiv:2605.07326v1 Announce Type: new Abstract: World models, which simulate environmental dynamics and generate sensor observations, are gaining increasing attention in autonomous driving. However, p

agentsarxiv-cs-cv

Generalised Linear Models in Deep Bayesian RL with Learnable Basis Functions

arXiv:2512.20974v3 Announce Type: replace-cross Abstract: Bayesian Reinforcement Learning (BRL), a subclass of Meta-Reinforcement Learning (Meta-RL), provides a principled framework for generalisation

safetyarxiv-cs-ai

Generalising Travel Time Prediction To Varying Route Choices In Urban Networks

arXiv:2605.06918v1 Announce Type: cross Abstract: Previous methods that predict system-wide travel time, predominantly grounded in graph neural networks, remain limited to typical and recurring demand

researcharxiv-cs-lg

Generalized Euler Logarithm and its Applications in Machine Learning: Natural Gradient, Backpropagation, Generalized EG, Mirror Descent and OLPS

arXiv:2502.17500v3 Announce Type: replace-cross Abstract: This paper investigates in depth the fundamental properties of the two-parameter generalized Euler logarithm and its inverse, the associated d

Generate an image of the scariest thing you could possibly imagine scribbled into a child's coloring book

This Reddit post from r/ChatGPT likely discusses a creative prompt experiment where users attempted to get ChatGPT or similar AI image generation tools to create disturbing or horror-themed content by

industryr-chatgpt

Generate split-stack videos like this in a single click. The basic setup is simple, just upload an image and the system takes care of the re…

Generate split-stack videos like this in a single click. The basic setup is simple, just upload an image and the system takes care of the rest. Want more control? Switch to graph mode and fine-tune ea

local-aicomfyui--x

Generating training datasets for legal chatbots in Korean

arXiv:2605.07432v1 Announce Type: new Abstract: Chatbots are robots that can communicate with humans using text or voice signals. Legal chatbots improve access to justice, since legal representation a

local-aiarxiv-cs-cl

Generative Modeling with Flux Matching

arXiv:2605.07319v1 Announce Type: cross Abstract: We introduce Flux Matching, a new paradigm for generative modeling that generalizes existing score-based models to a broader family of vector fields t

researcharxiv-cs-ai

Applications

Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension

arXiv:2510.01105v2 Announce Type: replace Abstract: Neural multivariate regression underpins a wide range of domains, including control, robotics, and finance, yet the geometry of its learned represen

applicationsarxiv-cs-lg

Geometric Kolmogorov--Arnold Network (GeoKAN)

arXiv:2605.06740v1 Announce Type: cross Abstract: We introduce Geometric Kolmogorov--Arnold Networks (GeoKANs), a family of geometry-aware KAN-type models in which approximation is carried out in lear

local-aiarxiv-cs-ai

GESR: Graph-Based Edge Semantic Reconstruction for Stealthy Communication Detection with Benign-Only Training

arXiv:2605.07536v1 Announce Type: cross Abstract: Detecting stealthy malicious communications from flow logs under benign-only training remains a critical challenge in network security. Malicious comm

local-aiarxiv-cs-lg

GitLab announces layoffs, saying they are 'not an AI optimization or cost cutting exercise', and plans to cut the number of countries it operates in (Sarah Frier/Bloomberg)

Sarah Frier / Bloomberg: GitLab announces layoffs, saying they are “not an AI optimization or cost cutting exercise”, and plans to cut the number of countries it operates in — GitLab Inc., which makes

agentstechmeme

Give our early preview of Computer Use (with ANY model) a try today! Built into the latest Hermes Agent and powered by @trycua - opens the d…

Give our early preview of Computer Use (with ANY model) a try today! Built into the latest Hermes Agent and powered by @trycua - opens the door to any model, not just the frontier models in special mo

agentsnous-research--x

GLiGuard: Schema-Conditioned Classification for LLM Safeguard

model-releasesarxiv-cs-cl

arXiv:2605.07982v1 Announce Type: new Abstract: Ensuring safe, policy-compliant outputs from large language models requires real-time content moderation that can scale across multiple safety dimension

Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction

arXiv:2605.08022v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) have been proposed as biologically plausible and energy-efficient alternatives to conventional Artificial Neural Networ

GM plans to lay off IT workers in an effort to trim costs and bring in staff with skills in other tech areas; sources: the cuts will affect 500 to 600 employees (David Welch/Bloomberg)

David Welch / Bloomberg: GM plans to lay off IT workers in an effort to trim costs and bring in staff with skills in other tech areas; sources: the cuts will affect 500 to 600 employees — General Moto

industrytechmeme

Goal-Conditioned Decision Transformer for Multi-Goal Offline Reinforcement Learning

arXiv:2410.06347v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) in robotics faces significant hurdles regarding sample efficiency and generalization across varying goals. While O

🤔Going to be in Vancouver tomorrow? 🧐Curious about whether or not scaling AI is a bad bet? 😎Catch TPN founder @zacharykarabell's chat wit…

🤔Going to be in Vancouver tomorrow? 🧐Curious about whether or not scaling AI is a bad bet? 😎Catch TPN founder @zacharykarabell's chat with @GaryMarcus at @WebSummit at 10:20am. 🎟️Get your tickets here

safetygary-marcus--x

Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning

arXiv:2602.14868v2 Announce Type: replace-cross Abstract: Reinforcement learning has emerged as a powerful paradigm for unlocking reasoning capabilities in language models. However, relying on sparse

researcharxiv-cs-ai

Gonna keep retweeting, because this flawed move never dies:

Gonna keep retweeting, because this flawed move never dies: AI hype has become a giant game of bait and switch. the bait: we are going to make an AI that can solve any problem a expert human could sol

safetygary-marcus--x

Google says criminals used AI to build a working zero-day exploit for the first time

model-releasessiliconangle

Criminal hackers have used artificial intelligence to develop a working zero-day exploit, the first confirmed case of its kind, according to a report released today by Google LLC’s Google Threat Intel

Google stopped a zero-day hack that it says was developed with AI

For the first time, Google says it has spotted and stopped a zero-day exploit developed with AI. According to a report from Google Threat Intelligence Group (GTIG), 'prominent cyber crime threat actor

industrythe-verge-ai

Google's TIG reports the first known example of hackers using AI to discover and weaponize a zero-day; TIG's chief analyst says 'this is the tip of the iceberg' (Dustin Volz/New York Times)

Dustin Volz / New York Times: Google's TIG reports the first known example of hackers using AI to discover and weaponize a zero-day; TIG's chief analyst says “this is the tip of the iceberg” — The com

industrytechmeme

Google's TIG says it likely thwarted the use of an AI-generated zero-day in a 'mass exploitation event' and tools like OpenClaw are being used to find exploits (Samantha Subin/CNBC)

Samantha Subin / CNBC: Google's TIG says it likely thwarted the use of an AI-generated zero-day in a “mass exploitation event” and tools like OpenClaw are being used to find exploits — Google's Threat

industrytechmeme

GPO-V: Jailbreak Diffusion Vision Language Model by Global Probability Optimization

arXiv:2605.07399v1 Announce Type: new Abstract: Diffusion Vision-Language Models (dVLMs), built upon the non-causal foundations of Diffusion Large Language Models (dLLMs), have demonstrated remarkable

safetyarxiv-cs-cv

Gradient-Based LoRA Rank Allocation Under GRPO: An Empirical Study

model-releasesarxiv-cs-cl

arXiv:2605.07366v1 Announce Type: new Abstract: Adaptive rank allocation for LoRA, allocating more parameters to important layers and fewer to unimportant ones, consistently improves efficiency under

Gradient Extrapolation-Based Policy Optimization

arXiv:2605.06755v1 Announce Type: cross Abstract: Reinforcement learning is widely used to improve the reasoning ability of large language models, especially when answers can be automatically checked.

Gradient Starvation in Binary-Reward GRPO: Why Group-Mean Centering Fails and Why the Simplest Fix Works

model-releasesarxiv-cs-lg

arXiv:2605.07689v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) is a standard algorithm for reinforcement learning from verifiable rewards, but its group-mean-centered advant

GRAPE: Let GRPO Supervise Query Rewriting by Ranking for Retrieval

arXiv:2509.23370v2 Announce Type: replace Abstract: The CLIP model has established itself as a cornerstone of large-scale retrieval systems. However, its performance often degrades under distributiona

safetyarxiv-cs-cv

Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs

model-releasesarxiv-cs-lg

arXiv:2605.07961v1 Announce Type: new Abstract: Federated fine-tuning (FFT) has emerged as a privacy-preserving paradigm for collaboratively adapting large language models (LLMs). Built upon federated

Graph-Structured Hyperdimensional Computing for Data-Efficient and Explainable Process-Structure-Property Prediction

arXiv:2605.07999v1 Announce Type: cross Abstract: Multiphoton photoreduction enables high-fidelity fabrication of complex 3D microstructures, yet reliable process-structure-property (PSP) prediction r

GraphDC: A Divide-and-Conquer Multi-Agent System for Scalable Graph Algorithm Reasoning

arXiv:2605.06671v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong potential for many mathematical problems. However, their performance on graph algorithmic tasks is

agentsarxiv-cs-ai

GraphFusion3D: Dynamic Graph Attention Convolution with Adaptive Cross-Modal Transformer for 3D Object Detection

arXiv:2512.02991v2 Announce Type: replace Abstract: Despite significant progress in 3D object detection, point clouds remain challenging due to sparse data, incomplete structures, and limited semantic

researcharxiv-cs-cv

GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs

arXiv:2605.08074v1 Announce Type: new Abstract: Conformal prediction (CP) provides a distribution-free approach to uncertainty quantification with finite-sample guarantees. However, applying CP to gra

local-aiarxiv-cs-lg

GraphReAct: Reasoning and Acting for Multi-step Graph Inference

arXiv:2605.07357v1 Announce Type: new Abstract: Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, ex

GRASP -- Graph-Based Anomaly Detection Through Self-Supervised Classification

arXiv:2605.07812v1 Announce Type: cross Abstract: Advanced persistent threat (APT) attacks remain difficult to detect due to their stealth, adaptability, and use of legitimate system components. Prove

researcharxiv-cs-lg

GRaSp: Automatic Example Optimization for In-Context Learning in Low-Data Tasks

arXiv:2605.07454v1 Announce Type: new Abstract: In-context learning enables large language models to adapt to new tasks, but their performance is highly sensitive to the selected examples. Finding eff

researcharxiv-cs-cl

great article, mostly focused on coding agents but applies elsewhere impo. aligns w a lot of my prior thoughts: - agents need scaffolding, n…

great article, mostly focused on coding agents but applies elsewhere impo. aligns w a lot of my prior thoughts: - agents need scaffolding, not just smarter models - failures should become durable impr

agentsyohei-nakajima--x

Great essay by Tobi. Building an AI-native company? Go read it now. I couldn't resist visualizing it with my artifact generator. Biggest tak…

Great essay by Tobi. Building an AI-native company? Go read it now. I couldn't resist visualizing it with my artifact generator. Biggest takeaway for me: 'The risk isn't that AI does the work. It's th

researchdair-ai--x