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Gamma-World: Generative Multi-Agent World Modeling Beyond Two Players

model-releasesarxiv-cs-cv

arXiv:2605.28816v1 Announce Type: new Abstract: World models for interactive video generation have largely focused on single-agent settings, where future observations are generated from a single contr

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Human DGX agent

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GE-Sim 2.0: A Roadmap Towards Comprehensive Closed-loop Video World Simulators for Robotic Manipulation

DGX agent

arXiv:2605.27491v1 Announce Type: new Abstract: We introduce GE-Sim 2.0 (Genie Envisioner World Simulator 2.0), a closed-loop video world simulator for robotic manipulation. Building on the action-con

safetyarxiv-cs-ro

28 May 2026

Applications

GEM: Generative Supervision Helps Embodied Intelligence

arXiv:2605.28548v1 Announce Type: new Abstract: Embodied Vision-Language Models (VLMs) have demonstrated impressive performance and generalization in robotics, particularly within Vision-Language-Acti

applicationsarxiv-cs-cv

Generalized Holographic Reduced Representations

arXiv:2405.09689v2 Announce Type: replace-cross Abstract: Hyperdimensional Computing (HDC) is a computationally and data-efficient paradigm that acts as a bridge between connectionist and symbolic app

GeneralThinker: Domain-General Reasoning through Likelihood-Guided Answer-Conditioned Optimization

arXiv:2605.27934v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves language model reasoning, but its reliance on domain-specific verifiers, sparse outcome rewards,

safetyarxiv-cs-cl

Genetic algorithm vs. gradient descent for training a neural network architecture dedicated to low data regimes in small medical datasets

arXiv:2605.27411v1 Announce Type: cross Abstract: Aim/Introduction: Distance-encoding biomorphic-informational neural network (DEBI-NN) is a recently proposed architecture in which connection weights

researcharxiv-cs-lg

GenSBI: Generative Methods for Simulation-Based Inference in JAX

arXiv:2605.27499v1 Announce Type: new Abstract: Flow and diffusion generative models have established themselves as widely adopted density estimators for simulation-based inference (SBI), extending na

researcharxiv-cs-lg

Geometry-Correct Diffusion Posterior Sampling with Denoiser-Pullback Curvature Guidance and Manifold-Aligned Damping

arXiv:2605.27990v1 Announce Type: cross Abstract: Diffusion posterior sampling conditions diffusion priors on measurements, but data-consistency updates are typically scaled by hand-tuned guidance wei

Local Ai

Geometry-First Generative Spatial Single-Cell Reconstruction

arXiv:2605.28200v1 Announce Type: new Abstract: Single-cell RNA sequencing (scRNA-seq) profiles large numbers of cells but loses spatial context, whereas spatial transcriptomics (ST) preserves partial

local-aiarxiv-cs-lg

Geometry of Human Perceptual Domains Emerges Transiently in LLM Representations

arXiv:2605.27970v1 Announce Type: new Abstract: While large language models (LLMs) are trained purely on textual data, prior work has shown that their internal representations can exhibit rich geometr

Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settings

arXiv:2605.28233v1 Announce Type: cross Abstract: Fairness-accuracy trade-offs are a central concern in the deployment of fairness-aware machine learning methods. When sensitive attributes are unavail

safetyarxiv-cs-lg

Global Policy-Space Response Oracles for Two-Player Zero-Sum Games

arXiv:2605.28273v1 Announce Type: new Abstract: The Policy-Space Response Oracles (PSRO) framework scales equilibrium computation to large zero-sum games by iteratively expanding a restricted strategy

GONDOR to the Rescue: Satisficing Planning with Low Memory

arXiv:2605.28454v1 Announce Type: new Abstract: Greedy Best-First Search (GBFS) is the dominant approach for solving search problems where the goal can be estimated with a heuristic, such as planning,

GOProteinGNN: Leveraging Protein Knowledge Graphs for Protein Representation Learning

arXiv:2408.00057v3 Announce Type: replace-cross Abstract: Proteins play a vital role in biological processes and are indispensable for living organisms. Accurate representation of proteins is crucial,

researcharxiv-cs-lg

Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems

arXiv:2605.27766v1 Announce Type: new Abstract: LLM safety evaluations predominantly test models in isolation, yet deployed AI agents increasingly operate within persistent social environments alongsi

Applications

GraD-IBD: Graph Representation Learning from Diagnosis Trajectories for Early Detection of Inflammatory Bowel Disease

arXiv:2605.27799v1 Announce Type: new Abstract: International Classification of Diseases (ICD) is a globally recognized coding system that records diagnostic events during each patient encounter, prov

applicationsarxiv-cs-ai

GRADE: Generalizable Reasoning-Aware Dialogue Evaluation for AI Tutors

arXiv:2605.27866v1 Announce Type: new Abstract: Evaluating AI tutor responses requires more than factual correctness: tutors must identify mistakes, locate errors, provide guidance, and offer actionab

researcharxiv-cs-cl

Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstruction

arXiv:2605.28124v1 Announce Type: new Abstract: The goal of this work is to reduce the effect of photon noise in dental cone-beam CT reconstruction. We consider an inverse problem formulation and deve

Gradient Transformer: Learning to Generate Updates for LLMs

model-releasesarxiv-cs-lg

arXiv:2605.27591v1 Announce Type: new Abstract: Many organizations lack computational resources to fine-tune large language models (LLMs) on private (unshareable) data for better utility, while fine-t

GradientStabilizer:Fix the Norm, Not the Gradient

arXiv:2502.17055v4 Announce Type: replace-cross Abstract: Training instability in modern deep learning systems is frequently triggered by rare but extreme gradient-norm spikes, which can induce oversi

Graph Neural Networks for Source Detection: A Review and Benchmark Study

model-releasesarxiv-cs-lg

arXiv:2512.20657v2 Announce Type: replace-cross Abstract: The source detection problem arises when an epidemic process unfolds over a contact network, and the objective is to identify its point of ori

Graph-of-Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills

arXiv:2604.05333v3 Announce Type: replace Abstract: Modern LLM agents increasingly rely on reusable skills, and as they interact with personal applications, web browsers, and other interfaces, skill l

GraphLit: Learning Text-Enriched Dynamic Character Network Representations for Literary Study

arXiv:2605.28643v1 Announce Type: new Abstract: Methods to represent literary texts as graphs or sequences of graphs mainly focus on representing character interactions, and often overlook another cru

researcharxiv-cs-cl

Applications

GraphSteal: Structural Knowledge Stealing from Graph RAG via Traversal Reconstruction

arXiv:2605.28645v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances LLMs by grounding generation in query-relevant external evidence. Beyond unstructured text corpora, Grap

applicationsarxiv-cs-cl

Grimlock: Guarding High-Agency Systems with eBPF and Attested Channels

arXiv:2605.27488v1 Announce Type: cross Abstract: Agentic systems increasingly run user-authored orchestration code that invokes tools, spawns subtasks, and delegates work across machines and clouds.

Grounded Cache Routing for Retrieval-Augmented Generation: When Is It Safe to Reuse an Answer?

arXiv:2605.27494v1 Announce Type: cross Abstract: Modern retrieval-augmented generation(RAG) deployments increasingly rely on caching to reduce token cost and time-to-first-token(TTFT). Prefix-level K

GS-FUSE: Granger-Supervised Gated Fusion and Multi-Granularity Alignment for Event-Driven Financial Forecasting

arXiv:2605.28520v1 Announce Type: new Abstract: Accurately forecasting the impact of salient financial events on markets is critical for investors and policymakers. However, existing multimodal time-s

Guaranteed Optimal Compositional Explanations for Neurons

arXiv:2511.20934v2 Announce Type: replace Abstract: Compositional explanations are a family of methods that aim to describe the spatial alignment between neurons' receptive field activations and conce

GUI Agents for Continual Game Generation

arXiv:2605.28258v1 Announce Type: cross Abstract: Generating a game is not the same as making one that can be played. Despite advances in code generation, existing approaches treat game generation as

GUI-CIDER: Mid-training GUI Agents via Causal Internalization and Density-aware Exemplar Reselection

arXiv:2605.28534v1 Announce Type: new Abstract: Despite the rapid progress of multimodal large language models in building Graphical User Interface (GUI) agents, their real-world task completion is fu

agentsarxiv-cs-cl

Hallucination Behavior in Multimodal LLMs Across Agricultural Image Interpretation and Generation Tasks

arXiv:2605.27595v1 Announce Type: cross Abstract: Large Language Models (LLMs) are being rapidly adopted in agricultural imaging applications, ranging from crop interpretation to synthetic field image

HardMTBench: Stress-Testing Chinese-English Translation on Knowledge-Intensive Domains

model-releasesarxiv-cs-cl

arXiv:2605.28315v1 Announce Type: new Abstract: General-purpose machine translation benchmarks such as FLORES-200 have reached a saturation regime on Chinese-English pairs, where modern large language

Local Ai

HarmoVid: Relightful Video Portrait Harmonization

arXiv:2605.28811v1 Announce Type: new Abstract: We present a method for harmonizing the lighting of a foreground video to match a target background scene, adjusting shadows, color tone, and illuminati

local-aiarxiv-cs-cv

Harness-Bench: Measuring Harness Effects across Models in Realistic Agent Workflows

arXiv:2605.27922v1 Announce Type: new Abstract: LLM agents are increasingly deployed as executable systems that use tools, modify workspaces, and produce concrete artifacts. In such workflows, perform

Local Ai

HARP: Measuring Harm Amplification in Multi-Agent LLM Systems

arXiv:2605.27489v1 Announce Type: cross Abstract: Multi-agent LLM systems decompose workflows across agents, tools, shared context, memory, and decision gates. This modularity improves interpretabilit

local-aiarxiv-cs-ai

HEAL: Resilient and Self-* Hub-based Learning

arXiv:2605.27475v1 Announce Type: cross Abstract: Decentralized learning enhances privacy, scalability, and fault tolerance by distributing data and computation across nodes. A popular approach is Fed

HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning

arXiv:2501.09934v3 Announce Type: replace-cross Abstract: The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient Machine Learning (ML) solutions that can handle high vehicular m

HELEA: Hard-Negative Benchmark and LLM-based Reranking for Robust Entity Alignment

model-releasesarxiv-cs-cl

arXiv:2605.28308v1 Announce Type: new Abstract: Entity Alignment (EA) is essential for knowledge graph (KG) fusion, but existing benchmarks often allow models to exploit name overlap rather than relat

Heterogeneous Causal Discovery of Repeated Undesirable Health Outcomes

arXiv:2503.11477v2 Announce Type: replace Abstract: Understanding the factors that trigger or prevent undesirable health outcomes across patient subpopulations is essential for designing targeted inte

Heterogeneous Multi-Agent Modeling for Measurement and Network Analysis of the Data Service Market

arXiv:2605.27433v1 Announce Type: cross Abstract: With the increasing complexity of collaboration among various social entities and user demands, the factors affecting the stable development of the da

Hardware

Heterogeneous Parallelism for Multimodal Large Language Model Training

arXiv:2605.27678v1 Announce Type: new Abstract: Foundation model training is becoming multimodal, from post-training pipelines to large-scale pretraining. As modality coverage broadens, context window

hardwarearxiv-cs-lg

HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling

arXiv:2512.23959v3 Announce Type: replace-cross Abstract: Multi-step retrieval-augmented generation (RAG) has become a widely adopted strategy for enhancing large language models (LLMs) on tasks that

Hierarchical Prompt-Domain Control and Learning for Resource-Constrained Agentic Language Models

arXiv:2605.27703v1 Announce Type: new Abstract: Large Language Models are increasingly deployed inside agentic systems, where they must follow structured protocols, adapt to evolving states, and opera

Tutorials

Hierarchical Relation-augmented Representation Generalization for Few-shot Action Recognition

arXiv:2504.10079v4 Announce Type: replace Abstract: Few-shot action recognition (FSAR) aims to recognize novel action categories with few exemplars. Existing methods typically learn frame-level repres

tutorialsarxiv-cs-cv

Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework

arXiv:2605.28198v1 Announce Type: new Abstract: Existing approaches for synthetic tabular data generation are based on either purely generative models or LLMs, both of which struggle with data heterog

safetyarxiv-cs-lg

High-Fidelity Industrial Crash Dynamics Prediction via Geometry-Aware Operator Learning with Memory-Efficient Low-Rank Attention

arXiv:2605.27758v1 Announce Type: cross Abstract: Automotive crashworthiness optimization remains a safety-critical challenge, requiring the management of large-scale nonlinear structural deformations

High Performance, Low Reliability: Uncertainty Benchmarking for Tabular Foundation Models

model-releasesarxiv-cs-lg

arXiv:2605.28554v1 Announce Type: new Abstract: Recent Tabular Foundation Models (TFMs) have demonstrated state-of-the-art predictive performance, often surpassing Gradient-Boosted Decision Trees (GBD

HiRQA: Hierarchical Ranking and Quality Alignment for Opinion-Unaware Image Quality Assessment

arXiv:2508.15130v2 Announce Type: replace Abstract: Despite significant progress in no-reference image quality assessment (NR-IQA), dataset biases and reliance on subjective labels continue to hinder

safetyarxiv-cs-cv