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GFMate: Empowering Graph Foundation Models with Test-time Prompt Tuning

model-releasesarxiv-cs-lg

arXiv:2605.14809v1 Announce Type: new Abstract: Graph prompt tuning has shown great potential in graph learning by introducing trainable prompts to enhance the model performance in conventional single

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GhostCite: A Large-Scale Analysis of Citation Validity in the Age of Large Language Models

DGX agent

arXiv:2602.06718v2 Announce Type: replace-cross Abstract: Citations provide the basis for trusting scientific claims; when they are invalid or fabricated, this trust collapses. With the advent of Larg

model-releasesarxiv-cs-ai

15 May 2026

Good to Go: The LOOP Skill Engine That Hits 99% Success and Slashes Token Usage by 99% via One-Shot Recording and Deterministic Replay

arXiv:2605.14237v1 Announce Type: new Abstract: Deploying AI agents for repetitive periodic tasks exposes a critical tension: Large Language Models (LLMs) offer unmatched flexibility in tool orchestra

GPart: End-to-End Isometric Fine-Tuning via Global Parameter Partitioning

arXiv:2605.14841v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) has become the dominant paradigm for parameter-efficient fine-tuning (PEFT) of large language models (LLMs). However, its b

Gradient Iterated Temporal-Difference Learning

arXiv:2603.07833v2 Announce Type: replace-cross Abstract: Temporal-difference (TD) learning is highly effective at controlling and evaluating an agent's long-term outcomes. Most approaches in this par

GradShield: Alignment Preserving Finetuning

arXiv:2605.14194v1 Announce Type: new Abstract: Large Language Models (LLMs) pose a significant risk of safety misalignment after finetuning, as models can be compromised by both explicitly and implic

safetyarxiv-cs-cl

Graph of States: Solving Abductive Tasks with Large Language Models

arXiv:2603.21250v2 Announce Type: replace Abstract: Logical reasoning encompasses deduction, induction, and abduction. However, while Large Language Models (LLMs) have effectively mastered the former

GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration

arXiv:2605.13848v1 Announce Type: new Abstract: Agentic LLM frameworks that rely on prompted orchestration, where the model itself determines workflow transitions, often suffer from hallucinated routi

Local Ai

GraphFlow: An Architecture for Formally Verifiable Visual Workflows Enabling Reliable Agentic AI Automation

arXiv:2605.14968v1 Announce Type: new Abstract: GraphFlow is a visual workflow system designed to improve the reliability of agentic AI automation in multi-step, mission-critical processes. In these w

local-aiarxiv-cs-ai

Graphs of Research: Citation Evolution Graphs as Supervision for Research Idea Generation

arXiv:2605.14790v1 Announce Type: cross Abstract: Research idea generation is the innovation-driving step of automated scientific research. Recently, large language models (LLMs) have shown potential

Grokking Finite-Dimensional Algebra

arXiv:2602.19533v2 Announce Type: replace-cross Abstract: This paper investigates the grokking phenomenon, which refers to the sudden transition from a long memorization to generalization observed dur

safetyarxiv-cs-ai

Grounded Continuation: A Linear-Time Runtime Verifier for LLM Conversations

arXiv:2605.14175v1 Announce Type: new Abstract: In long conversations, an LLM can produce a next utterance that sounds plausible but rests on premises the conversation has already abandoned. Context-m

GroupMemBench: Benchmarking LLM Agent Memory in Multi-Party Conversations

model-releasesarxiv-cs-cl

arXiv:2605.14498v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly serve as personal assistants and workplace collaborators, where their utility depends on memory systems t

Guided Diffusion Sampling for Precipitation Forecast Interventions

arXiv:2605.14317v1 Announce Type: new Abstract: Extreme precipitation causes severe societal and economic damage, and weather control has long been discussed as a potential mitigation strategy. Howeve

researcharxiv-cs-lg

Applications

H-OmniStereo: Zero-Shot Omnidirectional Stereo Matching with Heading-Aligned Normal Priors

arXiv:2605.14963v1 Announce Type: new Abstract: Stereo matching on top-bottom equirectangular images provides an effective framework for full-surround perception, as vertically aligned epipolar lines

applicationsarxiv-cs-cv

Hand-in-the-Loop: Improving Dexterous VLA via Seamless Interventional Correction

arXiv:2605.15157v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are prone to compounding errors in dexterous manipulation, where high-dimensional action spaces and contact-rich d

safetyarxiv-cs-lg

HASTE: Training-Free Video Diffusion Acceleration via Head-Wise Adaptive Sparse Attention

arXiv:2605.14513v1 Announce Type: cross Abstract: Diffusion-based video generation has advanced substantially in visual fidelity and temporal coherence, but practical deployment remains limited by the

HDRFace: Rethinking Face Restoration with High-Dimensional Representation

model-releasesarxiv-cs-cv

arXiv:2605.14821v1 Announce Type: new Abstract: Face restoration under complex degradations still remains an ill-posed inverse problem due to severe information loss. Although diffusion models benefit

Head Forcing: Long Autoregressive Video Generation via Head Heterogeneity

arXiv:2605.14487v1 Announce Type: cross Abstract: Autoregressive video diffusion models support real-time synthesis but suffer from error accumulation and context loss over long horizons. We discover

HeatKV: Head-tuned KV-cache Compression for Visual Autoregressive Modeling

arXiv:2605.14877v1 Announce Type: new Abstract: Visual Autoregressive (VAR) models have recently demonstrated impressive image generation quality while maintaining low latency. However, they suffer fr

safetyarxiv-cs-cv

Herculean: An Agentic Benchmark for Financial Intelligence

arXiv:2605.14355v1 Announce Type: new Abstract: As AI agents improve, the central question is no longer whether they can solve isolated well-defined financial tasks, but whether they can reliably carr

HERO: Hierarchical Extrapolation and Refresh for Efficient World Models

arXiv:2508.17588v2 Announce Type: replace Abstract: Generation-driven world models create immersive virtual environments but suffer slow inference due to the iterative nature of diffusion models. Whil

researcharxiv-cs-cv

Heuristic Pathologies and Further Variance Reduction via Uncertainty Propagation in the AIVAT Family of Techniques

arXiv:2605.14261v1 Announce Type: new Abstract: How should an agent's performance in a multiagent environment be evaluated when there is a limited sample size or a high cost of running a trial? The AI

Hidden State Poisoning Attacks against Mamba-based Language Models

arXiv:2601.01972v4 Announce Type: cross Abstract: State space models (SSMs) like Mamba offer efficient alternatives to Transformer-based language models, with linear time complexity. Yet, their advers

Hierarchical Image Tokenization for Multi-Scale Image Super Resolution

arXiv:2605.14891v1 Announce Type: new Abstract: We introduce a multi-scale Image Super Resolution (ISR) method building on recent advances in Visual Auto-Regressive (VAR) modeling. VAR models break im

safetyarxiv-cs-cv

HiSem: Hierarchical Semantic Disentangling for Remote Sensing Image Change Captioning

model-releasesarxiv-cs-cv

arXiv:2605.15024v1 Announce Type: new Abstract: Remote sensing image change captioning (RSICC) aims to achieve high-level semantic understanding of genuine changes occurring between bi-temporal images

HodgeCover: Higher-Order Topological Coverage Drives Compression of Sparse Mixture-of-Experts

arXiv:2605.13997v1 Announce Type: cross Abstract: Sparse Mixture-of-Experts (MoE) layers route tokens through a handful of experts, and learning-free compression of these layers reduces inference cost

Holistic Evaluation and Failure Diagnosis of AI Agents

arXiv:2605.14865v1 Announce Type: new Abstract: AI agents execute complex multi-step processes, but current evaluation falls short: outcome metrics report success or failure without explaining why, an

How Sensitive Are Radiomic AI Models to Acquisition Parameters?

arXiv:2605.14667v1 Announce Type: new Abstract: A main barrier for the deployment of AI radiomic systems in clinical routine is their drop in performance under heterogeneous multicentre acquisition pr

Tutorials

How to Evaluate and Refine your CAM

arXiv:2605.14641v1 Announce Type: cross Abstract: Class attribution maps (CAMs) provide local explanations for the decisions of convolutional neural networks. While widely used in practice, the evalua

tutorialsarxiv-cs-ai

Tutorials

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization

arXiv:2605.14200v1 Announce Type: new Abstract: Recent frontier large language models predominantly rely on Mixture-of-Experts (MoE) architectures. Despite empirical progress, there is still no princi

tutorialsarxiv-cs-lg

How well behaved is finite dimensional Diffusion Maps?

arXiv:2412.03992v3 Announce Type: replace-cross Abstract: Under a set of assumptions on a family of submanifolds subset {mathbb R}^D, we derive a series of geometric properties that remain valid after

researcharxiv-cs-lg

Hyperbolic Graph Neural Networks Under the Microscope: The Role of Geometry-Task Alignment

arXiv:2602.01828v2 Announce Type: replace Abstract: Many complex networks exhibit hierarchical, tree-like structures, making hyperbolic space a natural candidate wherein to learn representations of th

safetyarxiv-cs-lg

Hypergraph Enterprise Agentic Reasoner over Heterogeneous Business Systems

arXiv:2605.14259v1 Announce Type: new Abstract: Applying Large Language Models (LLMs) to heterogeneous enterprise systems is hindered by hallucinations and failures in multi-hop, n-ary reasoning. Exis

Hyperspectral Image Land Cover Captioning Dataset for Vision Language Models

model-releasesarxiv-cs-cv

arXiv:2505.12217v2 Announce Type: replace Abstract: We introduce HyperCap, the first large-scale hyperspectral captioning dataset designed to enhance model performance and effectiveness in remote sens

ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition

arXiv:2605.14309v1 Announce Type: cross Abstract: Machine unlearning in Vision-Language Models (VLMs) is typically performed at the image or instance level, making it difficult to precisely remove tar

safetyarxiv-cs-ai

Identifying Culprits Through Deep Deterministic Policy Gradient Deep Learning Investigation

arXiv:2605.14774v1 Announce Type: new Abstract: In the world of AI and advanced technologies investigation aspects identification of a crime or criminal plays a major problem. In this research we focu

safetyarxiv-cs-ai

Ideology Prediction of German Political Texts

arXiv:2605.14352v1 Announce Type: new Abstract: Elections represent a crucial milestone in a nation's ongoing development. To better understand the political rhetoric from various movements, ranging f

safetyarxiv-cs-cl

IFPV: An Integrated Multi-Agent Framework for Generative Operational Planning and High-Fidelity Plan Verification

arXiv:2605.14851v1 Announce Type: cross Abstract: Operational plan generation and verification are critical for modern complex and rapidly changing battlefield environments, yet traditional generation

Tutorials

IG-Diff: Complex Night Scene Restoration with Illumination-Guided Diffusion Model

arXiv:2605.14337v1 Announce Type: new Abstract: In nighttime circumstances, it is challenging for individuals and machines to perceive their surroundings. While prevailing image restoration methods ad

tutorialsarxiv-cs-cv

Image Restoration via Diffusion Models with Dynamic Resolution

arXiv:2605.14267v1 Announce Type: cross Abstract: Diffusion models (DMs) have exhibited remarkable efficacy in various image restoration tasks. However, existing approaches typically operate within th

Applications

ImmuVis: Hyperconvolutional Foundation Model for Imaging Mass Cytometry

arXiv:2602.04585v2 Announce Type: replace Abstract: We present ImmuVis, a family of efficient foundation models for imaging mass cytometry (IMC), a high-throughput multiplex imaging technology that ha

applicationsarxiv-cs-cv

Implicit spatial-frequency fusion of hyperspectral and lidar data via kolmogorov-arnold networks

arXiv:2605.14239v1 Announce Type: new Abstract: Hyperspectral image (HSI) classification is challenging in complex scenes due to spectral ambiguity, spatial heterogeneity, and the strong coupling betw

researcharxiv-cs-cv

Improving Multi-turn Dialogue Consistency with Self-Recall Thinking

arXiv:2605.15102v1 Announce Type: cross Abstract: Large language model (LLM) based multi-turn dialogue systems often struggle to track dependencies across non-adjacent turns, undermining both consiste

In-Context Learning for Data-Driven Censored Inventory Control

model-releasesarxiv-cs-lg

arXiv:2605.14840v1 Announce Type: new Abstract: We study inventory control with decision-dependent censoring, focusing on the censored or repeated newsvendor (R-NV), where each order quantity determin

In-IDE Toolkit for Developers of AI-Based Features

arXiv:2605.14612v1 Announce Type: cross Abstract: AI-enabled features built on LLMs and agentic workflows are difficult to test, debug, and reproduce, especially for product-focused software engineers

Indian Wedding System Optimization (IWSO): A Novel Socially Inspired Metaheuristic with Operational Design and Analysis

model-releasesarxiv-cs-lg

arXiv:2605.13871v1 Announce Type: cross Abstract: This paper presents a novel population-based metaheuristic, Indian Wedding System Optimization (IWSO), inspired by the socio-cultural dynamics of trad

InfoSFT: Learn More and Forget Less with Information-Aware Token Weighting

arXiv:2605.14967v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) provides the standard approach for teaching LLMs new behaviors from offline expert demonstrations. However, standard SFT un

safetyarxiv-cs-lg