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Golden Layers and Where to Find Them: Improved Knowledge Editing for Large Language Models Via Layer Gradient Analysis

arXiv:2602.20207v3 Announce Type: replace-cross Abstract: Knowledge editing in Large Language Models (LLMs) aims to update the model's prediction for a specific query to a desired target while preserv

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Safety

GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective

DGX agent

arXiv:2605.15723v1 Announce Type: cross Abstract: Multimodal alignment is commonly learned from isolated image-text pairs via CLIP-style dual encoders, leaving the relational context among entities la

safetyarxiv-cs-cv

18 May 2026

GQA-{mu}P: The maximal parameterization update for grouped query attention

arXiv:2605.15290v1 Announce Type: cross Abstract: Hyperparameter transfer across model architectures dramatically reduces the amount of compute necessary for tuning large language models (LLMs). The m

GQLA: Group-Query Latent Attention for Hardware-Adaptive Large Language Model Decoding

arXiv:2605.15250v1 Announce Type: cross Abstract: Multi-head Latent Attention (MLA), the attention used in DeepSeek-V2/V3, jointly compresses keys and values into a low-rank latent and matches the H10

Gradient-Based Join Ordering

arXiv:2511.14482v2 Announce Type: replace-cross Abstract: Join ordering is the NP-hard problem of selecting the most efficient order in which to evaluate joins (conjunctive, binary operators) in a dat

Graph-Regularized Sparse Autoencoders for LLM Safety Steering

arXiv:2512.06655v3 Announce Type: replace-cross Abstract: Sparse autoencoders (SAEs) are increasingly used to extract activation directions for inference-time steering, but their standard sparsity obj

GRASP: Learning to Ground Social Reasoning in Multi-Person Non-Verbal Interactions

arXiv:2605.15764v1 Announce Type: cross Abstract: Understanding social interactions requires reasoning over subtle non-verbal cues, yet current multimodal large language models (MLLMs) often fail to i

Applications

Greedy or not, here I come: Language production under vocabulary constraints in humans and resource-rational models

arXiv:2605.15365v1 Announce Type: new Abstract: Communicating using only a limited vocabulary is a common but challenging cognitive phenomenon, requiring an ideal communicator to plan carefully to opt

applicationsarxiv-cs-cl

GRLO: Towards Generalizable Reinforcement Learning in Open-Ended Environments from Zero

arXiv:2605.15464v1 Announce Type: cross Abstract: Post-training has become a crucial step for unlocking the capabilities of large language models, with reinforcement learning (RL) emerging as a critic

Grokking as Structural Inference: Transformers Need Bayesian Lottery Tickets

arXiv:2605.15787v1 Announce Type: cross Abstract: Why does a Transformer that has memorized its training set wait thousands of steps before it generalizes? Existing accounts locate this delay in norm

Local Ai

Grounded Reinforcement Learning for Visual Reasoning

arXiv:2505.23678v3 Announce Type: replace Abstract: While reinforcement learning (RL) over chains of thought has significantly advanced language models in tasks such as mathematics and coding, visual

local-aiarxiv-cs-cv

Agents

H-Mem: A Novel Memory Mechanism for Evolving and Retrieving Agent Memory via a Hybrid Structure

arXiv:2605.15701v1 Announce Type: cross Abstract: Memory data are ubiquitous in Large Language Model (LLM)-based agents (e.g., OpenClaw and Manus). A few recent works have attempted to exploit agents'

agentsarxiv-cs-ai

HAI-Eval: Measuring Human-AI Synergy in Collaborative Coding

arXiv:2512.04111v2 Announce Type: replace-cross Abstract: LLM-powered coding agents are reshaping the development paradigm. However, existing evaluation systems, neither traditional tests for humans n

Hallucinations are inevitable but can be made statistically negligible

arXiv:2502.12187v3 Announce Type: replace Abstract: Hallucinations, a phenomenon where a language model (LM) generates nonfactual content, pose a significant challenge to the practical deployment of L

researcharxiv-cs-cl

Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations

arXiv:2605.15216v1 Announce Type: cross Abstract: Always-on AI applications, from environmental sensors to biomedical implants, require ultra-low power consumption. Analog circuits offer a path to sub

Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning

arXiv:2605.15411v1 Announce Type: cross Abstract: We study contextual dynamic pricing in a semiparametric scalar-index valuation model where the latent value is v_t=mu_ast(mathsf c_t)+xi_t, with an un

Health-Conditioned Vision-Language-Action Models for Malfunction-Aware Robot Control

arXiv:2605.16056v1 Announce Type: new Abstract: Research on Vision Language Action (VLA) models has been increasing rapidly in recent years. Although some of them focus on detecting, preventing, and r

tutorialsarxiv-cs-ro

Applications

Hestia: Voxel-Face-Aware Hierarchical Next-Best-View Acquisition for Efficient 3D Reconstruction

arXiv:2508.01014v4 Announce Type: replace-cross Abstract: Advances in 3D reconstruction and novel view synthesis have enabled efficient and photorealistic rendering. However, images for reconstruction

applicationsarxiv-cs-cv

Heuristic-Based Merging of HPC Traces to Extend Hardware Counter Coverage

arXiv:2605.15832v1 Announce Type: cross Abstract: This work extends a framework for predicting the performance of High-Performance Computing (HPC) workloads using Machine Learning (ML). A common limit

Hidden in Memory: Sleeper Memory Poisoning in LLM Agents

arXiv:2605.15338v1 Announce Type: cross Abstract: Large language models are increasingly augmented with persistent memory, allowing assistants to store user-specific information across sessions for pe

Hierarchical and Holistic Open-Vocabulary Functional 3D Scene Graphs for Indoor Spaces

model-releasesarxiv-cs-cv

arXiv:2605.15753v1 Announce Type: cross Abstract: Functional 3D scene graphs offer a versatile and flexible representation for 3D scene understanding and robotic manipulation, defined by object nodes,

High-accuracy log-concave sampling with stochastic queries

arXiv:2602.14342v2 Announce Type: replace-cross Abstract: We show that high-accuracy guarantees for log-concave sampling -- that is, iteration and query complexities which scale as polylog(1/elta), wh

Highly Detailed and Generalizable Broadleaf Tree Crown Instance Segmentation from UAV Imagery

arXiv:2605.15673v1 Announce Type: cross Abstract: We present a highly detailed instance segmentation model for delineating individual tree crowns in natural broadleaf forests using aerial imagery acqu

researcharxiv-cs-cv

HoloMotion-1 Technical Report

arXiv:2605.15336v1 Announce Type: cross Abstract: In this report, we present HoloMotion-1, a humanoid motion foundation model for zero-shot whole-body motion tracking. A key innovation of HoloMotion-1

safetyarxiv-cs-ai

HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations

arXiv:2603.03243v2 Announce Type: replace Abstract: We present Whole-Body Mobile Manipulation Interface (HoMMI), a data collection and policy learning framework that learns whole-body mobile manipulat

safetyarxiv-cs-ro

Honey, I shrunk the hypothesis space (through logical preprocessing)

arXiv:2506.06739v3 Announce Type: replace Abstract: Inductive logic programming (ILP) is a form of logical machine learning. The goal is to search a hypothesis space for a hypothesis that generalises

How Data Augmentation Shapes Neural Representations

arXiv:2605.15306v1 Announce Type: new Abstract: Data augmentation is widely recognized for improving generalization in deep networks, yet its impact on the geometry of learned representations remains

How to Choose Your Teacher for Fine Grained Image Recognition

arXiv:2605.15689v1 Announce Type: new Abstract: Fine-grained image recognition classifies subcategories such as bird species or car models. While state-of-the-art (SOTA) models are accurate, they are

tutorialsarxiv-cs-cv

How to Train Your Advisor: Steering Black-Box LLMs with Advisor Models

arXiv:2510.02453v3 Announce Type: replace-cross Abstract: Frontier language models are deployed as black-box services, where model weights cannot be modified and customization is limited to prompting.

Hybrid LLM-based Intelligent Framework for Robot Task Scheduling

arXiv:2605.15486v1 Announce Type: cross Abstract: This study introduces intelligent frameworks that use Large Language Models (LLMs) to improve task scheduling for construction robots. The LLM is fed

Hydra: Efficient, Correct Code Generation via Checkpoint-and-Rollback Support

arXiv:2605.15238v1 Announce Type: cross Abstract: Large language models are increasingly used for code generation, but many generated programs fail to compile, a prerequisite for further correctness c

HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion

arXiv:2605.15741v1 Announce Type: new Abstract: Pixel-space diffusion models bypass the reconstruction bottleneck of Variational Autoencoders (VAEs) but face a fundamental 'granularity dilemma': captu

safetyarxiv-cs-cv

Hypothesis-driven construction of mesoscopic dynamics

arXiv:2605.16211v1 Announce Type: new Abstract: Traditional scientific modeling typically begins with fixed, instance-wise effective equations and then carries out equation-specific analysis and compu

tutorialsarxiv-cs-lg

Agents

ICRL: Learning to Internalize Self-Critique with Reinforcement Learning

arXiv:2605.15224v1 Announce Type: new Abstract: Large language model-based agents make mistakes, yet critique can often guide the same model toward correct behavior. However, when critique is removed,

agentsarxiv-cs-ai

IHF-Harmony: Multi-Modality Magnetic Resonance Images Harmonization using Invertible Hierarchy Flow Model

arXiv:2602.21536v2 Announce Type: replace Abstract: Retrospective MRI harmonization is limited by poor scalability across modalities and reliance on traveling subject datasets. To address these challe

researcharxiv-cs-cv

Imitation learning for clinical decision support in pediatric ECMO

arXiv:2605.16175v1 Announce Type: new Abstract: Pediatric critical care is a dynamic, high-stakes process involving constant monitoring and adjustments in life-saving treatments. Modeling these interv

tutorialsarxiv-cs-lg

Imperfect World Models are Exploitable

arXiv:2605.15960v1 Announce Type: new Abstract: We propose a novel definition of model exploitation in reinforcement learning. Informally, a world model is exploitable if it implies that one policy sh

safetyarxiv-cs-ai

Improve Large Language Model Systems with User Logs

arXiv:2602.06470v2 Announce Type: replace-cross Abstract: Scaling training data and model parameters has long driven progress in large language models (LLMs), but this paradigm is increasingly constra

safetyarxiv-cs-ai

Improved Bounds for Reward-Agnostic and Reward-Free Exploration

arXiv:2602.16363v2 Announce Type: replace Abstract: We study reward-free and reward-agnostic exploration in episodic finite-horizon Markov decision processes (MDPs), where an agent explores an unknown

Improving Cross-Cultural Survey Simulation with Calibrated Value Personas

arXiv:2605.16193v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to simulate human opinions and survey responses, but their ability to reproduce population responses

researcharxiv-cs-cl

IndicSafe: A Benchmark for Evaluating Multilingual LLM Safety in South Asia

arXiv:2603.17915v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are deployed in multilingual settings, their safety behavior in culturally diverse, low-resource languages rem

Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection

arXiv:2510.05676v2 Announce Type: replace Abstract: Graph-based methods are becoming increasingly popular in machine learning due to their ability to model complex data and relations. Insurance fraud

Applications

Inevitable Encounters: Backdoor Attacks Involving Lossy Compression

arXiv:2603.13864v2 Announce Type: replace-cross Abstract: Real-world backdoor attacks often require poisoned datasets to be stored and transmitted before being used to compromise deep learning systems

applicationsarxiv-cs-cv

Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference

arXiv:2605.05652v2 Announce Type: replace Abstract: Simulation-based inference (SBI) provides amortized Bayesian parameter inference from simulator-generated data without requiring explicit likelihood

Integrating chemical structures as treatments improves representations of microscopy images for morphological profiling

arXiv:2504.09544v3 Announce Type: replace-cross Abstract: Recent advances in self-supervised deep learning have improved our ability to quantify cellular morphological changes in high-throughput micro

tutorialsarxiv-cs-cv

Interaction-Aware Influence Functions for Group Attribution

arXiv:2605.15675v1 Announce Type: cross Abstract: Influence functions approximate how removing a training example changes a quantity of interest, called the target function, such as a held-out loss. T

Intrinsic Wasserstein Rates for Score-Based Generative Models on Smooth Manifolds

arXiv:2605.15822v1 Announce Type: new Abstract: Score-based generative models are trained in high-dimensional ambient spaces, yet many data distributions are supported on low-dimensional nonlinear str

Introducing MELI: the Mandarin-English Language Interview Corpus

model-releasesarxiv-cs-cl

arXiv:2603.27043v2 Announce Type: replace Abstract: We introduce the Mandarin-English Language Interview (MELI) Corpus, an open-source resource of 29.8 hours of speech from 51 Mandarin-English bilingu