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GRASPrune: Global Gating for Budgeted Structured Pruning of Large Language Models

arXiv:2604.19398v1 Announce Type: new Abstract: Large language models (LLMs) are expensive to serve because model parameters, attention computation, and KV caches impose substantial memory and latency

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Ground-Level Near Real-Time Modeling for PM2.5 Pollution Prediction

DGX agent

arXiv:2604.18973v1 Announce Type: cross Abstract: Air pollution is a worldwide public health threat that can cause or exacerbate many illnesses, including respiratory disease, cardiovascular disease,

safetyarxiv-cs-lg

22 Apr 2026

Guiding Distribution Matching Distillation with Gradient-Based Reinforcement Learning

arXiv:2604.19009v1 Announce Type: cross Abstract: Diffusion distillation, exemplified by Distribution Matching Distillation (DMD), has shown great promise in few-step generation but often sacrifices q

safetyarxiv-cs-cv

HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models

arXiv:2604.19300v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have recently achieved strong performance across various audio-centric tasks. However, hallucination, where models

HALO: Hybrid Auto-encoded Locomotion with Learned Latent Dynamics, Poincare Maps, and Regions of Attraction

arXiv:2604.18887v1 Announce Type: new Abstract: Reduced-order models are powerful for analyzing and controlling high-dimensional dynamical systems. Yet constructing these models for complex hybrid sys

safetyarxiv-cs-ro

Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling

arXiv:2604.18753v1 Announce Type: cross Abstract: An active challenge in developing multimodal machine learning (ML) models for healthcare is handling missing modalities during training and deployment

applicationsarxiv-cs-ai

HarDBench: A Benchmark for Draft-Based Co-Authoring Jailbreak Attacks for Safe Human-LLM Collaborative Writing

model-releasesarxiv-cs-cl

arXiv:2604.19274v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as co-authors in collaborative writing, where users begin with rough drafts and rely on LLMs to compl

HardNet++: Nonlinear Constraint Enforcement in Neural Networks

arXiv:2604.19669v1 Announce Type: new Abstract: Enforcing constraint satisfaction in neural network outputs is critical for safety, reliability, and physical fidelity in many control and decision-maki

local-aiarxiv-cs-lg

Harmful Intent as a Geometrically Recoverable Feature of LLM Residual Streams

arXiv:2604.18901v1 Announce Type: cross Abstract: Harmful intent is geometrically recoverable from large language model residual streams: as a linear direction in most layers, and as angular deviation

HarmoniDiff-RS: Training-Free Diffusion Harmonization for Satellite Image Composition

model-releasesarxiv-cs-cv

arXiv:2604.19392v1 Announce Type: new Abstract: Satellite image composition plays a critical role in remote sensing applications such as data augmentation, disaste simulation, and urban planning. We p

Has Automated Essay Scoring Reached Sufficient Accuracy? Deriving Achievable QWK Ceilings from Classical Test Theory

arXiv:2604.19131v1 Announce Type: new Abstract: Automated essay scoring (AES) is commonly evaluated on public benchmarks using quadratic weighted kappa (QWK). However, because benchmark labels are ass

Headlines You Won't Forget: Can Pronoun Insertion Increase Memorability?

arXiv:2604.19189v1 Announce Type: new Abstract: For news headlines to influence beliefs and drive action, relevant information needs to be retained and retrievable from memory. In this probing study w

researcharxiv-cs-cl

HELM: Harness-Enhanced Long-horizon Memory for Vision-Language-Action Manipulation

arXiv:2604.18791v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models fail systematically on long-horizon manipulation tasks despite strong short-horizon performance. We show that this

Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics

model-releasesarxiv-cs-lg

arXiv:2604.19451v1 Announce Type: new Abstract: Federated prognostics enable clients (e.g., companies, factories, and production lines) to collaboratively develop a failure time prediction model while

Hierarchically Robust Zero-shot Vision-language Models

arXiv:2604.18867v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) can perform zero-shot classification but are susceptible to adversarial attacks. While robust fine-tuning improves their

Highly Efficient and Effective LLMs with Multi-Boolean Architectures

arXiv:2505.22811v5 Announce Type: replace-cross Abstract: Weight binarization has emerged as a promising strategy to reduce the complexity of large language models (LLMs). Existing approaches fall int

researcharxiv-cs-lg

HMR-Net: Hierarchical Modular Routing for Cross-Domain Object Detection in Aerial Images

arXiv:2604.18866v1 Announce Type: new Abstract: Despite advances in object detection, aerial imagery remains a challenging domain, as models often fail to generalize across variations in spatial resol

local-aiarxiv-cs-cv

How Adversarial Environments Mislead Agentic AI?

arXiv:2604.18874v1 Announce Type: new Abstract: Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack

Tutorials

How Do Answer Tokens Read Reasoning Traces? Self-Reading Patterns in Thinking LLMs for Quantitative Reasoning

arXiv:2604.19149v1 Announce Type: cross Abstract: Thinking LLMs produce reasoning traces before answering. Prior activation steering work mainly targets on shaping these traces. It remains less unders

tutorialsarxiv-cs-ai

How does the optimizer implicitly bias the model merging loss landscape?

arXiv:2510.04686v2 Announce Type: replace-cross Abstract: Model merging combines independent solutions with different capabilities into a single one while maintaining the same inference cost. Two popu

How Far Are Video Models from True Multimodal Reasoning?

model-releasesarxiv-cs-cv

arXiv:2604.19193v1 Announce Type: new Abstract: Despite remarkable progress toward general-purpose video models, a critical question remains unanswered: how far are these models from achieving true mu

How Out-of-Equilibrium Phase Transitions can Seed Pattern Formation in Trained Diffusion Models

arXiv:2603.20092v3 Announce Type: replace Abstract: Diffusion models generate structure by progressively transforming noise into data, yet the mechanisms underlying this transition remain poorly under

local-aiarxiv-cs-lg

How to Teach Large Multimodal Models New Skills

arXiv:2510.08564v2 Announce Type: replace Abstract: How can we teach large multimodal models (LMMs) new skills without erasing prior abilities? We study sequential fine-tuning on five target skills wh

HoWToBench: Holistic Evaluation for LLM's Capability in Human-level Writing using Tree of Writing

model-releasesarxiv-cs-cl

arXiv:2604.19071v1 Announce Type: new Abstract: Evaluating the writing capabilities of large language models (LLMs) remains a significant challenge due to the multidimensional nature of writing skills

HP-Edit: A Human-Preference Post-Training Framework for Image Editing

arXiv:2604.19406v1 Announce Type: cross Abstract: Common image editing tasks typically adopt powerful generative diffusion models as the leading paradigm for real-world content editing. Meanwhile, alt

Human-Guided Harm Recovery for Computer Use Agents

arXiv:2604.18847v1 Announce Type: new Abstract: As LM agents gain the ability to execute actions on real computer systems, we need ways to not only prevent harmful actions at scale but also effectivel

Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning

arXiv:2604.18862v1 Announce Type: cross Abstract: Bug reports, encompassing a wide range of bug types, are crucial for maintaining software quality. However, the increasing complexity and volume of bu

applicationsarxiv-cs-ai

Hybrid Architectures for Language Models: Systematic Analysis and Design Insights

arXiv:2510.04800v3 Announce Type: replace Abstract: Recent progress in large language models demonstrates that hybrid architectures--combining self-attention mechanisms with structured state space mod

researcharxiv-cs-cl

Hybrid Task and Motion Planning with Reactive Collision Handling for Multi-Robot Disassembly of Complex Products: Application to EV Batteries

arXiv:2509.21020v2 Announce Type: replace Abstract: This paper addresses the problem of multi-robot coordination for complex manipulation task sequences. We present a vision-driven task-and-motion pla

safetyarxiv-cs-ro

Idle is the New Sleep: Configuration-Aware Alternative to Powering Off FPGA-Based DL Accelerators During Inactivity

arXiv:2407.12027v2 Announce Type: replace-cross Abstract: In the rapidly evolving Internet of Things (IoT) domain, we concentrate on enhancing energy efficiency in Deep Learning accelerators on FPGA-b

IMPACT: Importance-Aware Activation Space Reconstruction

arXiv:2507.03828v4 Announce Type: replace Abstract: Large language models (LLMs) achieve strong performance across diverse domains but remain difficult to deploy in resource-constrained environments d

applicationsarxiv-cs-lg

Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI

arXiv:2604.19578v1 Announce Type: cross Abstract: With the rapid advancement of Large Language Models (LLMs), the academic community has faced unprecedented disruptions, particularly in the realm of a

Implicit Neural Field-Based Process Planning for Multi-Axis Manufacturing: Direct Control over Collision Avoidance and Toolpath Geometry

arXiv:2511.17578v2 Announce Type: replace Abstract: Existing curved-layer-based process planning methods for multi-axis manufacturing address collisions only indirectly and generate toolpaths in a pos

applicationsarxiv-cs-ro

Improved Anomaly Detection in Medical Images via Mean Shift Density Enhancement

arXiv:2604.19191v1 Announce Type: cross Abstract: Anomaly detection in medical imaging is essential for identifying rare pathological conditions, particularly when annotated abnormal samples are limit

Improvements to the post-processing of weather forecasts using machine learning and feature selection

arXiv:2604.19340v1 Announce Type: cross Abstract: This study aims to develop and improve machine learning-based post-processing models for precipitation, temperature, and wind speed predictions using

researcharxiv-cs-lg

Improving the Distributional Alignment of LLMs using Supervision

model-releasesarxiv-cs-cl

arXiv:2507.00439v4 Announce Type: replace Abstract: The ability to accurately align LLMs with diverse population groups on subjective questions would have great value. In this work, we show that addin

IndiaFinBench: An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text

arXiv:2604.19298v1 Announce Type: cross Abstract: We introduce IndiaFinBench, to our knowledge the first publicly available evaluation benchmark for assessing large language model (LLM) performance on

Inductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning

arXiv:2604.19186v1 Announce Type: cross Abstract: Heterophily is a prevalent property of real-world graphs and is well known to impair the performance of homophilic Graph Neural Networks (GNNs). Prior

local-aiarxiv-cs-ai

Industrial Surface Defect Detection via Diffusion Generation and Asymmetric Student-Teacher Network

arXiv:2604.19240v1 Announce Type: new Abstract: Industrial surface defect detection often suffers from limited defect samples, severe long-tailed distributions, and difficulties in accurately localizi

local-aiarxiv-cs-ai

InHabit: Leveraging Image Foundation Models for Scalable 3D Human Placement

arXiv:2604.19673v1 Announce Type: new Abstract: Training embodied agents to understand 3D scenes as humans do requires large-scale data of people meaningfully interacting with diverse environments, ye

applicationsarxiv-cs-cv

InsideOut: Measuring and Mitigating Insider-Outsider Bias in Interview Script Generation

arXiv:2509.21080v2 Announce Type: replace-cross Abstract: Advancements in Large language models (LLMs) have enabled a variety of downstream applications like story and interview script generation. How

Integrating Anomaly Detection into Agentic AI for Proactive Risk Management in Human Activity

arXiv:2604.19538v1 Announce Type: new Abstract: Agentic AI, with goal-directed, proactive, and autonomous decision-making capabilities, offers a compelling opportunity to address movement-related risk

Intentional Updates for Streaming Reinforcement Learning

arXiv:2604.19033v1 Announce Type: cross Abstract: In gradient-based learning, a step size chosen in parameter units does not produce a predictable per-step change in function output. This often leads

Investigating Counterfactual Unfairness in LLMs towards Identities through Humor

arXiv:2604.18729v1 Announce Type: new Abstract: Humor holds up a mirror to social perception: what we find funny often reflects who we are and how we judge others. When language models engage with hum

safetyarxiv-cs-cl

Investigating the structure of emotions by analyzing similarity and association of emotion words

arXiv:2602.06430v2 Announce Type: replace-cross Abstract: In the field of natural language processing, some studies have attempted sentiment analysis on text by handling emotions as explanatory or res

IonMorphNet: Generalizable Learning of Ion Image Morphologies for Peak Picking in Mass Spectrometry Imaging

arXiv:2604.19369v1 Announce Type: new Abstract: Peak picking is a fundamental preprocessing step in Mass Spectrometry Imaging (MSI), where each sample is represented by hundreds to thousands of ion im

researcharxiv-cs-cv

Tutorials

IR-Flow: Bridging Discriminative and Generative Image Restoration via Rectified Flow

arXiv:2604.19680v1 Announce Type: new Abstract: In image restoration, single-step discriminative mappings often lack fine details via expectation learning, whereas generative paradigms suffer from ine

tutorialsarxiv-cs-cv

Joint Magnetometer-IMU Calibration via Maximum A Posteriori Estimation

arXiv:2505.16662v4 Announce Type: replace Abstract: This paper presents a new method for jointly calibrating a magnetometer and inertial measurement unit (IMU), focusing on balancing calibration accur

applicationsarxiv-cs-ro