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arXiv:2605.19178v1 Announce Type: cross Abstract: The great success of neural networks in recognizing hidden patterns and correlations in complex data lies in the way they take advantage of the large

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Agents

Agentic Discovery of Cryomicroneedle Formulations

DGX agent

arXiv:2605.19677v1 Announce Type: new Abstract: Cryomicroneedles offer a route to minimally invasive intradermal delivery of living cells, but their cryogenic formulations must reconcile cell protecti

agentsarxiv-cs-lg

20 May 2026

ALDEN: Boosting Private Data Extraction from Retrieval-Augmented Generation Systems via Active Learning and Distribution Estimation

arXiv:2605.18762v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is widely used to augment large language models with external knowledge retrieval to improve reliability and gene

Are Rationales Necessary and Sufficient? Tuning LLMs for Explainable Misinformation Detection

arXiv:2605.19285v1 Announce Type: cross Abstract: The rapid spread of misinformation on social media platforms has become a formidable challenge. To mitigate its proliferation, Misinformation Detectio

Beyond Extrapolation: Knowledge Utilization Paradigm with Bidirectional Inspiration for Time Series Forecasting

arXiv:2605.19249v1 Announce Type: new Abstract: Time-series forecasting is critical in various scenarios, such as energy, transportation, and public health. However, most existing forecasters rely pri

safetyarxiv-cs-lg

CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection

arXiv:2605.20032v1 Announce Type: new Abstract: Text-attributed graph fraud detection (TAGFD) plays a critical role in preventing fraudulent activities on online social and e-commerce platforms. Howev

researcharxiv-cs-lg

Data-driven Acceleration of MPC with Guarantees

arXiv:2511.13588v2 Announce Type: replace-cross Abstract: Model Predictive Control (MPC) is a powerful framework for optimal control but can be too slow for low-latency applications. We present a data

Data-Efficient Self-Supervised Algorithms for Fine-Grained Birdsong Analysis

arXiv:2511.12158v3 Announce Type: replace Abstract: Research in bioacoustics, neuroscience, and linguistics often uses birdsong as a proxy to acquire knowledge across diverse areas. This requires audi

applicationsarxiv-cs-lg

Data-Free Client Contribution Estimation via Logit Maximization for Federated Learning

arXiv:2605.18892v1 Announce Type: cross Abstract: Federated learning (FL) enables collaborative learning of computer vision models, where privacy and regulatory constraints prevent centralizing data a

DynaTok: Temporally Adaptive and Positional Bias-Aware Token Compression for Video-LLMs

arXiv:2605.19322v1 Announce Type: new Abstract: Recent advances in Video Large Language Models (Video-LLMs) have greatly expanded multimodal reasoning capabilities. However, the massive number of visu

ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation

arXiv:2602.04279v2 Announce Type: replace Abstract: Electrocardiography (ECG) serves as an indispensable diagnostic tool in clinical practice, yet existing multimodal large language models (MLLMs) rem

researcharxiv-cs-cl

EmbGen: Teaching with Reassembled Corpora

arXiv:2605.19394v1 Announce Type: cross Abstract: Adapting small instruction-tuned models to specialized domains often relies on supervised fine-tuning (SFT) on curated instruction-response examples,

Exploiting Non-Negativity in DAG Structure Learning

arXiv:2605.19947v1 Announce Type: new Abstract: This work addresses the problem of learning directed acyclic graphs (DAGs) from nodal observations generated by a linear structural equation model. DAG

applicationsarxiv-cs-lg

From Refusal to Recovery: A Control-Theoretic Approach to Generative AI Guardrails

arXiv:2510.13727v2 Announce Type: replace Abstract: Generative AI systems are increasingly assisting and acting on behalf of end users in practical settings, from digital shopping assistants to next-g

Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization

arXiv:2605.20145v1 Announce Type: cross Abstract: Bayesian optimization (BO) selects evaluation points for expensive black-box objectives using Gaussian process (GP) predictive distributions. Kernel c

researcharxiv-cs-lg

HiDe: Rethinking The Zoom-IN method in High Resolution MLLMs via Hierarchical Decoupling

arXiv:2510.00054v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have made significant strides in visual understanding tasks. However, their performance on high-resol

Interoceptive Divergence in Aesthetic Evaluation and Implications for Human-AI Alignment

arXiv:2605.18759v1 Announce Type: cross Abstract: Artificial intelligence (AI), exemplified by large language models (LLMs), is rapidly approaching and in some cases surpassing human performance acros

Tutorials

Latent Laplace Diffusion for Irregular Multivariate Time Series

arXiv:2605.19805v1 Announce Type: cross Abstract: Irregular multivariate time series impose a trade-off for long-horizon forecasting: discrete methods can distort temporal structure via re-gridding, w

tutorialsarxiv-cs-ai

Learning Long-Term Temporal Dependencies in Photovoltaic Power Output Prediction Through Multi-Horizon Forecasting

arXiv:2605.19074v1 Announce Type: cross Abstract: The rapid global expansion of solar photovoltaic (PV) capacity-reaching a record 597 GW in 2024-highlights the urgent need for robust forecasting mode

LLM-Based Financial Sentiment Analysis in Arabic: Evidence from Saudi Markets

arXiv:2605.19714v1 Announce Type: new Abstract: Investor sentiment shapes financial markets, yet modeling sentiment in Arabic financial contexts remains challenging due to linguistic complexity and li

researcharxiv-cs-cl

Locate-then-Sparsify: Attribution Guided Sparse Strategy for Visual Hallucination Mitigation

arXiv:2603.16284v2 Announce Type: replace Abstract: Despite the significant advancements in Large Vision-Language Models (LVLMs), their tendency to generate hallucinations undermines reliability and r

researcharxiv-cs-cv

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution

arXiv:2512.05958v2 Announce Type: replace-cross Abstract: Generative search engines based on large language models (LLMs) are replacing traditional search, fundamentally changing how information provi

Mind Your Moras: Orthography-Aware Error Analysis of Neural Japanese Morphological Generation

arXiv:2605.20043v1 Announce Type: new Abstract: We present an orthography-aware error analysis of Japanese past-tense morphological inflection, treating hiragana not merely as a transcriptional medium

researcharxiv-cs-cl

MMoA: An AI-Agent framework with recurrence for Memoried Mixure-of-Agent

arXiv:2605.19194v1 Announce Type: new Abstract: The Mixture-of-Agents (MoA) framework has shown promise in improving large language model (LLM) performance by aggregating outputs from multiple agents.

agentsarxiv-cs-cl

MO-CAPO: Multi-Objective Cost-Aware Prompt Optimization

arXiv:2605.18869v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong performance across a wide range of tasks but are highly sensitive to prompt design, motivating the need fo

Multi-Headed Transformer Architectures as Time-dependent Wasserstein Gradient Flows

arXiv:2605.18870v1 Announce Type: new Abstract: In recent years, transformer architectures have revolutionized the field of language processing, opening the door to previously unforeseen possibilities

agentsarxiv-cs-lg

Multilayer Correlation Clustering

arXiv:2404.16676v2 Announce Type: replace-cross Abstract: We establish Multilayer Correlation Clustering, a novel generalization of Correlation Clustering to the multilayer setting. In this model, we

applicationsarxiv-cs-lg

OEP: Poisoning Self-Evolving LLM Agents via Locally Correct but Non-Transferable Experiences

arXiv:2605.18930v1 Announce Type: cross Abstract: Memory-augmented large language model (LLM) agents use iterative reflection and self-evolution to solve complex tasks, but these mechanisms introduce

local-aiarxiv-cs-ai

On-Device Continual Learning with Dual-Stage Buffer and Dynamic Loss for Point-of-Care Pneumonia Diagnosis

arXiv:2605.19201v1 Announce Type: cross Abstract: Deep learning models detect pneumonia from chest X-rays with high accuracy, but the performance declines under domain shifts caused by differences in

local-aiarxiv-cs-ai

Open-Set Domain Adaptation Under Background Distribution Shift: Challenges and A Provably Efficient Solution

arXiv:2512.01152v4 Announce Type: replace-cross Abstract: As we deploy machine learning systems in the real world, a core challenge is to maintain a model that is performant even as the data shifts. S

applicationsarxiv-cs-ai

PAVE: A Cognitive Architecture for Legitimate Violation in Generative Agent Societies

arXiv:2605.19351v1 Announce Type: cross Abstract: Generative agents based on large language models reproduce believable human behavior in cooperative settings, but how they should reason in situations

PEEK: Context Map as an Orientation Cache for Long-Context LLM Agents

arXiv:2605.19932v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate over long and recurring external contexts, like document corpora and code repositories. Across in

Hardware

PiKV: KV Cache Management System for Mixture of Experts

arXiv:2508.06526v3 Announce Type: replace-cross Abstract: As large-scale language models continue to scale up in both size and context length, the memory and communication cost of key-value (KV) cache

hardwarearxiv-cs-ai

Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas

arXiv:2605.19685v1 Announce Type: cross Abstract: Accurately assessing financial risk requires capturing both individual asset volatility and the complex, asymmetric dependence structures that emerge

safetyarxiv-cs-lg

Probability-Conserving Flow Guidance

arXiv:2605.20079v1 Announce Type: cross Abstract: Diffusion and flow-based generative models dominate visual synthesis, with guidance aligning samples to user input and improving perceptual quality. H

Probing Embodied LLMs: When Higher Observation Fidelity Hurts Problem Solving

arXiv:2605.20072v1 Announce Type: new Abstract: Large Language Models are increasingly proposed as cognitive components for robotic systems, yet their opaque decision processes make it difficult to ex

ReCrit: Transition-Aware Reinforcement Learning for Scientific Critic Reasoning

arXiv:2605.18799v1 Announce Type: cross Abstract: Large language models can fail in critic interaction not only by answering incorrectly, but also by abandoning an initially correct scientific solutio

Robust Checkpoint Selection for Multimodal LLMs via Agentic Evaluation and Stability-Aware Ranking

arXiv:2605.18852v1 Announce Type: cross Abstract: Checkpoint selection for multimodal large language models (MLLMs) presents significant challenges when performance differentials are marginal and eval

SafeAlign-VLA: A Negative-Enhanced Safe Alignment Framework for Risk-Aware Autonomous Driving

arXiv:2605.19524v1 Announce Type: cross Abstract: End-to-end autonomous driving systems excel in common scenarios but struggle with safety-critical long-tail cases. Vision-Language-Action (VLA) models

Self-Creative Text-to-Object Generation using Semantic-Aware Spatial Weighting

arXiv:2605.19554v1 Announce Type: new Abstract: Instilling creativity in text-to-image (T2I) generation presents a significant challenge, as it requires synthesized images to exhibit not only visual n

Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM

arXiv:2505.17726v3 Announce Type: replace-cross Abstract: Recently, multimodal large language models (MLLMs) have emerged as a key approach in achieving artificial general intelligence. In particular,

local-aiarxiv-cs-ai

Stage-adaptive Token Selection for Efficient Omni-modal LLMs

arXiv:2605.20035v1 Announce Type: new Abstract: Omni-modal large language models (om-LLMs) achieve unified audio-visual understanding by encoding video and audio into temporally aligned token sequence

researcharxiv-cs-cv

STRIDE: Learnable Stepwise Language Feedback for LLM Reasoning

arXiv:2605.18851v1 Announce Type: new Abstract: Recent advances in Reinforcement Learning (RL) have underscored its potential for incentivizing reasoning capabilities of Large Language Models (LLMs).

local-aiarxiv-cs-lg

Structural Energy Guidance for View-Consistent Text-to-3D Generation

arXiv:2605.19876v1 Announce Type: new Abstract: Text-to-3D generation based on diffusion models often suffers from the Janus problem, leading to inconsistent geometry across viewpoints. This work iden

The Insurability Frontier of AI Risk: Mapping Threats to Affirmative Coverage, Silent Exposures, and Exclusions

arXiv:2605.18784v1 Announce Type: cross Abstract: The rapid diffusion of agentic AI has created a new coverage problem for commercial insurance: some AI-mediated losses are now affirmatively insured,

TORQ: Two-Level Orthogonal Rotation for MXFP4 Quantization

arXiv:2605.19561v1 Announce Type: cross Abstract: As Large Language Models (LLMs) advance toward practical deployment, the Microscaling FP4 (MXFP4) format has emerged as a cornerstone for next-generat

Toward Training Superintelligent Software Agents through Self-Play SWE-RL

arXiv:2512.18552v2 Announce Type: replace-cross Abstract: While current software agents powered by large language models (LLMs) and agentic reinforcement learning (RL) can boost programmer productivit

Towards LLM-Assisted Architecture Recovery for Real-World ROS~2 Systems: An Agent-Based Multi-Level Approach to Hierarchical Structural Architecture Reconstruction

arXiv:2605.20055v1 Announce Type: cross Abstract: Explicit software architecture models are essential artifacts for communicating, analyzing, and evolving complex software-intensive systems. In ROS~2-