Search: “papers”

A Dataset of Robot-Patient and Doctor-Patient Medical Dialogues for Spoken Language Processing Tasks

arXiv:2605.26747v1 Announce Type: new Abstract: Large Language Models (LLMs) have brought huge improvements to Artificial Intelligence (AI), which can be applied to general-purpose tasks. However, the

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A Hybrid Vision-Language Architecture for Automated Defect Reasoning and Report Generation in Industrial Inspection

DGX agent

arXiv:2605.26533v1 Announce Type: cross Abstract: Automated industrial inspection requires both precise defect localization and structured maintenance report generation; in current practice these task

model-releasesarxiv-cs-ai

27 May 2026

A Multivariate Bernoulli-Based Sampling Method for Multi-Label Data with Application to Meta-Research

arXiv:2512.08371v4 Announce Type: replace Abstract: Datasets may contain observations with multiple labels. If the labels are not mutually exclusive, and if the labels vary greatly in frequency, obtai

researcharxiv-cs-lg

A Physics-Informed Hierarchical Neural Network for Microwave Scattering Analysis of 3D PEC Targets

arXiv:2508.03774v5 Announce Type: replace-cross Abstract: Accurate modeling of scattering from three-dimensional (3D) perfectly electrically conducting (PEC) targets at microwave frequencies constitut

Adaptive Multi-prompt Contrastive Network for Few-shot Out-of-distribution Detection

arXiv:2506.17633v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection attempts to distinguish outlier samples to prevent models trained on the in-distribution (ID) dataset from

AdaSD: Adaptive Speculative Decoding for Efficient Language Model Inference

model-releasesarxiv-cs-cl

arXiv:2512.11280v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable performance across a wide range of tasks, but their increasing parameter sizes significantly s

Adversarial Water-Filling: Theory, Algorithms and Foundation Model

arXiv:2605.26163v1 Announce Type: cross Abstract: Competitive resource allocation problems over frequency and space can be formulated as minimax interaction between transmit power and worst-case inter

tutorialsarxiv-cs-lg

AI-T2I: Aggregating-and-Isolating Cross-Attention to Diffusion Models for Text-to-Image Synthesis

arXiv:2605.25763v2 Announce Type: replace Abstract: Text-to-image synthesis has made significant progress, benefiting from the strong generative capabilities of diffusion models. However, these models

safetyarxiv-cs-cv

Amortized Factor Inference Networks for Posterior Inference

arXiv:2605.26419v1 Announce Type: new Abstract: Amortized inference promises fast test-time Bayesian inference, but existing methods are inherently tied to fixed models. Extending amortization to unse

researcharxiv-cs-lg

An End-to-End Learning Approach for Solving Capacitated Location-Routing Problems

arXiv:2511.02525v2 Announce Type: replace-cross Abstract: The capacitated location-routing problems (CLRPs) are classical problems in combinatorial optimization, which require simultaneously making lo

An investigation of AI integration in sound designer workflows and experiences

arXiv:2605.27174v1 Announce Type: cross Abstract: Artificial intelligence is increasingly being integrated into professional audio production workflows, yet a gap persists between the tools developers

tutorialsarxiv-cs-ai

Augment Engineering: A Methodology for Multi-Tool AI Orchestration Across Professional Domains

arXiv:2605.26146v1 Announce Type: cross Abstract: Organizations increasingly deploy separate purpose-built AI tools across professional domains, often hiring domain specialists for each, recreating th

Beyond Binary: Turning Partial Success into Dense Verifiable Rewards for Reinforcement Learning in Code Generation

arXiv:2601.03525v3 Announce Type: replace-cross Abstract: Effective reward design is a central challenge in Reinforcement Learning (RL) for code generation. Mainstream test-suite-level outcome rewards

Beyond Questions: Evaluating What Large Language Models (Actually) Know

arXiv:2605.26937v1 Announce Type: cross Abstract: Parametric knowledge in large language models (LLMs) is a cornerstone of their success, yet remains poorly understood. Existing knowledge benchmarks t

Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems

arXiv:2502.14321v3 Announce Type: replace-cross Abstract: Large language model-based multi-agent systems have recently gained significant attention due to their potential for complex, collaborative, a

agentsarxiv-cs-cl

Beyond the Data Mesh Illusion: Designing Modern AI-augmented Lakehouses to Bridge the Gap Between Theory and Practice

arXiv:2605.27131v1 Announce Type: cross Abstract: Enterprise data platforms face an enduring tension between domain self-service and holistic governance. The data mesh paradigm proposed decentralized

Black-box Membership Inference Attacks on the Pre-training Data of Image-generation Models

arXiv:2605.27020v1 Announce Type: cross Abstract: The rapid advancement of diffusion-based image generation models has raised serious concerns regarding potential copyright and privacy infringements i

Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling

arXiv:2605.27023v1 Announce Type: new Abstract: Knowledge graphs (KGs) have become the core backbone of numerous downstream tasks such as question answering and recommender systems. However, despite a

Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection

arXiv:2605.26193v1 Announce Type: cross Abstract: Time series anomaly detection (TSAD) has long been a hot research topic in data mining due to its various applications. Recent studies challenge the e

Local Ai

CART Random Forests as Sequential Allocation over Random Opportunity Sets: A Stochastic-Control Theory of Ensemble Risk

arXiv:2605.26675v1 Announce Type: cross Abstract: CART random forests are among the most widely used modern predictive methods, with well-documented empirical success. Yet, at the mechanistic level, t

local-aiarxiv-cs-lg

Chain Of Thought Compression: A Theoretical Analysis

arXiv:2601.21576v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) has unlocked advanced reasoning abilities of Large Language Models (LLMs) with intermediate steps, yet incurs prohibitive com

Chaos-SSL: An Attention-Based Self-Supervised Learning Framework with Chaotic Transformation for Medical Image Classification

arXiv:2605.27146v1 Announce Type: new Abstract: Self-Supervised Learning (SSL) has emerged as a powerful paradigm to mitigate the reliance on large, annotated datasets, a common bottleneck in medical

tutorialsarxiv-cs-cv

CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learning

model-releasesarxiv-cs-lg

arXiv:2502.03946v5 Announce Type: replace Abstract: Data preprocessing is often paid little attention in machine learning, despite its potentially significant impact on model performance. While automa

CNNs, Transformers, Hybrid, and Vision Language Models for Skin Cancer Detection

model-releasesarxiv-cs-cv

arXiv:2605.26294v1 Announce Type: new Abstract: Skin cancer is a common and fast rising malignancy worldwide. Early detection is critical for improving outcomes. Deep learning models trained on dermos

Conceptual Schema Inference for Tabular Datasets using Large Language Models

arXiv:2509.04632v2 Announce Type: replace-cross Abstract: Large collections of tabular data from data lakes, web tables and open data portals often originate from heterogeneous sources, leading to rep

Confounder Detection via Treatment Intent: A New Observational Study Design

arXiv:2605.26413v1 Announce Type: cross Abstract: Understanding the effects of interventions is central to scientific progress, with randomized controlled trials (RCTs) regarded as the gold standard f

Constrained Bayesian Experimental Design via Online Planning

arXiv:2605.26990v1 Announce Type: cross Abstract: Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential experiments. However, existing BED methods are un

safetyarxiv-cs-lg

Cordyceps: Covert Control Attacks on LLMs via Data Poisoning

arXiv:2605.26595v1 Announce Type: cross Abstract: Large language models (LLMs) are often fine-tuned on uncurated text datasets that adversaries can poison. Existing poisoning attacks primarily rely on

CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection

arXiv:2405.16417v2 Announce Type: replace Abstract: Recent vision-language pre-trained models (VL-PTMs) have shown remarkable success in open-vocabulary tasks. However, downstream use cases often invo

tutorialsarxiv-cs-cv

Curriculum Learning for Safety Alignment

arXiv:2605.26315v1 Announce Type: cross Abstract: Direct Preference Optimisation (DPO) is widely used for safety alignment in large language models. However, prior work shows it is brittle and exhibit

Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review

arXiv:2504.08540v2 Announce Type: replace Abstract: Accurate lane detection is essential for automated driving, enabling safe and reliable vehicle navigation across a variety of road scenarios. Numero

agentsarxiv-cs-cv

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection

arXiv:2605.26446v1 Announce Type: cross Abstract: Graph anomaly detection (GAD) aims to identify nodes or substructures whose behavior or attributes deviate significantly from the overall pattern in g

Deep-layer limit and stability analysis of the basic forward-backward-splitting induced network (II): learning problems

arXiv:2605.27133v1 Announce Type: cross Abstract: Deep unfolding neural networks derived from iterative optimization schemes and numerical ordinary/partial differential equations (ODEs/PDEs) have attr

Design First, Code Later: Aesthetically Pleasing Template-Free Slides Generation

arXiv:2605.26451v1 Announce Type: cross Abstract: Producing presentation slides automatically entails coordinating narrative structure with page-level graphic design under strict spatial constraints.

agentsarxiv-cs-cv

Developing a Totally Unimodular Linear Program for Optimal Conformance Checking: When and Why It Complements A*

arXiv:2605.26938v1 Announce Type: new Abstract: Alignment-based conformance checking is the state-of-the-art approach for comparing observed process executions with normative process models. The stand

Do Modern Post-Hoc Watermarking Methods Beat Broken-Arrows?

arXiv:2605.27135v1 Announce Type: cross Abstract: With the rapid proliferation of generative models, such as diffusion models, digital watermarking has emerged as a crucial solution for identifying AI

applicationsarxiv-cs-cv

Efficient Agentic Reinforcement Learning with On-Policy Intrinsic Knowledge Boundary Enhancement

arXiv:2605.26952v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) has proven effective for training LLM-based agents with external tool-use capabilities. However, we identify that ag

safetyarxiv-cs-cl

Examining the Challenges of Intellectual Property in AI-Generated Productions

arXiv:2605.26590v1 Announce Type: cross Abstract: With the advancement of artificial intelligence systems capable of autonomously generating artistic, literary, musical works, and even inventions with

Exploring the robustness of TractOracle methods in RL-based tractography

arXiv:2507.11486v2 Announce Type: replace Abstract: Tractography algorithms leverage diffusion MRI to reconstruct the fibrous architecture of the brain's white matter. Among machine learning approache

researcharxiv-cs-lg

FalAR: A Large-scale Speaker-Annotated European Portuguese Speech Corpus of Parliamentary Sessions

arXiv:2605.27062v1 Announce Type: new Abstract: State-of-the-art performance for Automatic Speech Recognition (ASR) largely depends on the availability of large-scale labeled corpora. This creates a d

safetyarxiv-cs-cl

For over a decade, we’ve accepted that end-to-end backprop is the only way to train deep networks. But holding the entire network in memory …

For over a decade, we’ve accepted that end-to-end backprop is the only way to train deep networks. But holding the entire network in memory all at once is why AI training is hitting a resource wall. W

researchdavid-ha--x

FoundObj: Self-supervised Foundation Models as Rewards for Label-free 3D Object Segmentation

arXiv:2605.27178v1 Announce Type: cross Abstract: We address the challenging task of 3D object segmentation in complex scene point clouds without relying on any scene-level human annotations during tr

agentsarxiv-cs-ai

Frequency-Guided Fusion For RGB-Thermal Semantic Segmentation

arXiv:2605.26273v1 Announce Type: new Abstract: Semantic segmentation in complex environments such as urban driving scenes remains challenging under adverse lighting conditions, where RGB images alone

researcharxiv-cs-cv

From Norms to Indicators (N2I-RAG): An Agentic Retrieval-Augmented Generation Framework for Legal Indicator Computation

arXiv:2605.26926v1 Announce Type: new Abstract: Computing legal indicators from normative texts is a key task in legal monitoring and policy evaluation, but presents significant challenges due to the

Gaussian-Voxel Duet: A Dual-Scaffolding Hybrid Representation for Fast and Accurate Monocular Surface Reconstruction

arXiv:2605.26616v1 Announce Type: new Abstract: While 3D Gaussian Splatting has achieved remarkable success in photorealistic novel view synthesis, its pursuit of fast and high-fidelity 3D reconstruct

applicationsarxiv-cs-cv

Generating Logically Consistent Synthetic Supply Chain Data with LLM-Driven Knowledge Graph Reasoning

arXiv:2605.26823v1 Announce Type: new Abstract: Synthetic data offers a promising solution to two persistent barriers in supply chain analytics: data scarcity and data privacy. However, for synthetic

tutorialsarxiv-cs-cl

GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing

arXiv:2605.27360v1 Announce Type: cross Abstract: Cellular research and development (R&D) is throttled by six structural processes that each consume months of manual engineering work per iteration: (i

agentsarxiv-cs-ai

Get a state of the art protein structure prediction in just 9 lines of code. No MSAs needed.

Get a state of the art protein structure prediction in just 9 lines of code. No MSAs needed. I'm so excited to show the world what we've been working on the for the past months!! I'm going to highligh

researchyann-lecun--x