Search: “applications”

Bound-Constrained Sparse Representation for Electrical Impedance Tomography

arXiv:2605.28392v1 Announce Type: new Abstract: This study proposes a bound-constrained sparse representation (BC-SR) framework for electrical impedance tomography (EIT), aimed at improving conductivi

researcharxiv-cs-cv

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Breaking the Script Barrier: Enabling Automatic Alignment for PoS-based ASR Error Analysis in Non-Latin Scripts

DGX agent

arXiv:2605.28438v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) systems are commonly evaluated using aggregate metrics such as Word Error Rate (WER), which do not capture the lingui

safetyarxiv-cs-cl

28 May 2026

CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation

arXiv:2412.08052v2 Announce Type: replace Abstract: Off-policy evaluation (OPE) is critical for applying contextual bandit algorithms to high-stakes decision-making settings such as healthcare, where

Causal Machine Learning: A Survey and Open Problems

arXiv:2206.15475v3 Announce Type: replace Abstract: Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a structural causa

CubePart: An Open-Vocabulary Part-Controllable 3D Generator

arXiv:2605.28763v1 Announce Type: new Abstract: Interactive 3D assets used in games and simulation are typically decomposed into specific semantic parts to support animation, physics, and scripted beh

Decision-focused learning for optimal PV-Battery scheduling

arXiv:2605.28340v1 Announce Type: cross Abstract: The use of residential photovoltaics has increased dramatically in recent years. With battery systems becoming more affordable, the optimal operation

Agents

Discovery Agents for Real-Time Analytics: Toward Proactive Insight Systems

arXiv:2605.27571v1 Announce Type: new Abstract: Modern analytics systems are fundamentally reactive, requiring users to define queries over increasingly complex and continuously evolving data. In real

agentsarxiv-cs-ai

Disentangling Adversarial Prompts: A Semantic-Graph Defense for Robust LLM Security

arXiv:2605.27823v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulti

Dynamic Topic Modeling with a Higher-Order Hypergraphical Representation

arXiv:2605.28269v1 Announce Type: new Abstract: Dynamic topic modeling is widely used to analyze evolving trends in scientific literature, medical records, and social media. Traditional topic models r

Efficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning

arXiv:2605.28409v1 Announce Type: new Abstract: Post-training using online reinforcement learning (RL) is an important training step for LLMs, including code-generating models. However, online RL for

EgoBench: An Interactive Egocentric Multimodal Benchmark for Tool-Using Agents

arXiv:2605.27820v1 Announce Type: new Abstract: As AI agents increasingly operate in open, real-world environments, they require a deep synergy of multimodal perception, tool invocation with multi-hop

EgoRelight: Egocentric Human Capture and Illumination Recovery for Relightable and Photoreal Avatar Rendering

arXiv:2605.28401v1 Announce Type: new Abstract: Mixed Reality (MR) headsets promise a future of immersive telepresence where virtual humans blend indistinguishably into real or virtual surroundings. A

researcharxiv-cs-cv

ESC-Skills: Discovering and Self-Evolving Skills for Emotional Support Conversations

arXiv:2605.27908v1 Announce Type: cross Abstract: Existing emotional support conversation (ESC) systems mainly rely on end-to-end response generation or coarse strategy supervision, offering limited i

Evolving and Detecting Multi-Turn Deception using Geometric Signatures

arXiv:2605.27671v1 Announce Type: cross Abstract: Safety defenses for large language models (LLMs) are typically trained and evaluated on single-turn prompts, yet real attacks often unfold as indirect

Explanation Generation for Contradiction Reconciliation with LLMs

arXiv:2603.22735v2 Announce Type: replace Abstract: Existing NLP work commonly treats contradictions as errors to be resolved by choosing which statements to accept or discard. Yet a key aspect of hum

researcharxiv-cs-cl

FedMPT: Federated Multi-label Prompt Tuning of Vision-Language Models

arXiv:2605.28347v1 Announce Type: new Abstract: Multi-Label Recognition (MLR) based on Vision-Language Models (VLMs) aims to leverage their pre-trained knowledge to better adapt complex recognition sc

FLORO: A Multimodal Geospatial Foundation Model for Ecological Remote Sensing Across Sensors and Scales

arXiv:2605.28174v1 Announce Type: cross Abstract: Foundation models offer a promising route to transferable remote sensing representations, but many current approaches depend on very large pretraining

GeneralThinker: Domain-General Reasoning through Likelihood-Guided Answer-Conditioned Optimization

arXiv:2605.27934v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves language model reasoning, but its reliance on domain-specific verifiers, sparse outcome rewards,

safetyarxiv-cs-cl

GraphLit: Learning Text-Enriched Dynamic Character Network Representations for Literary Study

arXiv:2605.28643v1 Announce Type: new Abstract: Methods to represent literary texts as graphs or sequences of graphs mainly focus on representing character interactions, and often overlook another cru

researcharxiv-cs-cl

Grimlock: Guarding High-Agency Systems with eBPF and Attested Channels

arXiv:2605.27488v1 Announce Type: cross Abstract: Agentic systems increasingly run user-authored orchestration code that invokes tools, spawns subtasks, and delegates work across machines and clouds.

Heterogeneous Causal Discovery of Repeated Undesirable Health Outcomes

arXiv:2503.11477v2 Announce Type: replace Abstract: Understanding the factors that trigger or prevent undesirable health outcomes across patient subpopulations is essential for designing targeted inte

Improving Requirements Classification with SMOTE-Tomek Preprocessing

arXiv:2501.06491v3 Announce Type: replace-cross Abstract: This study emphasizes the domain of requirements engineering by applying the SMOTE-Tomek preprocessing technique, combined with stratified K-f

Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals

arXiv:2605.27583v1 Announce Type: new Abstract: Electrocardiograms (ECGs) are widely used non-invasive measurements of cardiac activity and play a central role in clinical diagnosis. Recent multimodal

Inpainting-Style Conditional Diffusion for Multivariable Time Series Forecasting

arXiv:2605.28324v1 Announce Type: new Abstract: In this paper, we propose a novel conditional diffusion-based framework for multivariable time-series solar power forecasting. The proposed method refor

Tutorials

Inversely Learning Transferable Rewards via Abstracted States

arXiv:2501.01669v4 Announce Type: replace Abstract: Inverse reinforcement learning (IRL) has progressed significantly toward accurately learning the underlying rewards in both discrete and continuous

tutorialsarxiv-cs-lg

Agents

Large Language Models as Automatic Annotators and Annotation Adjudicators for Fine-Grained Opinion Analysis

arXiv:2601.16800v3 Announce Type: replace Abstract: Fine-grained opinion analysis of text provides a detailed understanding of expressed sentiments, including the addressed entity. Although this level

agentsarxiv-cs-cl

Learning to Translate from Soft to Hard LLM Prompts

model-releasesarxiv-cs-cl

arXiv:2605.27642v1 Announce Type: new Abstract: Soft prompt tuning is a parameter-efficient method for adapting LLMs to specific tasks, but suffers from a lack of interpretability. Building on recent

LiDDA: Data Driven Attribution at LinkedIn

arXiv:2505.09861v3 Announce Type: replace-cross Abstract: Data Driven Attribution, which assigns conversion credits to marketing interactions based on causal patterns learned from data, is the foundat

LV-OSD: Language-Vision-Complementary Open-Set Object Detection

arXiv:2605.28271v1 Announce Type: new Abstract: Object detection is an important task in computer vision, which aims to detect the objects of interest. through the given category list or query images.

Local Ai

Machine learning enables experimental access to photon-by-photon arrival times in scintillation detectors

arXiv:2605.27937v1 Announce Type: cross Abstract: Scintillation detectors with excellent timing resolution enable more precise localization of radiation sources in positron emission tomography, leadin

local-aiarxiv-cs-lg

Mag-VLA: Vision-Language-Action Model for Bimanual Magnetically Actuated Microrobot Manipulation

arXiv:2605.28486v1 Announce Type: new Abstract: Magnetically actuated microrobots have been used as wireless, non-contact manipulation tools at microscales, making them promising for minimally invasiv

safetyarxiv-cs-ro

Magnet-Based Soft Robotic Skin Using a 3D-Printed Multi-Lattice Structure and CNN-Based Tactile Super-Resolution

arXiv:2605.28352v1 Announce Type: new Abstract: This paper presents a magnet-based robotic skin that integrates a multilayer soft lattice with distributed Hall-effect sensor arrays and a tactile super

researcharxiv-cs-ro

Mahalanobis PatchCore: Covariance-Aware and Streaming-Compatible Industrial Anomaly Detection

arXiv:2605.27748v1 Announce Type: cross Abstract: Industrial visual anomaly detection is usually one-class: normal images are abundant, while defects are rare, heterogeneous, and often unavailable dur

Tutorials

Measure-to-measure Regression with Transformers

arXiv:2605.28075v1 Announce Type: new Abstract: Many learning problems require predicting how populations evolve under an unknown transformation. A natural representation for such populations is a pro

tutorialsarxiv-cs-lg

Multi-Adapter Representation Interventions via Energy Calibration

arXiv:2605.28722v1 Announce Type: new Abstract: Representation intervention has emerged as a promising paradigm for aligning large language models toward desired behaviors without modifying model weig

Agents

Multi-Agent LLM-based Metamorphic Testing for REST APIs

arXiv:2605.28321v1 Announce Type: cross Abstract: As REST APIs become an increasingly significant part of software systems, their validation is becoming more critical. Hence, testing and uncovering un

agentsarxiv-cs-ai

Natural Functional Gradients for Smooth Trajectory Optimization

arXiv:2605.28202v1 Announce Type: new Abstract: Generating collision-free and smooth motions remains a central challenge in robotic manipulation, particularly in cluttered environments and narrow pass

researcharxiv-cs-ro

Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity

arXiv:2605.27385v1 Announce Type: cross Abstract: Federated reinforcement learning (FedRL) enables multiple agents to collaboratively train a global policy without sharing raw data, making it ideal fo

Picid: A Modular Evaluation Infrastructure for Reproducible PHM Across Tasks and Domains

arXiv:2605.28345v1 Announce Type: new Abstract: Progress in Prognostics and Health Management (PHM) is hindered by the lack of standardized and reusable evaluation practices across tasks, datasets, an

Position: Retire the 'Positive Backdoor' Label -- Secret Alignment Requires Strict and Systematic Evaluation

arXiv:2605.28597v1 Announce Type: cross Abstract: This position paper argues that the AI/ML community should stop overclaiming and retire the label 'positive backdoor,' and instead treat trigger-activ

Privacy Protection Against Personalized Text-to-Image Synthesis via Cross-image Consistency Constraints

arXiv:2504.12747v2 Announce Type: replace Abstract: The rapid advancement of diffusion models and personalization techniques has made it possible to recreate individual portraits from just a few publi

researcharxiv-cs-cv

Quantum principal component analysis without eigenvector recovery

arXiv:2605.27942v1 Announce Type: cross Abstract: Principal component analysis (PCA) is traditionally implemented through a covariance or kernel matrix, leading-eigenvector extraction, and hard rank-k

RASR: Retrieval-Augmented Super Resolution for Practical Reference-based Image Restoration

arXiv:2508.09449v2 Announce Type: replace Abstract: Reference-based Super Resolution (RefSR) improves upon Single Image Super Resolution (SISR) by leveraging high-quality reference images to enhance t

Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification

arXiv:2512.12887v3 Announce Type: replace Abstract: 3D medical image classification is essential for modern clinical workflows. Medical foundation models (FMs) have emerged as a promising approach for

ROOM: A Physics-Based Continuum Robot Simulator for Photorealistic Medical Datasets Generation

arXiv:2509.13177v2 Announce Type: replace Abstract: Continuum robots are advancing bronchoscopy procedures by accessing complex lung airways and enabling targeted interventions. However, their develop

safetyarxiv-cs-ro

Safe In-Context Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2509.25582v3 Announce Type: replace Abstract: In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks w

SelfJudge: Faster Speculative Decoding via Self-Supervised Judge Verification

arXiv:2510.02329v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates LLM inference by verifying candidate tokens from a draft model against a larger target model. Recent judge de

Semi-Supervised Hypothesis Testing by Betting on Predictions

arXiv:2605.28533v1 Announce Type: new Abstract: We introduce a testing-by-betting framework that leverages predictions on unlabeled data to enhance the power of sequential hypothesis testing. Given li