Search: “papers”

SSG: Logit-Balanced Vocabulary Partitioning for LLM Watermarking

arXiv:2604.22438v1 Announce Type: cross Abstract: Watermarking has emerged as a promising technique for tracing the authorship of content generated by large language models (LLMs). Among existing appr

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StateX: Enhancing RNN Recall via Post-training State Expansion

DGX agent

arXiv:2509.22630v3 Announce Type: replace-cross Abstract: Recurrent neural networks (RNNs), such as linear attention and state-space models, have gained popularity due to their constant per-token comp

researcharxiv-cs-ai

27 Apr 2026

Survey Response Generation: Generating Closed-Ended Survey Responses In-Silico with Large Language Models

arXiv:2510.11586v2 Announce Type: replace Abstract: Many in-silico simulations of human survey responses with large language models (LLMs) focus on generating closed-ended survey responses, whereas LL

safetyarxiv-cs-cl

Uni-Encoder Meets Multi-Encoders: Representation Before Fusion for Brain Tumor Segmentation with Missing Modalities

arXiv:2604.22177v1 Announce Type: new Abstract: Multimodal MRI offers complementary information for brain tumor segmentation, but clinical scans often lack one or more modalities, which degrades segme

researcharxiv-cs-cv

Unified Taxonomy for Multivariate Time Series Anomaly Detection using Deep Learning

arXiv:2603.18941v2 Announce Type: replace-cross Abstract: The topic of Multivariate Time Series Anomaly Detection (MTSAD) has grown rapidly over the past years, with a steady rise in publications and

researcharxiv-cs-lg

UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions

arXiv:2604.22209v1 Announce Type: cross Abstract: Generative audio modeling has largely been fragmented into specialized tasks, text-to-speech (TTS), text-to-music (TTM), and text-to-audio (TTA), each

V-STC: A Time-Efficient Multi-Vehicle Coordinated Trajectory Planning Approach

arXiv:2604.22196v1 Announce Type: new Abstract: Coordinating the motions of multiple autonomous vehicles (AVs) requires planning frameworks that ensure safety while making efficient use of space and t

safetyarxiv-cs-ro

When Does LLM Self-Correction Help? A Control-Theoretic Markov Diagnostic and Verify-First Intervention

arXiv:2604.22273v1 Announce Type: new Abstract: Iterative self-correction is widely used in agentic LLM systems, but when repeated refinement helps versus hurts remains unclear. We frame self-correcti

A Case Study in Recovery of Drones using Discrete-Event Systems

arXiv:2604.21740v1 Announce Type: cross Abstract: Discrete-event systems and supervisory control theory provide a rigorous framework for specifying correct-by-construction behavior. However, their pra

agentsarxiv-cs-ro

A Deployable Embodied Vision-Language Navigation System with Hierarchical Cognition and Context-Aware Exploration

arXiv:2604.21363v1 Announce Type: new Abstract: Bridging the gap between embodied intelligence and embedded deployment remains a key challenge in intelligent robotic systems, where perception, reasoni

applicationsarxiv-cs-ro

A Metamorphic Testing Approach to Diagnosing Memorization in LLM-Based Program Repair

arXiv:2604.21579v1 Announce Type: cross Abstract: LLM-based automated program repair (APR) techniques have shown promising results in reducing debugging costs. However, prior results can be affected b

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment

arXiv:2604.21891v1 Announce Type: cross Abstract: Maintaining instantaneous balance between electricity supply and demand is critical for reliability and grid instability. System operators achieve thi

A Replicable Robotics Awareness Method Using LLM-Enabled Robotics Interaction: Evidence from a Corporate Challenge

arXiv:2604.21377v1 Announce Type: new Abstract: Large language models are increasingly being explored as interfaces between humans and robotic systems, yet there remains limited evidence on how such t

researcharxiv-cs-ro

A single algorithm for both restless and rested rotting bandits

arXiv:2604.21432v1 Announce Type: cross Abstract: In many application domains (e.g., recommender systems, intelligent tutoring systems), the rewards associated to the actions tend to decrease over tim

researcharxiv-cs-lg

A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems

arXiv:2604.21030v1 Announce Type: cross Abstract: The integration of Model Predictive Control (MPC) and Reinforcement Learning (RL) has emerged as a promising paradigm for constrained decision-making

Active Data

arXiv:2604.21044v1 Announce Type: new Abstract: In some complex domains, certain problem-specific decompositions can provide advantages over monolithic designs by enabling comprehension and specificat

Addressing Image Authenticity When Cameras Use Generative AI

arXiv:2604.21879v1 Announce Type: cross Abstract: The ability of generative AI (GenAI) methods to photorealistically alter camera images has raised awareness about the authenticity of images shared on

Agentic AI for Personalized Physiotherapy: A Multi-Agent Framework for Generative Video Training and Real-Time Pose Correction

arXiv:2604.21154v1 Announce Type: new Abstract: At-home physiotherapy compliance remains critically low due to a lack of personalized supervision and dynamic feedback. Existing digital health solution

AgenticQwen: Training Small Agentic Language Models with Dual Data Flywheels for Industrial-Scale Tool Use

arXiv:2604.21590v1 Announce Type: new Abstract: Modern industrial applications increasingly demand language models that act as agents, capable of multi-step reasoning and tool use in real-world settin

agentsarxiv-cs-cl

AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure

arXiv:2604.21129v1 Announce Type: cross Abstract: Current blockchain Layer 2 solutions, including Optimism, Arbitrum, zkSync, and their derivatives, optimize for human-initiated financial transactions

Local Ai

an interpretable vision transformer framework for automated brain tumor classification

arXiv:2604.21311v1 Announce Type: new Abstract: Brain tumors represent one of the most critical neurological conditions, where early and accurate diagnosis is directly correlated with patient survival

local-aiarxiv-cs-cv

Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach

arXiv:2502.15793v2 Announce Type: replace Abstract: Anomaly detection in smart power grids is a critical challenge due to the complexity, heterogeneity, and dynamic nature of sensor data streams. Exis

applicationsarxiv-cs-lg

Architectures for Robust Self-Organizing Energy Systems under Information and Control Constraints

arXiv:2604.21529v1 Announce Type: cross Abstract: Applying the concept of controlled self-organization in agent-based Cyber-Physical Energy Systems (CPES) is a promising approach to ensure system robu

AttDiff-GAN: A Hybrid Diffusion-GAN Framework for Facial Attribute Editing

arXiv:2604.21289v1 Announce Type: new Abstract: Facial attribute editing aims to modify target attributes while preserving attribute-irrelevant content and overall image fidelity. Existing GAN-based m

safetyarxiv-cs-cv

Beyond the Frame: Generating 360 Panoramic Videos from Perspective Videos

arXiv:2504.07940v3 Announce Type: replace Abstract: 360{eg} videos have emerged as a promising medium to represent our dynamic visual world. Compared to the 'tunnel vision' of standard cameras, their

researcharxiv-cs-cv

BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature

arXiv:2604.21508v1 Announce Type: new Abstract: Protein-ligand bioactivity data published in the literature are essential for drug discovery, yet manual curation struggles to keep pace with rapidly gr

Bounding the Black Box: A Statistical Certification Framework for AI Risk Regulation

arXiv:2604.21854v1 Announce Type: new Abstract: Artificial intelligence now decides who receives a loan, who is flagged for criminal investigation, and whether an autonomous vehicle brakes in time. Go

safetyarxiv-cs-ai

Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models

arXiv:2604.20994v1 Announce Type: cross Abstract: The growth of agentic AI has drawn significant attention to function calling Large Language Models (LLMs), which are designed to extend the capabiliti

C-SHAP for time series: An approach to high-level temporal explanations

arXiv:2504.11159v2 Announce Type: replace Abstract: In high-stakes domains, such as healthcare and industry, the explainability of AI-based decision-making has become crucial. Without insight into mod

applicationsarxiv-cs-ai

CaST-POI: Candidate-Conditioned Spatiotemporal Modeling for Next POI Recommendation

arXiv:2604.20845v1 Announce Type: cross Abstract: Next Point-of-Interest (POI) recommendation plays a crucial role in location-based services by predicting users' future mobility patterns. Existing me

Causal Disentanglement for Full-Reference Image Quality Assessment

arXiv:2604.21654v1 Announce Type: cross Abstract: Existing deep network-based full-reference image quality assessment (FR-IQA) models typically work by performing pairwise comparisons of deep features

Cognitive Amplification vs Cognitive Delegation in Human-AI Systems: A Metric Framework

arXiv:2603.18677v2 Announce Type: replace-cross Abstract: Artificial intelligence is increasingly embedded in human decision making. In some cases, it enhances human reasoning. In others, it fosters e

Conjecture and Inquiry: Quantifying Software Performance Requirements via Interactive Retrieval-Augmented Preference Elicitation

arXiv:2604.21380v1 Announce Type: cross Abstract: Since software performance requirements are documented in natural language, quantifying them into mathematical forms is essential for software enginee

applicationsarxiv-cs-ai

CSC: Turning the Adversary's Poison against Itself

arXiv:2604.21416v1 Announce Type: cross Abstract: Poisoning-based backdoor attacks pose significant threats to deep neural networks by embedding triggers in training data, causing models to misclassif

DenoiseRank: Learning to Rank by Diffusion Models

arXiv:2604.20852v1 Announce Type: cross Abstract: Learning to rank (LTR) is one of the core tasks in Machine Learning. Traditional LTR models have made great progress, but nearly all of them are imple

Tutorials

Design, Modelling and Experimental Evaluation of a Tendon-driven Wrist Abduction-Adduction Mechanism for an upper limb exoskeleton

arXiv:2604.20893v1 Announce Type: new Abstract: Wrist exoskeletons play a vital role in rehabilitation and assistive applications, yet conventional actuation mechanisms such as electric motors or pneu

tutorialsarxiv-cs-ro

Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models

model-releasesarxiv-cs-cl

arXiv:2507.04023v3 Announce Type: replace Abstract: Large language models (LLMs) achieve impressive performance on complex mathematical benchmarks yet sometimes fail on basic math reasoning while gene

Droplet-LNO: Physics-Informed Laplace Neural Operators for Accurate Prediction of Droplet Spreading Dynamics on Complex Surfaces

model-releasesarxiv-cs-lg

arXiv:2604.20993v1 Announce Type: new Abstract: Spreading of liquid droplets on solid substrates constitutes a classic multiphysics problem with widespread applications ranging from inkjet printing, s

Drug Synergy Prediction via Residual Graph Isomorphism Networks and Attention Mechanisms

arXiv:2604.21473v1 Announce Type: cross Abstract: In the treatment of complex diseases, treatment regimens using a single drug often yield limited efficacy and can lead to drug resistance. In contrast

EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization

model-releasesarxiv-cs-lg

arXiv:2411.00171v2 Announce Type: replace Abstract: To avoid myopic behavior, multi-step lookahead Bayesian optimization (BO) algorithms consider the sequential nature of BO and have demonstrated prom

Effects of Swarm Size Variability on Operator Workload

arXiv:2604.21707v1 Announce Type: new Abstract: Real-world deployments of human--swarm teams depend on balancing operator workload to leverage human strengths without inducing overload. A key challeng

agentsarxiv-cs-ro

Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation

arXiv:2604.21264v1 Announce Type: new Abstract: Person-Job Fit (PJF) is a critical component for online recruitment. Existing approaches face several challenges, particularly in handling low-quality j

ERA: Evidence-based Reliability Alignment for Honest Retrieval-Augmented Generation

arXiv:2604.20854v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds language models in factual evidence but introduces critical challenges regarding knowledge conflicts betw

safetyarxiv-cs-ai

Evaluation of Automatic Speech Recognition Using Generative Large Language Models

arXiv:2604.21928v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) is traditionally evaluated using Word Error Rate (WER), a metric that is insensitive to meaning. Embedding-based sema

researcharxiv-cs-cl

Tutorials

Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model

arXiv:2410.16006v3 Announce Type: replace Abstract: A common challenge towards the adaptability of Large Language Models (LLMs) is their ability to learn new languages over time without hampering the

tutorialsarxiv-cs-cl

Fairness under uncertainty in sequential decisions

arXiv:2604.21711v1 Announce Type: cross Abstract: Fair machine learning (ML) methods help identify and mitigate the risk that algorithms encode or automate social injustices. Algorithmic approaches al

safetyarxiv-cs-ai

Finding Meaning in Embeddings: Concept Separation Curves

arXiv:2604.21555v1 Announce Type: new Abstract: Sentence embedding techniques aim to encode key concepts of a sentence's meaning in a vector space. However, the majority of evaluation approaches for s

researcharxiv-cs-cl

Fine-Tuning Regimes Define Distinct Continual Learning Problems

model-releasesarxiv-cs-lg

arXiv:2604.21927v1 Announce Type: new Abstract: Continual learning (CL) studies how models acquire tasks sequentially while retaining previously learned knowledge. Despite substantial progress in benc