Search: “papers”

The Tool-Overuse Illusion: Why Does LLM Prefer External Tools over Internal Knowledge?

arXiv:2604.19749v1 Announce Type: new Abstract: Equipping LLMs with external tools effectively addresses internal reasoning limitations. However, it introduces a critical yet under-explored phenomenon

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Safety

Toward Cooperative Driving in Mixed Traffic: An Adaptive Potential Game-Based Approach with Field Test Verification

DGX agent

arXiv:2604.20231v1 Announce Type: new Abstract: Connected autonomous vehicles (CAVs), which represent a significant advancement in autonomous driving technology, have the potential to greatly increase

safetyarxiv-cs-ro

23 Apr 2026

Towards Secure Logging: Characterizing and Benchmarking Logging Code Security Issues with LLMs

arXiv:2604.20211v1 Announce Type: cross Abstract: Logging code plays an important role in software systems by recording key events and behaviors, which are essential for debugging and monitoring. Howe

Trajectory-Aware Reliability Modeling of Democratic Systems

arXiv:2604.20127v1 Announce Type: new Abstract: Failures in complex systems often emerge through gradual degradation and the propagation of stress across interacting components rather than through iso

researcharxiv-cs-lg

Ultrafast Sampling-based Kinodynamic Planning via Differential Flatness

arXiv:2603.16059v2 Announce Type: replace Abstract: Motion planning under dynamics constraints, i.e, kinodynamic planning, enables safe robot operation by generating dynamically feasible trajectories

researcharxiv-cs-ro

UniCVR: From Alignment to Reranking for Unified Zero-Shot Composed Visual Retrieval

model-releasesarxiv-cs-cv

arXiv:2604.20318v1 Announce Type: new Abstract: Composed image retrieval, multi-turn composed image retrieval, and composed video retrieval all share a common paradigm: composing the reference visual

Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges

arXiv:2604.19788v1 Announce Type: new Abstract: As Artificial Intelligence (AI) systems continue to grow in size and complexity, so does the difficulty of the quest for AI transparency. In a world of

VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection

arXiv:2603.26842v2 Announce Type: replace-cross Abstract: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems. Existing method

applicationsarxiv-cs-ai

Verification of Machine Unlearning is Fragile

arXiv:2408.00929v2 Announce Type: replace Abstract: As privacy concerns escalate in the realm of machine learning, data owners now have the option to utilize machine unlearning to remove their data fr

safetyarxiv-cs-lg

Visual-Tactile Peg-in-Hole Assembly Learning from Peg-out-of-Hole Disassembly

arXiv:2604.20712v1 Announce Type: new Abstract: Peg-in-hole (PiH) assembly is a fundamental yet challenging robotic manipulation task. While reinforcement learning (RL) has shown promise in tackling s

safetyarxiv-cs-ro

A Dual Perspective on Synthetic Trajectory Generators: Utility Framework and Privacy Vulnerabilities

arXiv:2604.19653v1 Announce Type: new Abstract: Human mobility data are used in numerous applications, ranging from public health to urban planning. Human mobility is inherently sensitive, as it can c

Local Ai

A Heterogeneous Long-Micro Scale Cascading Architecture for General Aviation Health Management

arXiv:2603.22885v4 Announce Type: replace Abstract: BACKGROUND: General aviation fleet expansion demands intelligent health monitoring under computational constraints. Real-world aircraft health diagn

local-aiarxiv-cs-lg

A Mechanism and Optimization Study on the Impact of Information Density on User-Generated Content Named Entity Recognition

arXiv:2604.18944v1 Announce Type: new Abstract: Named Entity Recognition (NER) models trained on clean, high-resource corpora exhibit catastrophic performance collapse when deployed on noisy, sparse U

researcharxiv-cs-cl

Agents

A Multi-Agent Framework with Structured Reasoning and Reflective Refinement for Multimodal Empathetic Response Generation

arXiv:2604.18988v1 Announce Type: new Abstract: Multimodal empathetic response generation (MERG) aims to generate emotionally engaging and empathetic responses based on users' multimodal contexts. Exi

agentsarxiv-cs-cv

Hardware

AdaGScale: Viewpoint-Adaptive Gaussian Scaling in 3D Gaussian Splatting to Reduce Gaussian-Tile Pairs

arXiv:2604.18980v1 Announce Type: new Abstract: Reducing the number of Gaussian-tile pairs is one of the most promising approaches to improve 3D Gaussian Splatting (3D-GS) rendering speed on GPUs. How

hardwarearxiv-cs-cv

Adaptive Slicing-Assisted Hyper Inference for Enhanced Small Object Detection in High-Resolution Imagery

arXiv:2604.19233v1 Announce Type: new Abstract: Deep learning-based object detectors have achieved remarkable success across numerous computer vision applications, yet they continue to struggle with s

Allo{SR}^2: Rectifying One-Step Super-Resolution to Stay Real via Allomorphic Generative Flows

arXiv:2604.19238v1 Announce Type: new Abstract: Real-world image super-resolution (Real-SR) has been revolutionized by leveraging the powerful generative priors of large-scale diffusion and flow-based

safetyarxiv-cs-cv

Agents

An AI Agent Execution Environment to Safeguard User Data

arXiv:2604.19657v1 Announce Type: cross Abstract: AI agents promise to serve as general-purpose personal assistants for their users, which requires them to have access to private user data (e.g., pers

agentsarxiv-cs-ai

An Experimental Characterization of Mechanical Layer Jamming Systems

model-releasesarxiv-cs-ro

arXiv:2511.07882v2 Announce Type: replace Abstract: Organisms in nature, such as Cephalopods and Pachyderms, exploit stiffness modulation to achieve amazing dexterity in the control of their appendage

Analytical Extraction of Conditional Sobol' Indices via Basis Decomposition of Polynomial Chaos Expansions

model-releasesarxiv-cs-lg

arXiv:2604.19165v1 Announce Type: cross Abstract: In uncertainty quantification, evaluating sensitivity measures under specific conditions (i.e., conditional Sobol' indices) is essential for systems w

Article and Comment Frames Shape the Quality of Online Comments

arXiv:2603.27889v2 Announce Type: replace Abstract: Framing theory posits that how information is presented shapes audience responses, but computational work has largely ignored audience reactions. Wh

researcharxiv-cs-cl

Attention-based Multi-modal Deep Learning Model of Spatio-temporal Crop Yield Prediction with Satellite, Soil and Climate Data

arXiv:2604.19217v1 Announce Type: cross Abstract: Crop yield prediction is one of the most important challenge, which is crucial to world food security and policy-making decisions. The conventional fo

Agents

Autonomous UAV Pipeline Near-proximity Inspection via Disturbance-Aware Predictive Visual Servoing

arXiv:2604.19618v1 Announce Type: new Abstract: Reliable pipeline inspection is critical to safe energy transportation, but is constrained by long distances, complex terrain, and risks to human inspec

agentsarxiv-cs-ro

Bayesian Event-Based Model for Disease Subtype and Stage Inference

arXiv:2512.03467v2 Announce Type: replace Abstract: Chronic diseases often progress differently across patients. Rather than randomly varying, there are typically a small number of subtypes for how a

applicationsarxiv-cs-lg

BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps

arXiv:2604.19532v1 Announce Type: cross Abstract: Tokenizing music to fit the general framework of language models is a compelling challenge, especially considering the diverse symbolic structures in

Benchmarking Vision Foundation Models for Domain-Generalizable Face Anti-Spoofing

arXiv:2604.19196v1 Announce Type: new Abstract: Face Anti-Spoofing (FAS) remains challenging due to the requirement for robust domain generalization across unseen environments. While recent trends lev

Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models

arXiv:2604.18751v1 Announce Type: cross Abstract: Nonlinear machine-learning models are increasingly used to discover causal relationships in time-series data, yet the interpretation of their outputs

applicationsarxiv-cs-ai

Beyond Rating: A Comprehensive Evaluation and Benchmark for AI Reviews

model-releasesarxiv-cs-cl

arXiv:2604.19502v1 Announce Type: new Abstract: The rapid adoption of Large Language Models (LLMs) has spurred interest in automated peer review; however, progress is currently stifled by benchmarks t

Beyond the Bellman Fixed Point: Geometry and Fast Policy Identification in Value Iteration

arXiv:2604.17457v2 Announce Type: replace-cross Abstract: Dynamic programming is one of the most fundamental methodologies for solving Markov decision problems. Among its many variants, Q-value iterat

Byzantine-tolerant distributed learning of finite mixture models

model-releasesarxiv-cs-lg

arXiv:2407.13980v3 Announce Type: replace-cross Abstract: Traditional statistical methods need to be updated to work with modern distributed data storage paradigms. A common approach is the split-and-

Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation

arXiv:2507.06321v2 Announce Type: replace Abstract: Collecting and annotating images for the purpose of training segmentation models is often cost prohibitive. In the domain of wildland fire science,

Chimera: Neuro-Symbolic Attention Primitives for Trustworthy Dataplane Intelligence

arXiv:2602.12851v3 Announce Type: replace-cross Abstract: Deploying expressive learning models directly on programmable dataplanes promises line-rate, low-latency traffic analysis but remains hindered

Colour Extraction Pipeline for Odonates using Computer Vision

arXiv:2604.18725v1 Announce Type: new Abstract: The correlation between insect morphological traits and climate has been documented in physiological studies, but such studies remain limited by the tim

Concept Inconsistency in Dermoscopic Concept Bottleneck Models: A Rough-Set Analysis of the Derm7pt Dataset

model-releasesarxiv-cs-cv

arXiv:2604.19323v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) route predictions exclusively through a clinically grounded concept layer, binding interpretability to concept-label

Conditional Diffusion Modeling with Attention for Probabilistic Battery Capacity Prediction under Real-World Condition

arXiv:2510.17414v2 Announce Type: replace Abstract: Accurate prediction of lithium-ion battery capacity and its associated uncertainty is essential for reliable battery management but remains challeng

applicationsarxiv-cs-lg

Council Mode: Mitigating Hallucination and Bias in LLMs via Multi-Agent Consensus

arXiv:2604.02923v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs), particularly those employing Mixture-of-Experts (MoE) architectures, have achieved remarkable capabilities acros

Counting Worlds Branching Time Semantics for post-hoc Bias Mitigation in generative AI

arXiv:2604.19431v1 Announce Type: cross Abstract: Generative AI systems are known to amplify biases present in their training data. While several inference-time mitigation strategies have been propose

DASH-KV: Accelerating Long-Context LLM Inference via Asymmetric KV Cache Hashing

arXiv:2604.19351v1 Announce Type: new Abstract: The quadratic computational complexity of the standard attention mechanism constitutes a fundamental bottleneck for large language models in long-contex

researcharxiv-cs-cl

Debiased neural operators for estimating functionals

arXiv:2604.19296v1 Announce Type: new Abstract: Neural operators are widely used to approximate solution maps of complex physical systems. In many applications, however, the goal is not to recover the

safetyarxiv-cs-lg

Decomposed Trust: Privacy, Adversarial Robustness, Ethics, and Fairness in Low-Rank LLMs

arXiv:2511.22099v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have driven major advances across domains, yet their massive size hinders deployment in resource-constrained sett

Deep sprite-based image models: An analysis

model-releasesarxiv-cs-cv

arXiv:2604.19480v1 Announce Type: new Abstract: While foundation models drive steady progress in image segmentation and diffusion algorithms compose always more realistic images, the seemingly simple

Deep Supervised Contrastive Learning of Pitch Contours for Robust Pitch Accent Classification in Seoul Korean

model-releasesarxiv-cs-cl

arXiv:2604.19477v1 Announce Type: cross Abstract: The intonational structure of Seoul Korean has been defined with discrete tonal categories within the Autosegmental-Metrical model of intonational pho

DeltaSeg: Tiered Attention and Deep Delta Learning for Multi-Class Structural Defect Segmentation

arXiv:2604.18745v1 Announce Type: new Abstract: Automated segmentation of structural defects from visual inspection imagery remains challenging due to the diversity of damage types, extreme class imba

Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference

arXiv:2604.19566v1 Announce Type: cross Abstract: Reliable biomedical and clinical retrieval requires more than strong ranking performance: it requires a practical way to find systematic model failure

safetyarxiv-cs-cl

DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs

arXiv:2604.19118v1 Announce Type: cross Abstract: Modern distributed systems generate massive volumes of log data that are critical for detecting anomalies and cyber threats. However, in real world se

Early Pruning for Public Transport Routing

arXiv:2603.12592v2 Announce Type: replace-cross Abstract: Routing algorithms for public transport, particularly the widely used RAPTOR and its variants, often face performance bottlenecks during the t

Hardware

Efficient Mixture-of-Experts LLM Inference with Apple Silicon NPUs

arXiv:2604.18788v1 Announce Type: new Abstract: Apple Neural Engine (ANE) is a dedicated neural processing unit (NPU) present in every Apple Silicon chip. Mixture-of-Experts (MoE) LLMs improve inferen

hardwarearxiv-cs-lg

Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student Attacks

arXiv:2604.18660v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in education, yet their default helpfulness often conflicts with pedagogical principles. Prior work