research

From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments

arXiv:2605.02098v1 Announce Type: new Abstract: Large-scale 3D point clouds can consist of billions of points. Even after downsampling, these point clouds are too large for modern 3D neural networks.

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

FunFuzz: An LLM-Powered Evolutionary Fuzzing Framework

DGX agent

arXiv:2605.02789v1 Announce Type: cross Abstract: Modern fuzzers increasingly use Large Language Models (LLMs) to generate structured inputs, but LLM-driven fuzzing is sensitive to prompt initializati

researcharxiv-cs-cl

5 May 2026

Fuzzy Fingerprinting Encoder Pre-trained Language Models for Emotion Recognition in Conversations: Human Assessment and Validity Study

arXiv:2605.02665v1 Announce Type: new Abstract: In Emotion Recognition in Conversations (ERC), model decisions should align with nuanced human perception and ideally provide insights on the classifica

Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent

arXiv:2502.06719v3 Announce Type: replace-cross Abstract: In this paper, we establish the non-asymptotic validity of the multiplier bootstrap procedure for constructing the confidence sets using the S

GEASS: Training-Free Caption Steering for Hallucination Mitigation in Vision-Language Models

arXiv:2605.01733v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at grounded reasoning but remain prone to object hallucination. Recent work treats self-generated captions as a unif

Generative Interfaces for Language Models

arXiv:2508.19227v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly seen as assistants, copilots, and consultants, capable of supporting a wide range of tasks through nat

GEODE: Angle-Adaptive OOD Detection with Universal Scorer Compatibility

arXiv:2605.01063v1 Announce Type: cross Abstract: Outlier Exposure (OE) is among the strongest training-based OOD detectors on standard benchmarks but exhibits scorer-dependent tradeoffs (e.g., strong

Global-Local Feature Decoding with Adapter-Guided SAMv2 for Salient Object Detection

arXiv:2605.02616v1 Announce Type: new Abstract: Salient Object Detection (SOD) remains an essential yet underexplored task in the era of large-scale vision models. Although foundation models like SAM

Gradient Boosted Risk Scores

arXiv:2605.02593v1 Announce Type: new Abstract: Risk scores are an interpretable and actionable class of machine learning models with applications in medicine, insurance, and risk management. Unlike m

Gradient-Discrepancy Acquisition for Pool-Based Active Learning

arXiv:2605.02609v1 Announce Type: new Abstract: The effectiveness of active learning hinges on the choice of the acquisition criterion by which a learning algorithm selects potentially informative dat

Graph-Augmented Topological Internalization with Dual-Stream Classifiers for Medical Report Generation

arXiv:2605.02376v1 Announce Type: new Abstract: Automated medical report generation, MRG, holds substantial value for alleviating radiologist workload and enhancing diagnostic efficiency. However, mai

GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning

arXiv:2605.01310v1 Announce Type: new Abstract: Graph self-supervised learning typically relies on large-scale unlabeled datasets, heavily inflating computational costs. However, empirical evidence su

GRAVITY: Architecture-Agnostic Structured Anchoring for Long-Horizon Conversational Memory

arXiv:2605.01688v1 Announce Type: new Abstract: Long-horizon conversational agents rely on memory systems with increasingly sophisticated retrieval mechanisms. However, retrieved fragments are typical

GSDeformer: Direct, Real-time and Extensible Cage-based Deformation for 3D Gaussian Splatting

arXiv:2405.15491v4 Announce Type: replace Abstract: We present GSDeformer, a method that enables cage-based deformation on 3D Gaussian Splatting (3DGS). Our approach bridges cage-based deformation and

Hallucination Detection in LLMs with Topological Divergence on Attention Graphs

arXiv:2504.10063v4 Announce Type: replace Abstract: Hallucination, i.e., generating factually incorrect content, remains a critical challenge for large language models (LLMs). We introduce TOHA, a TOp

Harnessing Reasoning Trajectories for Hallucination Detection via Answer-agreement Representation Shaping

arXiv:2601.17467v2 Announce Type: replace Abstract: Large reasoning models (LRMs) often generate long, seemingly coherent reasoning traces yet still produce incorrect answers, making hallucination det

Heavy-Tailed Principal Component Analysis

arXiv:2603.11308v2 Announce Type: replace Abstract: Principal Component Analysis (PCA) is a cornerstone of dimensionality reduction, yet its classical formulation relies critically on second-order mom

HELIX: Hybrid Encoding with Learnable Identity and Cross-dimensional Synthesis for Time Series Imputation

arXiv:2605.02278v1 Announce Type: new Abstract: Time series imputation benefits from leveraging cross-feature correlations, yet existing attention-based methods re-discover feature relationships at ea

Hey, That's My Data! Token-Only Dataset Inference in Large Language Models

arXiv:2506.06057v2 Announce Type: replace Abstract: Large Language Models (LLMs) rely on massive training datasets, often including proprietary data, which raises concerns about unauthorized usage and

HiFi-Mamba: Dual-Stream W-Laplacian Enhanced Mamba for High-Fidelity MRI Reconstruction

arXiv:2508.09179v3 Announce Type: replace-cross Abstract: Reconstructing high-fidelity MR images from undersampled k-space data remains a challenging problem in MRI. While Mamba variants for vision ta

High-Quality Spatial Reconstruction and Orthoimage Generation Using Efficient 2D Gaussian Splatting

arXiv:2503.19703v3 Announce Type: replace Abstract: Highly accurate geometric precision and dense image features characterize True Digital Orthophoto Maps (TDOMs), which are in great demand for applic

High-Speed, Scalable Sensor Readout for Dexterous Robotic Hands via Shift-Register Multiplexing

arXiv:2605.01434v1 Announce Type: new Abstract: Dexterous robotic hands require high-speed multimodal sensing across many degrees of freedom, yet existing readout architectures often impose trade-offs

Higher-Order Flexible Configurations of Planar Parallel Manipulators Constructed by Averaging

arXiv:2605.02434v1 Announce Type: new Abstract: This paper investigates singular configurations of planar 3-RPR parallel manipulators, which result from applying the averaging technique to solution pa

How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP

arXiv:2411.05527v3 Announce Type: replace Abstract: Wikipedia's perceived high quality and broad language coverage have established it as a fundamental resource in NLP. However, in recent years, such

How Label Imbalance Shapes Geometry: A General Spectral Analysis of Multi-Label Neural Collapse

arXiv:2605.01897v1 Announce Type: new Abstract: This work investigates the phenomenon of Neural Collapse (NC) in multi-label classification, extending its conceptual framework from multi-class learnin

How Prompts Move Language Model Behavior: Frames, Salience, and Construal as Semantic Control

arXiv:2512.12688v3 Announce Type: replace-cross Abstract: Prompt engineering is widely used to shape large language model behavior, yet it is often treated as a practical heuristic rather than as a fo

HyMem: Hybrid Memory Architecture with Dynamic Retrieval Scheduling

arXiv:2602.13933v2 Announce Type: replace Abstract: Large language model (LLM) agents demonstrate strong performance in short-text contexts but often underperform in extended dialogues due to ineffici

researcharxiv-cs-ai

IMPACT-HOI: Supervisory Control for Onset-Anchored Partial HOI Event Construction

arXiv:2605.01666v1 Announce Type: new Abstract: We present IMPACT-HOI, a mixed-initiative framework for annotating egocentric procedural video by constructing structured event graphs for Human-Object

IMPACT-Scribe: Interactive Temporal Action Segmentation with Boundary Scribbles and Query Planning

arXiv:2605.01668v1 Announce Type: new Abstract: Dense temporal annotation of procedural activity videos is vital for action understanding and embodied intelligence but remains labor-intensive due to r

Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction

arXiv:2509.03540v3 Announce Type: replace Abstract: Large Language Models (LLMs) often struggle with producing factually consistent answers due to limitations in their parametric memory. Retrieval-Aug

Improving Imbalanced Multi-Label Chest X-Ray Diagnosis via CBAM-Enhanced CNN Backbones

arXiv:2605.02328v1 Announce Type: new Abstract: Chest radiography is a widely used imaging modality for thoracic disease diagnosis, yet its conventional interpretation remains time-consuming and heavi

Improving LLM Code Generation via Requirement-Aware Curriculum Reinforcement Learning

arXiv:2605.00433v1 Announce Type: cross Abstract: Code generation, which aims to automatically generate source code from given programming requirements, has the potential to substantially improve soft

researcharxiv-cs-ai

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction

arXiv:2605.02230v1 Announce Type: new Abstract: Gliomas are aggressive brain tumors that infiltrate surrounding tissue beyond the visible tumor margins observed on Magnetic Resonance Imaging (MRI). Pr

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition

arXiv:2605.02364v1 Announce Type: new Abstract: Upweighting high-quality data in LLM pretraining often improves performance, but in datalimited regimes, especially under overtraining, stronger upweigh

Interlaced R2D2 DNN Series for Scalable Non-Cartesian MRI with Sensitivity Self-calibration

arXiv:2503.09559v3 Announce Type: replace-cross Abstract: We introduce interlaced R2D2 (iR2D2), a DNN series paradigm for scalable image reconstruction from accelerated non-Cartesian k-space acquisiti

Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues

arXiv:2605.01097v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have led to the development of AI-powered tutoring systems that provide interactive support via dialogue

Is It Novel and Why? Fine-Grained Patent Novelty Prediction Based on Passage Retrieval

arXiv:2605.02392v1 Announce Type: new Abstract: Novelty assessment is a critical yet complex task in the examination process for patent acceptance, requiring examiners to determine whether an inventio

KANs need curvature: penalties for compositional smoothness

arXiv:2605.02190v1 Announce Type: new Abstract: Kolmogorov-Arnold networks (KANs) offer a potent combination of accuracy and interpretability, thanks to their compositions of learnable univariate acti

Kernel Treatment Effects with Adaptively Collected Data

arXiv:2510.10245v2 Announce Type: replace-cross Abstract: Adaptive experiments improve efficiency by adjusting treatment assignments based on past outcomes, but this adaptivity breaks the i.i.d. assum

Kinematic Kitbashing

arXiv:2510.13048v2 Announce Type: replace Abstract: We introduce Kinematic Kitbashing, an optimization framework that synthesizes articulated 3D objects by assembling reusable parts conditioned on an

Know Yourself Better: Diverse Object-Related Features Improve Open Set Recognition

arXiv:2404.10370v3 Announce Type: replace Abstract: Open set recognition (OSR) is a critical aspect of machine learning, addressing the challenge of detecting novel classes during inference. Within th

Large margin classifier with graph-based adaptive regularization

arXiv:2605.02027v1 Announce Type: new Abstract: This paper introduces the use of per-class regularization hyperparameters in Gabriel graph-based binary classifiers. We demonstrate how the quality inde

Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference

arXiv:2605.02739v1 Announce Type: new Abstract: Dual-system Vision-Language-Action (VLA) models achieve state-of-the-art robotic manipulation but are bottlenecked by the VLM backbone, which must execu

Latent Space Probing for Adult Content Detection in Video Generative Models

arXiv:2605.00874v1 Announce Type: new Abstract: The rapid proliferation of AI-powered video generation systems has introduced significant challenges in content moderation, particularly with respect to

Learning a Stochastic Differential Equation Model of Tropical Cyclone Intensification from Reanalysis and Observational Data

arXiv:2601.08116v2 Announce Type: replace Abstract: Tropical cyclones are dangerous natural hazards, but their hazard is challenging to quantify directly from historical datasets due to limited datase

Learning Discriminators for Resampling in the Ensemble Gaussian Mixture Filter through a Normalizing Flow Approach

arXiv:2605.01089v1 Announce Type: new Abstract: The ensemble Gaussian mixture filter (EnGMF) is a powerful, convergent particle filter capable of medium-to-high dimensional non-linear filtering. The E

Learning to Place Objects with Programs and Iterative Self Training

arXiv:2503.04496v2 Announce Type: replace-cross Abstract: In this work we study indoor scene object placement. Given a 3D indoor scene and an object, the task is to predict placement locations within

Leveraging Argument Structure to Predict Content Hatefulness

arXiv:2605.02457v1 Announce Type: new Abstract: Information disorder is a challenging phenomenon that affects society at large. This phenomenon entails the diffusion of misleading, misinforming, and h