Search: “research”

Characterizing control between interacting subsystems with deep Jacobian estimation

arXiv:2507.01946v2 Announce Type: replace-cross Abstract: Biological function arises through the dynamical interactions of multiple subsystems, including those between brain areas, within gene regulat

Class Angular Distortion Index for Dimensionality Reduction

DGX agent

arXiv:2605.00637v1 Announce Type: new Abstract: Dimensionality reduction (DR) techniques are often characterized by whether they preserve global, high-level structures in the data or local, neighborho

researcharxiv-cs-lg

4 May 2026

CleanBase: Detecting Malicious Documents in RAG Knowledge Databases

arXiv:2605.00460v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is vulnerable to prompt injection attacks, in which an adversary inserts malicious documents containing carefully

CollaFuse: Collaborative Diffusion Models

arXiv:2406.14429v3 Announce Type: replace-cross Abstract: In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic ima

Color Conditional Generation with Sliced Wasserstein Guidance

arXiv:2503.19034v2 Announce Type: replace Abstract: We propose SW-Guidance, a training-free approach for image generation conditioned on the color distribution of a reference image. While it is possib

Colorful-Noise: Training-Free Low-Frequency Noise Manipulation for Color-Based Conditional Image Generation

arXiv:2605.00548v1 Announce Type: new Abstract: Text-to-image diffusion models generate images by gradually converting white Gaussian noise into a natural image. White Gaussian noise is well suited fo

Combined Dictionary Unfolding Network with Gradient-Adaptive Fidelity for Transferable Multi-Source Fusion

arXiv:2605.00461v1 Announce Type: cross Abstract: Deep Unfolding Network-based methods have emerged as effective solutions for multi-source image fusion by combining model-driven iterative optimizatio

Comparing Exploration-Exploitation Strategies of LLMs and Humans: Insights from Standard Multi-armed Bandit Experiments

arXiv:2505.09901v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate or automate human behavior in complex sequential decision-making settings. A na

Confidence Estimation in Automatic Short Answer Grading with LLMs

arXiv:2605.00200v1 Announce Type: new Abstract: Automatic Short Answer Grading (ASAG) with generative large language models (LLMs) has recently demonstrated strong performance without task-specific fi

Consistent Diffusion Language Models

arXiv:2605.00161v1 Announce Type: new Abstract: Diffusion language models (DLMs) are an attractive alternative to autoregressive models because they promise sublinear-time, parallel generation, yet pr

Convergence Rate Analysis of the AdamW-Style Shampoo: Unifying One-sided and Two-Sided Preconditioning

arXiv:2601.07326v2 Announce Type: replace-cross Abstract: This paper studies the AdamW-style Shampoo optimizer, an effective implementation of classical Shampoo that notably won the external tuning tr

Copula-enhanced Vision Transformer for high myopia diagnosis through OU UWF fundus images

arXiv:2501.06540v2 Announce Type: replace Abstract: The advancement of AI-assisted myopia screening necessitates the joint diagnosis of both-eye (OU) high myopia (HM) status and the prediction of axia

CRADIPOR: Crash Dispersion Predictor

arXiv:2605.00070v1 Announce Type: new Abstract: We present CRADIPOR, a numerical dispersion prediction tool for automotive crash simulations. Finite Element (FE) crash models are widely used throughou

Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues

arXiv:2605.00119v1 Announce Type: new Abstract: There is a significant gap in evaluating cultural reasoning in LLMs using conversational datasets that capture culturally rich and dialectal contexts. M

Decouple before Integration: Test-time Synthesis of SFT and RLVR Task Vectors

arXiv:2605.00610v1 Announce Type: new Abstract: SFT and RLVR represent two fundamental yet distinct paradigms for LLM post-training, each excelling in distinct dimensions. SFT expands knowledge breadt

Deep Kernel Learning for Stratifying Glaucoma Trajectories

arXiv:2605.00708v1 Announce Type: new Abstract: Effectively stratifying patient risk in chronic diseases like glaucoma is a major clinical challenge. Clinicians need tools to identify patients at high

DiffMI: Breaking Face Recognition Privacy via Diffusion-Driven Training-Free Model Inversion

arXiv:2504.18015v4 Announce Type: replace-cross Abstract: Face recognition poses serious privacy risks due to its reliance on sensitive and immutable biometric data. While modern systems mitigate priv

Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance

arXiv:2507.18654v2 Announce Type: replace-cross Abstract: Diffusion models are powerful tools for sampling from high-dimensional distributions by progressively transforming pure noise into structured

Directed Social Regard: Surfacing Targeted Advocacy, Opposition, Aid, Harms, and Victimization in Online Media

arXiv:2605.00776v1 Announce Type: new Abstract: The language in online platforms, influence operations, and political rhetoric frequently directs a mix of pro-social sentiment (e.g., advocacy, helpful

Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces

arXiv:2601.01082v5 Announce Type: replace Abstract: Quality diversity (QD) optimization searches for a collection of solutions that optimize an objective while attaining diverse outputs of a user-spec

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers

arXiv:2405.13901v4 Announce Type: replace Abstract: Self-attention is central to the success of Transformer architectures; however, learning the query, key, and value projections from random initializ

Distance-Aware Error for Spline Networks: A Bottom-Up Approach to Uncertainty

arXiv:2501.04757v2 Announce Type: replace-cross Abstract: We develop a new class of distance-aware error bounds that tightly characterize the approximation error of spline neural networks. Our bottom-

Distance metric learning for conditional anomaly detection

arXiv:2605.00490v1 Announce Type: new Abstract: Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection f

Diversity in Large Language Models under Supervised Fine-Tuning

arXiv:2605.00195v1 Announce Type: new Abstract: Supervised Fine-Tuning (SFT) is essential for aligning Large Language Models (LLMs) with user intent, yet it is believed to suppress generative diversit

DMDSC: A Dynamic-Margin Deep Simplex Classifier for Open-Set Recognition on Medical Image Datasets

arXiv:2605.00675v1 Announce Type: new Abstract: Medical imaging datasets are often characterized by extreme class imbalances, where rare pathologies are significantly underrepresented compared to comm

EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure

arXiv:2605.00733v1 Announce Type: cross Abstract: Federated Multimodal Learning (FML) trains multimodal models across decentralized clients while keeping their image-text pairs private. However, joint

Efficient Finite Initialization with Partial Norms for Tensorized Neural Networks and Tensor Networks Algorithms

arXiv:2309.06577v5 Announce Type: replace Abstract: We present two algorithms to initialize layers of tensorized neural networks and general tensor network algorithms using partial computations of the

Elimination Templates in Macaulay2

arXiv:2605.00278v1 Announce Type: cross Abstract: We introduce the package exttt{EliminationTemplates} for the Macaulay2 computer algebra system, which provides tools for constructing automatic solver

Embodied Interpretability: Linking Causal Understanding to Generalization in Vision-Language-Action Models

arXiv:2605.00321v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies often fail under distribution shift, suggesting that decisions may depend on spurious visual correlations rather t

researcharxiv-cs-ro

End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer

arXiv:2605.00503v1 Announce Type: new Abstract: Autoregressive image modeling relies on visual tokenizers to compress images into compact latent representations. We design an end-to-end training pipel

Escaping Mode Collapse in LLM Generation via Geometric Regulation

arXiv:2605.00435v1 Announce Type: new Abstract: Mode collapse is a persistent challenge in generative modeling and appears in autoregressive text generation as behaviors ranging from explicit looping

Exploring the Limits of End-to-End Feature-Affinity Propagation for Single-Point Supervised Infrared Small Target Detection

arXiv:2605.00722v1 Announce Type: new Abstract: Single-point supervised infrared small target detection (IRSTD) drastically reduces dense annotation costs. Current state-of-the-art (SOTA) methods achi

Federated Learning with Hypergradient-based Online Update of Aggregation Weights

arXiv:2605.00458v1 Announce Type: new Abstract: Federated learning using mobile and Internet of Things devices requires not only the ability to handle heterogeneity of clients' data distributions but

Federated Weather Modeling on Sensor Data

arXiv:2605.00322v1 Announce Type: new Abstract: Federated weather modeling on sensor data is a distributed system underpinned by federated learning, enabling multiple sensor data sources, including gr

Feedback Lunch: Learned Feedback Codes for Secure Communications

arXiv:2510.16620v3 Announce Type: replace-cross Abstract: We consider reversely-degraded secure-communication channels, for which the secrecy capacity is zero if there is no channel feedback. Specific

Flow matching for Sentinel-2 super-resolution: implementation, application, and implications

arXiv:2605.00367v1 Announce Type: new Abstract: Developing robust techniques for super-resolution of satellite imagery involves navigating commonly observed trade-offs between spectral fidelity and pe

Free Energy Surface Sampling via Reduced Flow Matching

arXiv:2605.00337v1 Announce Type: new Abstract: Sampling the free energy surface, namely, the distribution of collective variables (CVs), is a crucial problem in statistical physics, as it underpins a

From Local to Global to Mechanistic: An iERF-Centered Unified Framework for Interpreting Vision Models

arXiv:2605.00474v1 Announce Type: new Abstract: Modern vision models achieve remarkable accuracy, but explaining where evidence arises, what the model encodes, and how internal computations assemble t

GAFSV-Net: A Vision Framework for Online Signature Verification

arXiv:2605.00120v1 Announce Type: new Abstract: Online signature verification (OSV) requires distinguishing skilled forgeries from genuine samples under high intra-class variability and with very few

GD4: Graph-based Discrete Denoising Diffusion for MIMO Detection

arXiv:2605.00423v1 Announce Type: new Abstract: In wireless communications, recovering the optimal solution to the multiple-input multiple-output (MIMO) detection problem is NP-hard. Obtaining high-qu

Generating Statistical Charts with Validation-Driven LLM Workflows

arXiv:2605.00800v1 Announce Type: new Abstract: Generating diverse, readable statistical charts from tabular data remains challenging for LLMs, as many failures become apparent after rendering and are

GMGaze: MoE-Based Context-Aware Gaze Estimation with CLIP and Multiscale Transformer

arXiv:2605.00799v1 Announce Type: new Abstract: Gaze estimation methods commonly use facial appearances to predict the direction of a person gaze. However, previous studies show three major challenges

Gradient Regularized Newton Boosting Trees with Global Convergence

arXiv:2605.00581v1 Announce Type: cross Abstract: Gradient Boosting Decision Trees (GBDTs) dominate tabular machine learning, with modern implementations like XGBoost, LightGBM, and CatBoost being bas

Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey

arXiv:2411.17429v2 Announce Type: replace Abstract: Graph Neural Networks are powerful models for learning from graph-structured data, yet their effectiveness is often limited by two critical challeng

H-RAG at SemEval-2026 Task 8: Hierarchical Parent-Child Retrieval for Multi-Turn RAG Conversations

arXiv:2605.00631v1 Announce Type: new Abstract: We present H-RAG, our submission to SemEval-2026 Task 8 (MTRAGEval), addressing both Task A (Retrieval) and Task C (Generation with Retrieved Passages).

High-Speed Vision Improves Zero-Shot Semantic Understanding of Human Actions

arXiv:2605.00496v1 Announce Type: new Abstract: Understanding human actions from visual observations is essential for human--robot interaction, particularly when semantic interpretation of unfamiliar

Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications

arXiv:2605.00068v1 Announce Type: new Abstract: Inertial Confinement Fusion (ICF) holds transformative promise for sustainable, near-limitless clean energy, yet remains constrained by prohibitively hi

Information-geometric adaptive sampling for graph diffusion

arXiv:2605.00250v1 Announce Type: cross Abstract: Standard diffusion models for graph generation typically rely on uniform time-stepping, an approach that overlooks the non-homogeneous dynamics of dis