Search: “research”

Adaptive Node Feature Selection For Graph Neural Networks

arXiv:2510.03096v2 Announce Type: replace Abstract: We propose an adaptive node feature selection approach for graph neural networks (GNNs) that identifies and removes unnecessary features during trai

Adaptive Norm-Based Regularization for Neural Networks

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arXiv:2605.00171v1 Announce Type: cross Abstract: In this paper, we study norm-based regularization methods for neural networks. We compare existing penalization approaches and introduce two regulariz

researcharxiv-cs-lg

4 May 2026

Adaptive Querying with AI Persona Priors

arXiv:2605.00696v1 Announce Type: cross Abstract: We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within

ADVICE: Answer-Dependent Verbalized Confidence Estimation

arXiv:2510.10913v3 Announce Type: replace Abstract: Recent progress in large language models (LLMs) has enabled them to communicate their confidence in natural language, improving transparency and rel

Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts

arXiv:2605.00604v1 Announce Type: new Abstract: Sparse MoE routing fails at domain transitions, where the current token belongs to one distribution and the next to another. In a controlled experiment

AIDA-ReID: Adaptive Intermediate Domain Adaptation for Generalizable and Source-Free Person Re-Identification

arXiv:2605.00111v1 Announce Type: new Abstract: Person re-identification (Re-ID) aims to match images of the same individual across non-overlapping camera views and remains challenging due to domain s

Aitchison Embeddings for Learning Compositional Graph Representations

arXiv:2605.00716v1 Announce Type: new Abstract: Representation learning is central to graph machine learning, powering tasks such as link prediction and node classification. However, most graph embedd

Batch Normalization for Neural Networks on Complex Domains

arXiv:2605.00467v1 Announce Type: new Abstract: Riemannian neural networks have proven effective in solving a variety of machine learning tasks. The key to their success lies in the development of pri

Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe

arXiv:2605.00607v1 Announce Type: new Abstract: Probing is widely used to study which features can be decoded from language model representations. However, the common decoding probe approach has two l

Brain MR Image Synthesis with 3D Multi-Contrast Self-Attention GAN

arXiv:2604.00070v2 Announce Type: replace-cross Abstract: Complete and high-quality multi-modal Magnetic Resonance Imaging (MRI) is essential for accurate neuro-oncological assessment, as each contras

Broadband Wide Field of View Imaging with Computational Mirrors

arXiv:2605.00029v1 Announce Type: cross Abstract: Traditional glass-based optics are typically optimized for narrow spectral bands, such as the visible (400-700nm) or shortwave infrared (1000-1800nm).

Budget-Aware Routing for Long Clinical Text

arXiv:2605.00336v1 Announce Type: new Abstract: A key challenge for large language models is token cost per query and overall deployment cost. Clinical inputs are long, heterogeneous, and often redund

Budget Constraints as Riemannian Manifolds

arXiv:2605.00649v1 Announce Type: new Abstract: Assigning one of K options to each of N groups under a total cost budget is a recurring problem in machine learning, appearing in mixed-precision quanti

Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Features

arXiv:2601.22816v2 Announce Type: replace Abstract: Advances in generative modeling have recently been adapted to tabular data containing discrete and continuous features. However, generating mixed-ty

Certain Head, Uncertain Tail: Expert-Sample for Test-Time Scaling in Fine-Grained MoE

arXiv:2602.02443v2 Announce Type: replace Abstract: Test-time scaling improves LLM performance by generating multiple candidate solutions, yet token-level sampling requires temperature tuning that tra

Certifiable Factor Graph Optimization

arXiv:2603.01267v2 Announce Type: replace-cross Abstract: We show that the factor graph and certifiable estimation paradigms, which have thus far been treated as essentially independent in the literat

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

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

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

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