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On the Fragility of Data Attribution When Learning Is Distributed

arXiv:2605.15520v1 Announce Type: cross Abstract: Data attribution has become an important component of pricing, auditing, and governance in machine learning pipelines, yet most attribution methods im

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On the Power of Adaptivity for arepsilon-Best Arm Identification in Linear Bandits

DGX agent

arXiv:2605.15663v1 Announce Type: new Abstract: We study the minimax sample complexity of arepsilon-best arm identification in linear bandits. Given a compact action set X that spans R^d and an unknow

researcharxiv-cs-lg

18 May 2026

On the Stability of Growth in Structural Plasticity

arXiv:2605.15435v1 Announce Type: new Abstract: Standard deep-learning pipelines usually choose the network architecture before training and keep it fixed throughout optimization. In contrast, a model

One Pass Is Not Enough: Recursive Latent Refinement for Generative Models

arXiv:2605.15309v1 Announce Type: new Abstract: Despite remarkable progress, image generation is far from solved. The dominant metric, FID, conflates sample fidelity with mode coverage and is close to

Online Algorithms for Repeated Optimal Stopping: Balancing Baseline Guarantees and Regret

arXiv:2511.04484v2 Announce Type: replace-cross Abstract: We study the repeated optimal stopping problem, in which the same optimal stopping instance with an unknown distribution is solved repeatedly

Online Vector Quantized Attention

arXiv:2602.03922v3 Announce Type: replace Abstract: Standard sequence mixing layers used in language models struggle to balance efficiency and performance. Self-attention performs well on long context

Ontology for Policing: Conceptual Knowledge Learning for Semantic Understanding and Reasoning in Law Enforcement Reports

arXiv:2605.15978v1 Announce Type: cross Abstract: Law enforcement reports contain structured fields and written narratives. However, many incident facts that are needed for review, police training, an

Operator-Controlled 6G: From Connectivity Infrastructure to Guaranteed Digital Services

arXiv:2605.15553v1 Announce Type: cross Abstract: Sixth-generation mobile networks (6G) are approaching a structural inflection point. Five generations of vendor-led architectures have left operators

Optimizing Line Segment Inspection with Limited-Range Drones

arXiv:2605.15765v1 Announce Type: cross Abstract: Optimization problems with drones are widely studied in a variety of civilian tasks, mainly due to their ability to traverse rough terrains and to car

researcharxiv-cs-ro

Overfitting has a limitation: a model-independent generalization gap bound based on Renyi entropy

arXiv:2506.00182v3 Announce Type: replace-cross Abstract: Will further scaling up of machine learning models continue to bring success? A significant challenge in answering this question lies in under

Overlap-aware segmentation for topological reconstruction of obscured objects

arXiv:2510.06194v2 Announce Type: replace-cross Abstract: The separation of overlapping objects presents a significant challenge in scientific imaging. While deep learning segmentation-regression algo

PACER: Acyclic Causal Discovery from Large-Scale Interventional Data

arXiv:2605.15353v1 Announce Type: cross Abstract: Inferring the structure of directed acyclic graphs (DAGs) from data is a central challenge in causal discovery, particularly in modern high-dimensiona

PanoWorld: Geometry-Consistent Panoramic Video World Modeling

arXiv:2605.15391v1 Announce Type: cross Abstract: We present PanoWorld, a panoramic video world model that generates geometry-consistent 360egree video from a single image and a caption. Existing pano

Petri Net Induced Heuristic Search for Resource Constrained Scheduling

arXiv:2605.15983v1 Announce Type: new Abstract: We formulate the Resource-Constrained Project Scheduling Problem (RCPSP) as optimal search over the reachability graph of a Timed Transition Petri Net w

PhysBrain 1.0 Technical Report

arXiv:2605.15298v1 Announce Type: cross Abstract: Vision-language-action models have advanced rapidly, but robot trajectories alone provide limited coverage for learning broad physical understanding.

Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered

arXiv:2605.15622v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization, learning from finite differences of function evaluations without backpropagation, has recently regained attention in dee

Practical Validity Conditions for Byzantine-Tolerant Federated Learning

arXiv:2605.15887v1 Announce Type: new Abstract: Robust aggregation is the core operation in Byzantine-tolerant federated learning. To ensure the quality of aggregation independently of data distributi

PRB-RUPFormer: A Recursive Unified Probabilistic Transformer for Residual PRB Forecasting

arXiv:2605.15363v1 Announce Type: new Abstract: Accurate forecasting of residual Physical Resource Blocks (PRBs) is critical for proactive network slice provisioning, energy-efficient operation, and s

Pretraining Objective Matters in Extreme Low-Data FGVC: A Backbone-Controlled Study

arXiv:2605.15599v1 Announce Type: cross Abstract: Extreme low-data fine-grained classification is common in expert domains where labeling is expensive, yet practitioners still need principled guidance

Privacy Evaluation of Generative Models for Trajectory Generation

arXiv:2605.15246v1 Announce Type: new Abstract: Trajectory data is fundamental to modern urban intelligence, yet its sensitivity raises significant privacy concerns. Generative models such as Generati

Process Rewards with Learned Reliability

arXiv:2605.15529v1 Announce Type: cross Abstract: Process Reward Models (PRMs) provide step-level feedback for reasoning, but current PRMs usually output only a single reward score for each step. Down

Prompt Stability Scoring for Text Annotation with Large Language Models

arXiv:2407.02039v3 Announce Type: replace Abstract: Researchers are increasingly using language models (LMs) for text annotation. These approaches rely only on a prompt telling the model to return a g

RaPD: Resolution-Agnostic Pixel Diffusion via Semantics-Enriched Implicit Representations

arXiv:2605.15908v1 Announce Type: cross Abstract: Natural images are continuous, yet most generative models synthesize them on discrete grids, limiting resolution-flexible generation. Continuous neura

Reading the Cell, Designing the Cure: Perturbation-Conditioned Molecular Diffusion for Function-Oriented Drug Design

arXiv:2605.15243v1 Announce Type: cross Abstract: When reliable target structures are unavailable at scale or phenotypes arise from dysregulated pathways, transcriptomic perturbations provide a system

Reasoning Models Don't Just Think Longer, They Move Differently

arXiv:2605.15454v1 Announce Type: new Abstract: Reasoning-trained language models often spend more tokens on harder problems, but longer chains of thought do not show whether a model is merely computi

Regret and Sample Complexity of Online Q-Learning via Concentration of Stochastic Approximation with Time-Inhomogeneous Markov Chains

arXiv:2602.16274v2 Announce Type: replace Abstract: We present the first regret bound for classical online Q-learning in infinite-horizon discounted Markov decision processes (MDPs), without relying o

Rethinking Neural Network Learning Rates: A Stackelberg Perspective

arXiv:2605.15530v1 Announce Type: new Abstract: Neural networks are typically trained with a single learning rate across all layers. While recent empirical evidence suggests that assigning layer-speci

RIDE: Retinex-Informed Decoupling for Exposing Concealed Objects

arXiv:2605.15450v1 Announce Type: cross Abstract: Concealed Object Segmentation (COS) encompasses a family of dense-prediction tasks, including camouflaged object detection, polyp segmentation, transp

Robust Prior-Guided Segmentation for Editable 3D Gaussian Splatting

arXiv:2605.16065v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3D-GS) enables real-time 3D scene reconstruction but lacks robust segmentation for editing tasks such as object removal, extrac

RoiMAM: Region-of-Interest Medical Attention Model for Efficient Vision-Language Understanding

arXiv:2605.15561v1 Announce Type: new Abstract: Vision-Language Models (VLMs) facilitate medical visual question answering (MedVQA) by jointly interpreting images and text. However, existing models ty

SAE-RNA: A Sparse Autoencoder Model for Interpreting RNA Language Model Representations

arXiv:2510.02734v2 Announce Type: replace-cross Abstract: Deep learning, particularly with the advancement of Large Language Models, has transformed biomolecular modeling, with protein language models

Sampling-Based Global Optimal Control and Estimation via Semidefinite Programming

arXiv:2507.17572v3 Announce Type: replace Abstract: Global optimization has gained attraction over the past decades, thanks to the development of both theoretical foundations and efficient numerical r

researcharxiv-cs-ro

Sampling-Free Privacy Accounting for Matrix Mechanisms under Random Allocation

arXiv:2601.21636v3 Announce Type: replace Abstract: We study privacy amplification for differentially private model training with matrix factorization under random allocation (also known as the balls-

Seeing is Understanding: Unlocking Causal Attention into Modality-Mutual Attention for Multimodal LLMs

arXiv:2503.02597v3 Announce Type: replace-cross Abstract: Recent Multimodal Large Language Models (MLLMs) have demonstrated significant progress in perceiving and reasoning over multimodal inquiries,

Self-Prompting Diffusion Transformer for Open-Vocabulary Scene Text Editing via In-Context Learning

arXiv:2605.15523v1 Announce Type: new Abstract: Scene text editing aims to modify text in a target region of an image while preserving surrounding background style and texture. Existing methods rely s

Self-Supervised ImageNet Representations for In Vivo Confocal Microscopy: Tortuosity Grading without Segmentation Maps

arXiv:2603.15269v2 Announce Type: replace Abstract: The tortuosity of corneal nerve fibers are used as indication for different diseases. Current state-of-the-art methods for grading the tortuosity he

SFHand: Learning Embodied Manipulation by Streaming Egocentric 3D Hand Forecasting

arXiv:2511.18127v2 Announce Type: replace Abstract: Real-time 3D hand forecasting is a critical component for fluid human-computer interaction in applications like AR and assistive robotics. However,

SGNO: Spectral Generator Neural Operators for Stable Long Horizon PDE Rollouts

arXiv:2602.18801v2 Announce Type: replace Abstract: Autoregressive neural PDE surrogates predict future states by repeatedly applying a learned one-step operator. This is a simple and widely used meth

Sharp Spectral Thresholds for Logit Fixed Points

arXiv:2605.15651v1 Announce Type: cross Abstract: Softmax feedback systems are a common mathematical core of entropy-regularized reinforcement learning, logit game dynamics, population choice, and mea

Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models

arXiv:2602.01970v2 Announce Type: replace Abstract: Reinforcement learning enhances the reasoning capabilities of large language models but often involves high computational costs due to rollout-inten

Smoothie: Smoothing Diffusion on Token Embeddings for Text Generation

arXiv:2505.18853v2 Announce Type: replace Abstract: Diffusion models have achieved state-of-the-art performance in generating images, audio, and video, but their adaptation to text remains challenging

Sparse Autoencoders enable Robust and Interpretable Fine-tuning of CLIP models

arXiv:2605.15961v1 Announce Type: new Abstract: Large-scale pre-trained vision-language models like CLIP demonstrate remarkable zero-shot performance across diverse tasks. However, fine-tuning these m

Spectral Priors vs. Attention: Investigating the Utility of Attention Mechanisms in EEG-Based Diagnosis

arXiv:2605.15433v1 Announce Type: new Abstract: Electroencephalograph (EEG) timeseries signals are characterized by significant noise and coarse spatial resolution, which complicates the classificatio

Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR

arXiv:2507.15778v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective post-training method for improving the reasoning abilities of Large La

Stochastic Compositional Optimization via Hybrid Momentum Frank--Wolfe

arXiv:2605.15350v1 Announce Type: cross Abstract: Stochastic compositional optimization minimizes objectives of the form min_{m{x} in X} F(m{f}(m{x}), m{x}), where m{f} is accessible only through nois

Stochastic Non-Smooth Convex Optimization with Unbounded Gradients

arXiv:2605.15522v1 Announce Type: cross Abstract: Much of the existing theory on first-order non-smooth optimization is built on a restrictive assumption that the gradients of the objective function a

Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis

arXiv:2603.05917v3 Announce Type: replace-cross Abstract: Stock market prediction presents considerable challenges for investors, financial institutions, and policymakers operating in complex market e

Structured Jacobian Construction for Motion Optimization with High-Order Time Derivatives in Multi-Link Systems

arXiv:2605.15845v1 Announce Type: new Abstract: This paper presents a novel framework for Jacobian computation in motion optimization problems involving multi-link systems, where physical quantities a

researcharxiv-cs-ro