Search: “research”

TTE-CAM: Self-Explainable Class Activation Maps for Pretrained Black-Box CNNs

arXiv:2603.26885v2 Announce Type: replace Abstract: Convolutional neural networks (CNNs) achieve state-of-the-art performance in medical image analysis yet remain opaque, limiting adoption in high-sta

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Two Degree-of-Freedom Vibratory Transport in a Grasp

DGX agent

arXiv:2605.30780v1 Announce Type: new Abstract: In this paper, we use asymmetric vibrations to demonstrate two degree-of-freedom (DoF) in-hand manipulation of grasped parts. The asymmetric vibrations

researcharxiv-cs-ro

1 Jun 2026

Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling

arXiv:2605.30376v1 Announce Type: cross Abstract: Modern time series architectures face a fundamental trade-off: channel-independent models scale well with increasing data volume but ignore critical i

Unifying and Optimizing Data Values for Selection via Sequential Decision-Making

arXiv:2502.04554v2 Announce Type: replace Abstract: Data selection has emerged as a crucial downstream application of data valuation, yet the theoretical foundations for using data values in selection

UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis

arXiv:2510.15710v3 Announce Type: replace Abstract: Medical workflows routinely combine reading images with producing visual and textual outputs, making both image understanding and generation central

Universal Decision Learners

arXiv:2605.30694v1 Announce Type: new Abstract: Many theories of decision making -- planning, reinforcement learning, causal intervention, online learning, and game-theoretic equilibrium -- turn local

Universal Multiclass Transductive Online Learning

arXiv:2605.30479v1 Announce Type: new Abstract: We consider the problem of universal transductive online classification with a possibly unbounded label space. This setting considers online learning, w

Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints

arXiv:2605.30825v1 Announce Type: cross Abstract: Unlearning in diffusion models aims to remove undesirable data or concepts while preserving the utility of pretrained models -- two fundamentally conf

Unlearning's Blind Spots: Over-Unlearning and Prototypical Relearning Attack

arXiv:2506.01318v4 Announce Type: replace-cross Abstract: Machine unlearning (MU) aims to expunge a designated forget set from a trained model without costly retraining, yet the existing techniques ov

Unlocking Fine-Grained Translation Quality Estimation in LRMs through Synergistically Evolving Implicit and Explicit Reasoning

arXiv:2605.31378v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) still struggle with fine-grained translation quality estimation (QE), even with long reasoning chains. We argue that LRMs

Unsupervised Diffusion Solver for Combinatorial Optimization via Combinatorial Adjoint Matching

arXiv:2605.30920v1 Announce Type: new Abstract: Diffusion-based neural solvers have shown strong promise for combinatorial optimization (CO), but existing methods typically rely on supervised training

Updating the standard neuron model in artificial neural networks

arXiv:2605.30370v1 Announce Type: cross Abstract: From their inception in the 1950s, artificial neural networks (ANNs) started using the so-called point neuron model then prevalent in neuroscience, ho

VAD-GS: Visibility-Aware Densification for 3D Gaussian Splatting in Dynamic Urban Scenes

arXiv:2510.09364v2 Announce Type: replace Abstract: 3D Gaussian splatting (3DGS) has demonstrated impressive performance in synthesizing high-fidelity novel views. Nonetheless, its effectiveness criti

Variance-Reduced Model Predictive Path Integral via Quadratic Model Approximation

arXiv:2602.03639v2 Announce Type: replace Abstract: Sampling-based controllers, such as Model Predictive Path Integral (MPPI) methods, offer substantial flexibility but often suffer from high variance

researcharxiv-cs-ro

Video-MTR: Reinforced Multi-Turn Reasoning for Long Video Understanding

arXiv:2508.20478v2 Announce Type: replace Abstract: Long-form video understanding, characterized by long-range temporal dependencies and multiple events, remains a challenge. Existing methods often re

View Space: Learning Representation across Arbitrary Graphs

arXiv:2512.11561v2 Announce Type: replace Abstract: Generalizing pretrained models to unseen datasets without retraining is a central challenge toward foundation models. Achieving fully inductive infe

VLM3: Vision Language Models Are Native 3D Learners

arXiv:2605.30561v1 Announce Type: cross Abstract: Vision Language Models (VLMs) enable a unified model to solve various vision tasks through prompting. They have shown promising performance in semanti

VolFill: Single-View Amodal 3D Scene Reconstruction with Volumetric Flow Matching

arXiv:2605.31466v1 Announce Type: new Abstract: Reconstructing the complete geometry of a scene from a single RGB image remains challenging - especially when inferring hidden structures where visual e

Wall-Clock Complexity for Zeroth-Order Optimization with Tunable Oracle Fidelity

arXiv:2605.31346v1 Announce Type: cross Abstract: Zeroth-order (black-box) optimization is applied when gradients are unavailable and objective evaluations rely on expensive simulations. In many such

Weights to Code: Extracting Interpretable Algorithms from the Discrete Transformer

arXiv:2601.05770v3 Announce Type: replace-cross Abstract: Algorithm extraction aims to synthesize executable programs directly from models trained on algorithmic tasks, enabling de novo recovery of ex

What Does Preference Learning Recover from Pairwise Comparison Data?

arXiv:2602.10286v2 Announce Type: replace Abstract: Pairwise preference learning is central to machine learning, with recent applications in aligning language models with human preferences. A typical

What Gets Unmasked First? Trajectory Analysis of Diffusion Models for Graph-to-Text Generation

arXiv:2605.31564v1 Announce Type: cross Abstract: We present the first systematic study of masked diffusion language models (MDLMs) for graph-to-text generation. We analyze MDLM generation trajectorie

What is Missing? Explaining Neurons Activated by Absent Concepts

arXiv:2603.09787v2 Announce Type: replace Abstract: Explainable artificial intelligence (XAI) aims to provide human-interpretable insights into the behavior of deep neural networks (DNNs), typically b

When Are Multimodal Predictions Biologically Supported? A Diagnostic Evaluation Framework

arXiv:2605.31504v1 Announce Type: new Abstract: Multimodal models in oncology can produce accurate predictions, but accurate prediction does not reveal whether the model has learned biology that is sh

When More Data Doesn't Help: Limits of Adaptation in Multitask Learning

arXiv:2601.20774v2 Announce Type: replace Abstract: Multitask learning and related frameworks have achieved tremendous success in modern applications. In multitask learning problem, we are given a set

Wind Turbine Maintenance Log Labelling Framework: LLM-Driven Data Correction and Enrichment via Semantic Extraction of Reliability Intelligence

arXiv:2605.31281v1 Announce Type: new Abstract: As wind turbine fleets age, data-driven reliability engineering is essential to optimise their operation and maintenance for service life extension and

XOResNet: Exclusive-OR Meta-Residuals Facilitate Deep Spiking Neural Networks Learning

arXiv:2605.30362v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) hold promise for demonstrating superior learning and representation capabilities in deep models. Given the tremendous s

Zeroth-Order Non-Log-Concave Sampling with Variance Reduction and Applications to Inverse Problems

arXiv:2605.30573v1 Announce Type: new Abstract: Sampling from high-dimensional, non-log-concave distributions with unnormalized densities remains a fundamental challenge in machine learning, particula

3DVLA: Enhancing Vision-Language-Action Models via 3D Spatial and Instance Understanding

arXiv:2605.29416v1 Announce Type: cross Abstract: Vision-Language-Action models have achieved remarkable progress in robotic manipulation, yet they suffer from a critical limitation: a lack of 3D scen

4DPC^2hat: Towards Dynamic Point Cloud Understanding with Failure-Aware Bootstrapping

arXiv:2602.03890v2 Announce Type: replace Abstract: Point clouds provide a compact and expressive representation of 3D objects, and have recently been integrated into multimodal large language models

A Complete Loss Landscape Analysis of Regularized Deep Matrix Factorization

arXiv:2506.20344v3 Announce Type: replace-cross Abstract: Despite its wide range of applications across various domains, the optimization foundations of deep matrix factorization (DMF) remain largely

A Composable Multimodal Framework for cine CMR-Text-Driven Prediction of Heart Failure Outcomes

arXiv:2502.16548v3 Announce Type: replace-cross Abstract: Objective. Heart failure is one of the leading causes of death worldwide, with millions of deaths each year, according to data from the World

A Deep Learning Iterative Framework for Sentinel-1 Stripmap Enhancement Based on Azimuth Doppler Decomposition

arXiv:2605.29088v1 Announce Type: new Abstract: Synthetic Aperture Radar (SAR) imagery enables all-weather, day-and-night Earth observation; however, it remains difficult to interpret due to speckle n

A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search

arXiv:2602.11171v2 Announce Type: replace-cross Abstract: Fine-tuning Large Language Models (LLMs) with Low-Rank Adaptation (LoRA) offers a resource-efficient way to personalize or specialize. However

Applications

A Matter of Interest: Understanding Interestingness of Math Problems in Humans and Language Models

arXiv:2511.08548v2 Announce Type: replace Abstract: The evolution of mathematics is shaped importantly by interestingness: researchers choose which problems to pursue, and students choose which proble

applicationsarxiv-cs-ai

A Systematic Evaluation of Molecular Mixture Behavior Prediction

arXiv:2605.29698v1 Announce Type: new Abstract: Machine learning for molecular property prediction has focused largely on pure compounds, even though many practical applications depend on mixtures wit

A unified deeplearning framework for contrast-phase-specific virtual monochromatic imaging

arXiv:2605.29753v1 Announce Type: cross Abstract: Dual-energy CT (DECT) enables virtual monochromatic imaging (VMI) and improved contrast resolution, but its clinical adoption is limited by hardware c

Accelerating Constrained Decoding with Token Space Compression

arXiv:2605.29986v1 Announce Type: new Abstract: To guarantee that an LLM's outputs conform to a specified structure, context-free grammar (CFG) decoding engines force the selection of next tokens that

Achieving Linear Speedup for Composite Federated Learning

arXiv:2602.03357v2 Announce Type: replace Abstract: This paper proposes FedNMap, a normal map-based method for composite federated learning, where the objective consists of a smooth loss and a possibl

Active Continual Learning with Metaplastic Binary Bayesian Neural Networks

arXiv:2605.30198v1 Announce Type: new Abstract: Always-on edge systems must keep learning as conditions change under tight compute budgets and must detect unreliable predictions. Bayesian binary neura

Adaptive Exponential Integration for Stable Gaussian Mixture Black-Box Variational Inference

arXiv:2601.14855v3 Announce Type: replace Abstract: Black-box variational inference (BBVI) with Gaussian mixture families offers a flexible approach for approximating complex posterior distributions w

Adaptive Targeted Dynamic Chunking for Tokenization-Free Hierarchical Model

arXiv:2605.30080v1 Announce Type: new Abstract: Tokenization-free hierarchical models are emerging as a promising alternative to traditional Large Language Models (LLMs), addressing inherent preproces

AdaState: Self-Evolving Anchors for Streaming Video Generation

arXiv:2605.30349v1 Announce Type: new Abstract: Autoregressive video diffusion models generate streaming video by producing frames sequentially, conditioning each chunk on previously generated content

Aggregate Models, Not Explanations: Improving Feature Importance Estimation

arXiv:2602.11760v2 Announce Type: replace-cross Abstract: Feature-importance methods show promise in transforming machine learning models from predictive engines into tools for scientific discovery. H

Ambient-robust Inverse Rendering using Active RGB-NIR Imaging

arXiv:2605.30250v1 Announce Type: new Abstract: Inverse rendering aims to reconstruct geometry and reflectance of objects from images. Despite recent progress, existing methods often produces inaccura

An accuracy-aware extension to LRP-based pruning for CNNs to prevent cascading accuracy degradation in data-scarce transfer learning

arXiv:2511.10861v3 Announce Type: replace-cross Abstract: Convolutional Neural Networks (CNNs) pre-trained on large-scale datasets such as ImageNet are widely used as feature extractors to construct h

Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception

arXiv:2605.29064v1 Announce Type: new Abstract: We study how persona prompting shapes language generated by multimodal large language models in an urban perception setting. Using 59,808 annotations fr

Anchorless Diversification for Parallel LLM Ideation

arXiv:2605.30150v1 Announce Type: new Abstract: LLMs are increasingly used to generate candidate-idea pools for creative tasks where broad exploration is valuable. Parallel inference can be attractive