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Dynamic Image Prompt Adapter for Scalable Zero-shot Personalized Text-to-Image Generation

arXiv:2512.09814v2 Announce Type: replace Abstract: Personalized Text-to-Image (PT2I) generation aims to produce customized images based on reference images. A prominent interest pertains to the integ

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Dynamic Model Identification and Gravity Compensation for the dVRK-Si Patient Side Manipulator

DGX agent

arXiv:2603.12099v2 Announce Type: replace Abstract: The da Vinci Research Kit (dVRK) has been widely used for research in robot-assisted surgery over the past decade, and its latest generation, the dV

researcharxiv-cs-ro

29 Sept 2026

DynamicDx: Evaluating Evidence Acquisition in Video-Based Diagnosis

arXiv:2609.32957v1 Announce Type: new Abstract: Diagnosing a patient from video requires more than recognizing the sign: a vision-language model must turn what it sees into hypotheses, questions and t

Edge-Level Automorphism in GNNs: A Quantitative Framework and Effective Designs For Link Prediction

arXiv:2609.34729v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are effective for learning node and link embeddings through permutation-equivariant aggregation. However, standard GNNs col

Editable Map-Conditioned Trajectory Generation for Human Mobility Simulation

arXiv:2609.32360v1 Announce Type: new Abstract: Geospatial simulation of infrastructure interventions requires mobility generators that respond directly to edited maps, yet many data-driven generators

EEG-Based Motor Imagery BCI Algorithms and Technologies: A Review

arXiv:2609.32930v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) have emerged as transformative technologies that enable direct communication between the brain and external devices.

Efficient Bezier Velocity Optimization for Free-Floating Space Manipulators

arXiv:2609.31880v1 Announce Type: new Abstract: We present FAVOR (Free-floating Arm Velocity Optimization with Recursive Sensitivities), a planner for collision-free reaching, tracking, and prescribed

Efficient Support Recovery of Mixtures of Sparse Linear Classifiers with Less Measurements

arXiv:2609.32176v1 Announce Type: new Abstract: The support recovery problem in mixture of linear classifiers intends to identify which features actually matter when data is generated by a mixture of

Endo-TSR: Temporal Spectral Modeling of Appearance and Motion for Endoscopic Reconstruction

arXiv:2609.32399v1 Announce Type: new Abstract: Endoscopic scene reconstruction requires modeling tissue motion and temporal appearance while recovering fine surface detail. Deformable Gaussian models

Enhancing generalization in endwall film cooling prediction: Incorporating the superposition principle into transformer-based neural operators

arXiv:2609.31633v1 Announce Type: new Abstract: In this study, a physics-enhanced neural operator framework is proposed to enhance the generalization prediction ability of the cooling layout of a turb

Enhancing Visual Reasoning in Chest X-Ray Report Generation Using Reinforcement Learning

arXiv:2609.31911v1 Announce Type: new Abstract: Medical report generation has made significant progress with the rise of modern vision-language models and the growing availability of large-scale medic

Epistemic Learning from Imprecise Annotation

arXiv:2609.34285v1 Announce Type: new Abstract: Imprecise annotations may support several plausible labelling distributions, yet learning methods often resolve this ambiguity into a single predictive

Evaluating Dynamical Fidelity through Predictive Structure in Physical Representations

arXiv:2609.34627v1 Announce Type: new Abstract: Machine-learning models for physical systems are currently evaluated primarily through errors between predicted and reference states and, increasingly,

Evaluating Hierarchy-Aware Deep Learning for the Recognition of Tironian Notes

arXiv:2609.35277v1 Announce Type: new Abstract: Tironian notes are generally regarded as the first Latin shorthand system and are notable for their large, fine-grained symbol inventory. Their high vis

Evaluating Single and Multi-Omics Based Explainable Artificial Intelligence (MOXAI) for Molecular Subclass Classification of Adult-Type Diffuse Gliomas

arXiv:2609.32190v1 Announce Type: new Abstract: DNA methylation (DNAM) profiling has emerged as a powerful diagnostic tool for classifying brain and solid tumors. However, existing computational model

Evasion Attacks on Cost-Utility-Based Adversarial Training for Online AutoML in IoT Networks

arXiv:2609.31981v1 Announce Type: cross Abstract: As Internet of Things (IoT) networks increasingly depend on machine learning for anomaly, malware, intrusion detection, and network monitoring, such s

Evidence Before Accuracy: A MRI-PET Fusion Network for Alzheimer Disease Classification with Causal Regional Validation

arXiv:2609.34520v1 Announce Type: new Abstract: Deep learning models for Alzheimer disease (AD) classification routinely report near-perfect discrimination, yet few are shown to rest on AD-relevant ne

EviSplat: Preserving Multi-View Evidence in 3D Gaussian Splatting for Open-Vocabulary Segmentation

arXiv:2609.34853v1 Announce Type: new Abstract: Open-vocabulary 3D scene understanding enables object localization and segmentation from free-form text queries without a fixed category vocabulary. Man

Explaining Hyperbolic Neural Networks via Geometry-Aware Relevance Propagation

arXiv:2609.35128v1 Announce Type: new Abstract: Hyperbolic neural networks introduce geometric operations that require explicit treatment in relevance propagation. Equivalent geometric realizations ca

Exterior complex scaling enables physics-informed neural networks for quantum scattering

arXiv:2602.04553v2 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving differential equations, yet their application to nuclear scatteri

Extremely Fast and Compact Binary Graph Representations via Randomized Operator Sketching

arXiv:2609.32641v1 Announce Type: new Abstract: Graph neural networks typically rely on dense, floating-point node representations, which can impose substantial memory and computational costs. Binary

f-FUM: Federated Unlearning via min--max and f-divergence

arXiv:2602.06187v2 Announce Type: replace Abstract: Federated learning (FL) enables collaborative training while keeping raw data on clients. Deletion requests and the discovery of poisoned data creat

FAST-Brain: A Flow-Aligned Spatio-Temporal Surrogate Brain Model

arXiv:2609.34354v1 Announce Type: new Abstract: Modeling resting-state functional magnetic resonance imaging (rs-fMRI) data is crucial for understanding brain-wide neural activity. However, traditiona

Fast Learning Rate Transfer in Shallow Linear Networks at Growing Training Horizons

arXiv:2609.35029v1 Announce Type: new Abstract: Hyperparameter transfer across model width can substantially reduce the cost of tuning large neural networks, but its behavior when the training horizon

Fast-Slow Thinking RM: Efficient Integration of Scalar and Generative Reward Models

arXiv:2603.20212v2 Announce Type: replace-cross Abstract: Reward models (RMs) are critical for aligning Large Language Models via Reinforcement Learning from Human Feedback (RLHF). While Generative Re

FEAT: Free energy Estimators with Adaptive Transport

arXiv:2504.11516v4 Announce Type: replace-cross Abstract: We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation -- a critical challenge across

Feature Space Guidance for Breast Cancer Classification in DCE-MRI

arXiv:2609.32555v1 Announce Type: new Abstract: Dynamic contrast enhanced breast MRI (DCE-MRI) is a powerful clinical tool for breast cancer detection, providing high resolution anatomical detail toge

Feedback Makes Perfect: A Closed-Loop Framework for NL-to-STL Translation

arXiv:2609.33287v1 Announce Type: cross Abstract: Signal Temporal Logic (STL) enables rigorous verification and control of cyber-physical systems, but writing correct specifications requires expertise

Fiber-Normalized Manipulability and Determinant Proxies: Intrinsic Redundancy Optimization Across and Within Task Fibers

arXiv:2609.32079v1 Announce Type: new Abstract: This work establishes that determinant-based manipulability is an exact objective for fixed-task redundancy optimization, despite its dependence on task

Filtered Conformal Ellipsoids for Graph-Native Time Series

arXiv:2606.17014v2 Announce Type: replace Abstract: Joint prediction sets for multivariate time series should control a single event while adapting to cross-coordinate dependence. We study filtered co

Fine Until Fine-Tuned: Repeated Solutions Make Reasoning Fragile

arXiv:2609.33559v1 Announce Type: new Abstract: Recipes such as s1 and LIMO teach a model to reason with little data by showing it the same thousand or fewer worked solutions many times over. Judged w

Finite Probes Suffice: Identifiability and Universality for Weight-Space Learning

arXiv:2609.33901v1 Announce Type: new Abstract: Learning properties of neural networks has recently attracted growing interest, with existing approaches operating either directly on network parameters

Fiona: Accelerating FHE Inference with Packing-Aware Ternary Weights

arXiv:2609.35352v1 Announce Type: new Abstract: Fully homomorphic encryption (FHE) enables neural network inference directly on encrypted inputs, but it remains orders of magnitude slower than plainte

FOCUS: Fixed-Confidence Online Causal Learning Using Sequential Adaptive Interventions

arXiv:2609.32165v1 Announce Type: cross Abstract: We study fully online fixed-confidence causal discovery without any historical observational data. Starting from zero samples, the learner sequentiall

Focus on Likely Classes for Test-Time Prediction

arXiv:2505.03819v4 Announce Type: replace Abstract: We ask: Can focusing on likely classes of a single, in-domain sample improve accuracy? Prior work argued 'no', we answer 'yes' on average. Standard

Forecast-Necessary Causal Discovery for Nonlinear Political Panel Data: Feedback, Functional Form, and the Dynamics of Democratization

arXiv:2609.34115v1 Announce Type: cross Abstract: A non-significant coefficient in a dynamic panel model need not imply the absence of a relationship. It may instead reflect heterogeneous effects aver

ForeFly: A Dual-Horizon World Action Model for Aerial Vision-Language Navigation

arXiv:2609.33581v1 Announce Type: new Abstract: Aerial Vision-Language Navigation (AVLN) requires UAVs to maintain reliable instruction following over long trajectories in complex 3D environments. How

ForensicZoom: Adaptive Visual Inspection with Multimodal LLMs for Industrial-Grade Face Forgery Detection

arXiv:2609.31661v1 Announce Type: new Abstract: Reliable face forgery detection is critical to the security of online identity verification systems, where missed attacks compromise security and excess

Frequency-Domain AI-Generated Image Detection: Exploring Decoder and Channel Attention for Feature Refinement

arXiv:2609.31723v1 Announce Type: new Abstract: With the rapid progress of AI, the number of AI-generated images has increased significantly in recent years. However, the increasing variety of image g

From Distributions to Stochastic Processes: Neural Approximation of Measure-Valued Maps

arXiv:2609.33649v1 Announce Type: new Abstract: Learning mappings between probability distributions arises naturally when inputs and outputs are represented by populations of samples rather than indiv

From Feed-Forward to Flow: Unifying Reconstruction and Generation Is Easier Than You Think

arXiv:2609.32761v1 Announce Type: new Abstract: Reconstruct where the images provide evidence, and generate where they do not: recent success of spatial world models such as Atlas (World Labs Team, 20

From Grey-Box to Green-Box: When can Physics-Informed Machine Learning Reduce Carbon Footprints in Structural Health Monitoring?

arXiv:2609.33387v1 Announce Type: new Abstract: Machine learning plays an increasingly vital role in engineering, but the corresponding increase in compute time is not without environmental cost. Phys

From Language to Task Maps: Compiling Semantic Relations While Preserving Task-Relevant Freedom

arXiv:2609.34412v1 Announce Type: new Abstract: Natural-language manipulation instructions specify qualitative relations, whereas continuous controllers require state-evaluable task quantities, differ

From PDF to Evidence: Structure-Aware Retrieval for Clinical Practice Guidelines

arXiv:2609.33447v1 Announce Type: cross Abstract: Guideline documents are published as unstructured PDFs whose evidence is locked in visual structures---tables, flowcharts, and graded recommendations-

From Perception to Integration: Revisiting the Internal Dynamics of Reasoning in Vision-Language Models

arXiv:2609.34809v1 Announce Type: new Abstract: Vision-language models (VLMs) can answer simple visual questions, but often struggle when one question requires several visual judgments. We study this

From Pixel Generation to Topological Inference: Structural Dual Super-Resolution for Trustworthy Cross-Physical-Domain Trabecular Morphology Learning

arXiv:2609.34716v1 Announce Type: new Abstract: Clinical CT and UHRCT cannot resolve individual trabeculae, whereas synchrotron radiation microCT (SR{mu}CT) provides 3.2{mu}m high-resolution reference

From Soft Targets to Reward Signals: How Assignment and Reward Objectives Interact

arXiv:2609.34850v1 Announce Type: cross Abstract: Soft preference targets specify supervision strength, and reward objectives convert that strength into learned reward signals. A central design questi

From UNI2-h to ConvNeXt-T: Lightweight Nuclei Instance Segmentation via Knowledge Distillation

arXiv:2609.35203v1 Announce Type: new Abstract: Nuclei instance segmentation is a core task in digital pathology, yet high-accuracy models rely on large vision transformer (ViT) encoders whose inferen