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Do Emotion Concepts Generalize Across Sources, Modalities, and Architectures in Vision-Language Models?

arXiv:2609.34742v1 Announce Type: new Abstract: Recent studies suggest that large language models encode emotion concepts as structured internal representations, but most existing work focuses on text

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Do Not Cut When Uncertain: Rejectable and Calibrated Decision Heads for VLA Policies in Robotic Harvesting

DGX agent

arXiv:2609.35039v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies trained with behavior cloning or flow matching are optimized to output an action trajectory, but they cannot expre

researcharxiv-cs-ro

29 Sept 2026

Do Temporal Link Predictors Need Learned Memory? A Smoothed-Count Baseline with a Handful of Parameters

arXiv:2609.35364v1 Announce Type: new Abstract: Many temporal link predictors summarize past interactions through learned node representations. We examine whether simple counts of recurring interactio

Does Joint-Embedding Predictive Architecture Pretraining Help Time Series Forecasting?

arXiv:2609.31680v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPA) have emerged as a promising self-supervised pretraining paradigm for time series, learning representatio

Does Native 3D Texture Generation Necessarily Require 3D Assets for Training?

arXiv:2609.34621v1 Announce Type: new Abstract: Native 3D texture generation synthesizes colors directly in 3D space for a given geometry, conditioned on multi-view reference images. It is generally b

Does Transolver really need a Transformer?

arXiv:2609.32525v1 Announce Type: new Abstract: The widely used Transolver family of neural operators is based on physics-attention, which softly assigns the points of an unstructured mesh to a small

Does Vision-Language Pretraining Granularity Matter? A Controlled Evaluation of Vision-Language Objectives Across Chest X-Ray Interpretation Tasks

arXiv:2609.31985v1 Announce Type: new Abstract: Vision-language pretraining objectives differ in the spatial granularity of their supervision, yet the implications of this distribution for chest X-ray

Don't stop me now: How Validation Criteria Affect Checkpoint Selection and Early Stopping

arXiv:2602.22107v2 Announce Type: replace Abstract: Checkpoint selection is a standard component of neural network training, yet the validation criterion used to select a checkpoint is often chosen he

dOPT: Differentiating Conic Optimization via Geometric Reduction

arXiv:2609.33828v1 Announce Type: new Abstract: Optimization layers enable the incorporation of structured constraints and decision problems into learning systems. Training such systems requires diffe

DP-Rec: Towards Dynamic Patching for Efficient Long-Sequence Recommendation

arXiv:2609.32215v1 Announce Type: new Abstract: Transformers have redefined sequential recommendation by effectively modeling dynamic user behaviors and long-range dependencies. However, they remain i

DPAMixerSR: An Efficient Degradation-Pattern-Aware Model for Image Super-Resolution

arXiv:2609.32705v1 Announce Type: new Abstract: While content-adaptive schemes have delivered notable advances in image super-resolution (SR), existing approaches typically focus on texture complexity

DraftAttention2: Fast Video Diffusion with Low-Resolution-Guided Mixed-Precision Attention

arXiv:2609.32628v1 Announce Type: new Abstract: Video generation has broad applications in content creation and entertainment. Diffusion transformers have advanced the quality of generated videos, but

DreamingGoose: Staged Distillation from Autoregressive Transformers to Bidirectional Recurrent Diffusion Language Models

arXiv:2609.34253v1 Announce Type: new Abstract: Pretrained autoregressive Transformers represent a large sunk investment in compute. Existing conversion methods reuse that investment by changing eithe

DRIFT: Disentangled Responsive-Invariant Flow Transport for Single-Cell Perturbation Prediction

arXiv:2609.35106v1 Announce Type: new Abstract: Predicting cellular responses to perturbations is a central problem in cellular biology, with broad applications in systems biology and drug discovery.

DS-VLA: A Dendritic-inspired Vision-Language-Action Model for Robust Action Control

arXiv:2609.32253v1 Announce Type: new Abstract: Vision-language-action (VLA) models have achieved strong performance in language-conditioned manipulation, yet success under nominal evaluation does not

Dynamic Buffers: Cost-Efficient Planning for Tabletop Rearrangement with Stacking

arXiv:2509.22828v2 Announce Type: replace Abstract: Rearranging objects in cluttered tabletop environments remains a long-standing challenge in robotics. Classical planners often generate inefficient,

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

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

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 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