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DynaCF: Mitigating Shortcut Learning in Reward Models via Dynamic Counterfactual Sensitivity

arXiv:2606.09043v1 Announce Type: new Abstract: Reward models trained from pairwise preferences often exploit superficial shortcut cues rather than learning true response quality. We propose DynaCF, a

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Echo-Memory: A Controlled Study of Memory in Action World Models

DGX agent

arXiv:2606.09803v1 Announce Type: new Abstract: We present extbf{Echo-Memory}, a controlled study of memory mechanisms in action-conditioned world models. These models generate multi-segment videos fr

researcharxiv-cs-cv

9 Jun 2026

Edge-Constrained UAV Small-Object Detection with P2 Enhancement and Quantum-Inspired Lightweight Structure Search

arXiv:2606.09081v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) object detection requires compact detectors that retain small-object details under onboard computation and memory constrai

Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization

arXiv:2510.10028v2 Announce Type: replace-cross Abstract: The rapid advancement of Low-Altitude Economy Networks (LAENets) has enabled a variety of applications, including aerial surveillance, environ

Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth

arXiv:2606.09539v1 Announce Type: new Abstract: Spatio-temporal graph neural networks (STGNNs) have become the dominant approach for traffic prediction, yet their computational requirements pose chall

EgoPriMo: Egocentric Motion Generation for Interactive Humanoid Control

arXiv:2606.08495v1 Announce Type: cross Abstract: Humanoid robots require whole-body motions that adapt to scene context, task requirements, and user intent. Motion tracking reproduces specified traje

EinSort: Sorting is All We Need for Tensorizing LLM

arXiv:2606.08565v1 Announce Type: cross Abstract: Tensor networks provide efficient representations for compressing large neural networks. By carefully designing shapes and topologies, they can signif

Embedded Graph Convolutional Networks for Real-Time Event Data Processing on SoC FPGAs

arXiv:2406.07318v3 Announce Type: replace Abstract: The utilisation of event cameras represents an important and swiftly evolving trend aimed at addressing the constraints of traditional video systems

Emergence via Phase Transitions: Mechanism Landscapes and Universal Convergence Across Complex Systems

arXiv:2606.07563v1 Announce Type: cross Abstract: Across machine learning, biology, and physics, independently evolving systems often converge toward strikingly similar high-level structures despite r

Enabling KV Caching of Shared Prefix for Diffusion Language Models

arXiv:2606.07571v1 Announce Type: cross Abstract: Key-value (KV) caching for shared prefixes is essential for high-throughput large language model (LLM) serving, but it faces critical challenges in em

End-to-End Control of a Powered Knee-Ankle Prosthesis Towards Unified, Tuning-Free Assistance

arXiv:2606.07902v1 Announce Type: new Abstract: Powered prostheses conventionally rely on impedance controllers that require extensive manual tuning and explicit mode classification. In this work, we

researcharxiv-cs-ro

End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout

arXiv:2606.09792v1 Announce Type: new Abstract: End-to-end co-optimization of optical front-ends (e.g. metasurfaces) and neural network back-ends has been widely applied to imaging tasks, yet a formal

ePC: Fast and Deep Predictive Coding in Digital Simulation

arXiv:2505.20137v5 Announce Type: replace-cross Abstract: Predictive Coding (PC) offers a brain-inspired alternative to backpropagation for neural network training, described as a physical system mini

EPS3D: End-to-End Feed-Forward 3D Panoptic Segmentation

arXiv:2606.08980v1 Announce Type: new Abstract: This paper introduces EPS3D, a new end-to-end feed-forward framework for open-vocabulary 3D panoptic segmentation. Unlike existing methods relying on ad

EssentialGIN: a new approach for gene essentiality prediction based on graph isomorphism neural networks

arXiv:2606.07700v1 Announce Type: cross Abstract: Background: Prediction of essential genes (proteins), is a basic and challenging problem but at the same time very costly and time-consuming in wet-la

Estimate Collapsibility of Causal Effects in Completed Partial DAGs via Strong d-Convex Hulls

arXiv:2606.08941v1 Announce Type: cross Abstract: This paper proposes a collapsible method for estimating causal effects that maintains the estimator's consistency before and after marginalization ove

Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles

arXiv:2606.09718v1 Announce Type: cross Abstract: Diffusion models have demonstrated remarkable generative capabilities and have also emerged as powerful self-supervised representation learners, yet t

EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning

arXiv:2606.07702v1 Announce Type: cross Abstract: The heterogeneity of client data and systems makes it difficult to achieve satisfactory convergence speed and robustness in federated learning with ra

ExDet: Open-Domain Open-Vocabulary Detection with Cross-modal Extrapolation and Rectification

arXiv:2606.09360v1 Announce Type: new Abstract: Open-domain open-vocabulary detection (ODOVD) requires detectors to generalize to both novel categories and unseen domains, making it more challenging t

Extending Ontologies: From Dense Embeddings to Hybrid Quantum-Fuzzy Systems

arXiv:2606.08658v1 Announce Type: new Abstract: LLMs have revolutionized knowledge representation and retrieval, but lack the explicit modeling that knowledge ontologies possess. This paper surveys th

Facial Expression Recognition in the Deep Learning Era: A Systematic Multi-Criteria Review of Methods, Models, Datasets, Performance, Challenges, and Future Research Directions

arXiv:2606.08612v1 Announce Type: new Abstract: Facial Expression Recognition (FER) has advanced rapidly over the last decade, driven by the shift from handcrafted descriptors and shallow classifiers

Failure-Aware Refinement of Vision-Language Model for Lithography Defect Detection

arXiv:2606.08908v1 Announce Type: cross Abstract: Semiconductor lithography inspection requires reliable detection of small pattern defects such as bridge, burr, pinch, and contamination. In this stud

Failure by Interference: Language Models Make Balanced Parentheses Errors When Faulty Mechanisms Overshadow Sound Ones

arXiv:2507.00322v2 Announce Type: replace-cross Abstract: Despite remarkable advances in coding capabilities, language models (LMs) still struggle with simple syntactic tasks such as generating balanc

Feasibility to detect rapid change and disappearance of seagrass: Lessons from nearly 80 years of vegetation change in the Ako, Seto Inland Sea, Japan

arXiv:2606.07949v1 Announce Type: cross Abstract: This study analyses the Ako tidal flat in the Seto Inland Sea, Japan, where nearly all Zostera marina disappeared within a single year in 2025. Using

Few-shot Class-variable Incremental Audio Classification via Prototype Adaptation and Pseudo Class-variable Training

arXiv:2606.08898v1 Announce Type: cross Abstract: In the task of few-shot class-incremental audio classification, the number of classes is assumed to always increase without considering the possibilit

Field Validation of a Multi-Resolution ConvLSTM Framework for Retaining Wall Deformation Prediction

arXiv:2606.05556v2 Announce Type: replace Abstract: This study presents a comprehensive field validation of a multi-resolution Convolutional Long Short-Term Memory (ConvLSTM) framework for predicting

FlashCP: Load-Balanced Communication-Efficient Context Parallelism for LLM Training

arXiv:2606.08476v1 Announce Type: cross Abstract: Context parallelism (CP) is essential for training large-scale, long-context language models, as it partitions sequences to reduce memory overhead. Ho

Forecasting Japanese elections: A nonlinear machine-learning approach

arXiv:2606.07572v1 Announce Type: cross Abstract: Despite Japan being one of the world's largest advanced democracies, the development of election forecasting models for its national elections remains

Formalizing Learning from Language Feedback with Provable Guarantees

arXiv:2506.10341v2 Announce Type: replace Abstract: Interactively learning from observation and language feedback is an increasingly studied area driven by the emergence of large language model (LLM)

Forward-Looking Stress Testing Under Macro Scenarios: Stable SVaR Estimation Using a Hybrid GPR-HS Framework with SACS

arXiv:2606.07575v1 Announce Type: cross Abstract: Regulatory stress testing frameworks, including the Comprehensive Capital Analysis and Review (CCAR) and the Internal Capital Adequacy Assessment Proc

Foundation Inference Models for Ordinary Differential Equations

arXiv:2602.08733v2 Announce Type: replace Abstract: Ordinary differential equations (ODEs) are central to scientific modelling, but inferring their vector fields from noisy trajectories remains challe

Fourier Neural Operators with rank-1 lattice points and hyperbolic cross

arXiv:2606.08871v1 Announce Type: cross Abstract: The Fourier neural operator (FNO) is a neural network architecture that learns mappings between function spaces. Its efficient implementation is based

Frequency-based Constrained Sampling for Interval Patterns

arXiv:2606.09666v1 Announce Type: new Abstract: Output space pattern sampling is a powerful alternative to exhaustive pattern mining for exploring large pattern spaces, as it enables users to focus on

Frequency Decoupled Framework for Screen Content Image Super-Resolution

arXiv:2606.09029v1 Announce Type: new Abstract: Methods based on implicit neural representations have demonstrated superior performance in Screen Content Image Super-Resolution (SCISR) . However, they

Frequency-Scale Saliency for Spectral Descriptor Analysis in 3D Shape Retrieval

arXiv:2606.07791v1 Announce Type: cross Abstract: Classical spectral descriptors such as the Heat Kernel Signature and Wave Kernel Signature are widely used for non-rigid 3D shape retrieval, yet their

From USD Scenes to Knowledge Graphs: Zero-Shot Ontology Grounding with LLMs

arXiv:2606.09134v1 Announce Type: cross Abstract: Constructing knowledge graphs from 3D simulation scenes is essential for robot task reasoning, but the key bottleneck, grounding scene objects to form

From Validator Selection to Portfolio Collection Optimization in Proof-of-Stake Blockchains

arXiv:2606.08282v1 Announce Type: new Abstract: We consider a problem arising in proof-of-stake blockchain environments, where agents called nominators select validators - entities responsible for mai

Full-Batch Gradient Descent Outperforms One-Pass SGD: Sample Complexity Separation in Single-Index Learning

arXiv:2602.02431v2 Announce Type: replace-cross Abstract: It is folklore that reusing training data more than once can improve the statistical efficiency of gradient-based learning. While this phenome

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking

arXiv:2603.27493v2 Announce Type: replace Abstract: Spiking Neural Networks (SNNs), characterized by their event-driven computation and low power consumption, have shown great potential for energy-eff

Function-Vector Heads Are Two Populations: Writers and Cancellers in In-Context Learning

arXiv:2606.07560v1 Announce Type: cross Abstract: Function-vector (FV) heads (Todd et al., 2024) are typically identified by the magnitude of their causal contribution to in-context rule tasks, under

Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation

arXiv:2606.08866v1 Announce Type: new Abstract: CNN-based semantic segmentation networks usually rely on context heads such as ASPP, PPM, or attention modules to enlarge the receptive field. These hea

Generation Properties of Stochastic Interpolation under Finite Training Set

arXiv:2509.21925v2 Announce Type: replace-cross Abstract: This paper investigates the theoretical behavior of generative models under finite training populations. Within the stochastic interpolation g

Generative Frontier Planning for Adaptive Peer-Referral Recruitment under Covariate-Dependent Arrivals

arXiv:2606.08360v1 Announce Type: cross Abstract: Peer-referral recruitment systems such as respondent-driven sampling are critical for studying and intervening on hidden populations affected by infec

GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks

arXiv:2606.08303v1 Announce Type: new Abstract: This paper investigates a novel concept of time series geolocalization, where the goal is to infer the geographic origin of each raw time series. Succes

Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via Deep Learning Segmentation

arXiv:2509.11485v3 Announce Type: replace-cross Abstract: Labyrinthine stripe patterns are common in many physical systems, yet their lack of long-range order makes quantitative characterization chall

Geometry-Aware Uncertainty Quantification via Conformal Prediction on Manifolds

arXiv:2602.16015v2 Announce Type: replace Abstract: Conformal prediction gives finite-sample coverage guarantees for regression, but most standard constructions are designed for Euclidean output space

Geometry-Driven Flow Analysis of Brain Sulcal Pattern

arXiv:2606.08404v1 Announce Type: new Abstract: Cortical folding reflects coordinated neurodevelopmental processes and is increasingly recognized as a sensitive marker of neurological disease. However

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series

arXiv:2606.07725v1 Announce Type: cross Abstract: Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic