All entries

EVOTS: Evolutionary Transformer Search for Time Series Forecasting

arXiv:2607.00154v1 Announce Type: cross Abstract: Evolutionary neural architecture design for multivariate time-series forecasting remains underexplored, with most approaches relying on fixed Transfor

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Explainability in mulimodal deep transformation models for stroke outcome prediction

DGX agent

arXiv:2504.06299v2 Announce Type: replace-cross Abstract: Multimodal prediction models based on imaging and clinical data are increasingly used for clinical decision support, yet their interpretabilit

researcharxiv-cs-cv

2 Jul 2026

Explainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature Attributions

arXiv:2607.00931v1 Announce Type: new Abstract: Predicting cancer drug response from transcriptomic profiles is a cornerstone of precision oncology, yet the scientific value of machine learning models

Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows

arXiv:2607.00828v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows. Although recent advances hav

ext{Log}_ext{b}Quant: Quantizing Language Models in Logarithmic Space

model-releasesarxiv-cs-cl

arXiv:2607.01127v1 Announce Type: new Abstract: Quantization has become an invaluable tool to reduce memory requirements and inference speed of modern language models, in particular to make them avail

Faithful by Definition: Emotion Analysis via Natural Semantic Metalanguage Explications

arXiv:2607.00661v1 Announce Type: cross Abstract: Explanations for emotion classifiers are usually produced post hoc, with no guarantee that they reflect the computation behind the label. We present a

FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement

arXiv:2607.01111v1 Announce Type: cross Abstract: Robot policies inevitably encounter failures when deployed in real environments. Naive retries often repeat the same mistakes, while many existing rec

Fast Score-Based Sampling via Log-Concave Reductions

arXiv:2512.24152v2 Announce Type: replace-cross Abstract: Sampling based on score diffusions has led to striking empirical results, and has attracted considerable attention from various research commu

FastBridge: Closing the Model-Based Realization Gap in Safety Filters on 3D Gaussian Splatting for Fast Quadrotor Flight

arXiv:2607.01200v1 Announce Type: new Abstract: Fast quadrotor flight requires safe obstacle avoidance under tight onboard compute limits. While 3D Gaussian Splatting (3DGS) provides a continuous, geo

safetyarxiv-cs-ro

FCL-COD: Weakly Supervised Camouflaged Object Detection with Frequency-aware and Contrastive Learning

arXiv:2603.22969v2 Announce Type: replace Abstract: Existing camouflage object detection (COD) methods typically rely on fully-supervised learning guided by mask annotations. However, obtaining mask a

FedIA: Importance-Aware Aggregation for Domain-Robust Federated Graph Learning

arXiv:2509.18171v4 Announce Type: replace Abstract: Federated graph learning (FGL) is a natural paradigm for social-media user graphs, where language communities, regional markets, and service boundar

Finite-Time Queue Peak Laws in Stochastic Networks: Logarithmic Scaling After Geometric Thresholds

arXiv:2606.18218v2 Announce Type: replace-cross Abstract: We study finite-horizon queue peaks in generalized switches, a standard stochastic-network model in which many queues share constrained servic

FLAT: Revealing Hidden Latent-Conditioned Backdoor Failures in Federated Learning

arXiv:2508.04064v2 Announce Type: replace-cross Abstract: Horizontal federated learning (HFL) backdoor audits often summarize model behavior through clean accuracy (CA), mean attack success rate (ASR)

Flow-Map GRPO: Reinforcement Learning for Few-Step Flow-Map Generators via Anchored Stochastic Composition

arXiv:2607.00535v1 Announce Type: cross Abstract: Few-step flow-map generators, such as consistency models and MeanFlow, accelerate sampling by directly learning long-range transport maps between nois

Flow-Through Tensors: A Unified Computational Graph Architecture for Multi-Layer Transportation Network Optimization

arXiv:2507.02961v2 Announce Type: replace-cross Abstract: Modern transportation network modeling increasingly involves the integration of diverse methodologies including sensor-based forecasting, rein

FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification

arXiv:2511.10841v3 Announce Type: replace-cross Abstract: Modeling continuous-time dynamics from sparse and irregularly-sampled time series remains a fundamental challenge. Neural controlled different

FLYNN: Robust Neural Network for Robot Navigation using Fly Brain Topology

arXiv:2607.00025v1 Announce Type: cross Abstract: While deep learning models achieve state-of-the-art performance in complex tasks, they remain brittle when faced with new environments or sensory depr

ForAug: Mitigating Biases in Image Classification via Controlled Image Compositions

arXiv:2503.09399v4 Announce Type: replace-cross Abstract: Large-scale image classification datasets exhibit strong compositional biases: objects tend to be centered, appear at characteristic scales, a

Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence

arXiv:2607.00763v1 Announce Type: cross Abstract: Digital forensic investigations of network intrusions require analytical outputs that are traceable, reproducible, and court-defensible - requirements

Foundation Model-driven Key Anatomy Frame Selection for Blind-sweep Ultrasound Fetal Birth Weight Estimation

arXiv:2607.00745v1 Announce Type: new Abstract: Accurate fetal birth weight (FBW) estimation shortly before delivery is clinically valuable yet challenging due to its reliance on operator expertise, p

Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices

model-releasesarxiv-cs-cv

arXiv:2607.01001v1 Announce Type: new Abstract: Radiomics is the established approach for CT-based lung cancer phenotyping, yet comparisons with foundation models rarely isolate contributions of featu

FRAME: Learning the Adaptation Domain with a Mixture of Fractional-Fourier Experts

arXiv:2607.00162v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) reparameterizes weight updates in a fixed basis: low-rank adapters operate in the spatial domain, while a recent

FrameONE: Hierarchical Motion Modeling for Universal Multi-View Echocardiographic Keyframe Detection

arXiv:2607.00748v1 Announce Type: new Abstract: Accurate detection of end-systole (ES) and end-diastole (ED) frames is fundamental to echocardiographic assessment. Existing methods are typically devel

safetyarxiv-cs-cv

Applications

Fraud is Not Just Rarity: A Causal Prototype Attention Approach to Realistic Synthetic Oversampling

arXiv:2507.14706v2 Announce Type: replace-cross Abstract: Detecting fraudulent credit card transactions remains a significant challenge, due to the extreme class imbalance in real-world data and the o

applicationsarxiv-cs-ai

From Holistic Evaluation to Structured Criteria: Rubrics Across the Evolving LLM Landscape

arXiv:2606.08625v2 Announce Type: replace Abstract: As Large Language Models (LLMs) advance toward open-ended autonomous agents, the mechanisms used to evaluate and guide their behavior must evolve ac

safetyarxiv-cs-cl

Agents

From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives

arXiv:2607.00918v1 Announce Type: cross Abstract: Although large language models (LLMs) have demonstrated impressive creative fiction generation, they struggle to maintain narrative consistency and co

agentsarxiv-cs-ai

From Pixels to Temporal Correlations: Learning Informative Representations for Reinforcement Learning Pre-training

arXiv:2607.00811v1 Announce Type: new Abstract: Unsupervised pre-training on large-scale datasets has demonstrated significant potential for improving the sample efficiency and performance of Reinforc

safetyarxiv-cs-lg

From Prediction Uncertainty to Conformalized Distance Fields for Safe Motion Planning

arXiv:2607.00776v1 Announce Type: new Abstract: Safe motion planning in dynamic environments requires reasoning about the uncertainty in predicted obstacle motion without sacrificing real-time perform

safetyarxiv-cs-ro

From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning

arXiv:2603.10263v2 Announce Type: replace-cross Abstract: We introduce Distribution Contractive Reinforcement Learning (DICE-RL), a framework that uses reinforcement learning (RL) as a 'distribution c

safetyarxiv-cs-lg

From Real-Time Planning to Reliable Execution:Scalable Coordination for Heterogeneous Multi-Robot Fleets in Industrial Environments

arXiv:2607.00591v1 Announce Type: new Abstract: With the increasing deployment of heterogeneous robot fleets in industrial environments, efficient coordination remains a critical challenge. Real-time

researcharxiv-cs-ro

From Signals to Structure: How Memory Architecture Drives Language Emergence in LLM Agents

arXiv:2607.00233v1 Announce Type: new Abstract: How do two agents invent a shared language from scratch? In a Lewis signaling game, a sender and receiver must coordinate on a code using only their int

From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems

arXiv:2410.22526v2 Announce Type: replace Abstract: To effectively address potential harms from Artificial Intelligence (AI) systems, it is essential to identify and mitigate system-level hazards. Cur

Tutorials

From Spectral Methods to Sample Complexity Bounds for Fourier Neural Operators

arXiv:2607.00320v1 Announce Type: cross Abstract: We establish approximation and learning guarantees for Fourier neural operators (FNOs) applied to time-T solution operators of dissipative evolution e

tutorialsarxiv-cs-lg

From 'Strings' to 'Things' for Personal Knowledge Graphs: Evaluating LLM Triple Extraction for Recommendation Systems

arXiv:2607.00003v1 Announce Type: cross Abstract: Personal Knowledge Graphs (PKGs) offer a privacy-preserving framework for modeling user preferences, yet constructing them from unstructured, decentra

From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Research

arXiv:2607.00512v1 Announce Type: new Abstract: Structural equation modelling (SEM) is widely used in survey-based business and information systems research to assess latent constructs and theory-driv

From Technical Metrics to User Perception: A User Study of a Multimodal Human-Robot Interaction System for Object Detection and Grasping

arXiv:2607.00530v1 Announce Type: cross Abstract: Improvements in the technical performance of human--robot interaction (HRI) systems do not automatically translate into differences that human users c

From World Models to World Action Models: A Concise Tutorial for Robotics

arXiv:2607.00836v1 Announce Type: cross Abstract: World models are increasingly used in embodied intelligence and generative simulation, yet their scope remains ambiguous across communities. This tuto

FSA: An Alternative Efficient Implementation of Native Sparse Attention Kernel

arXiv:2508.18224v3 Announce Type: replace-cross Abstract: Recent advances in sparse attention mechanisms have demonstrated strong potential for reducing the computational cost of long-context training

FUMO: Prior-Modulated Diffusion for Single Image Reflection Removal

model-releasesarxiv-cs-cv

arXiv:2603.19036v2 Announce Type: replace Abstract: Single image reflection removal (SIRR) is challenging in real scenes, where reflection strength varies spatially and reflection patterns are tightly

Applications

Function-Counting Theory for Low-Dimensional Data Structures

arXiv:2607.01010v1 Announce Type: cross Abstract: The success of deep learning models in classification and regression is widely attributed to the low-dimensional structure that real-world data tend t

applicationsarxiv-cs-lg

Applications

FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model

arXiv:2607.01212v1 Announce Type: cross Abstract: Current work on robot furniture assembly mostly focuses on toy-scale settings or single-arm manipulation. We introduce FurnitureVLA, the first systema

applicationsarxiv-cs-ai

FusionFactory: Fusing LLM Capabilities with Multi-LLM Log Data

arXiv:2507.10540v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has created a diverse landscape of models, each excelling at different tasks. This diversity d

GADA: Geometry-Aware Deformable Aggregation for Image-Based Gaussian Splatting

arXiv:2607.00595v1 Announce Type: new Abstract: Gaussian Splatting has achieved significant improvements by incorporating warping-based techniques. However, such methods suffer from pixel-level inaccu

GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems

arXiv:2607.01128v1 Announce Type: new Abstract: Operator learning for partial differential equations (PDEs) on arbitrary geometries builds fast neural surrogates for large-scale simulation. Although r

GameDevBench: Evaluating Agentic Capabilities Through Game Development

arXiv:2602.11103v2 Announce Type: replace Abstract: Despite rapid progress on coding agents, progress on their multimodal counterparts has lagged behind. A key challenge is the scarcity of evaluation

Gauging, Measuring, and Controlling Critic Complexity in Actor-Critic Reinforcement Learning

arXiv:2607.00452v1 Announce Type: cross Abstract: Actor-critic methods depend on learned critics, but critic quality is often evaluated only indirectly through return, temporal-difference error, or va

GaussianEmoTalker: Real-Time Emotional Talking Head Synthesis with Audio-Driven and Blendshape-Based 3D Gaussian Splatting

arXiv:2607.00959v1 Announce Type: new Abstract: Audio-driven talking head synthesis has achieved impressive progress in lip synchronization and visual quality, yet generating expressive emotional avat

GaussianFusion: Unified 3D Gaussian Representation for Multi-Modal Fusion Perception

arXiv:2607.00746v1 Announce Type: cross Abstract: The bird's-eye view (BEV) representation enables multi-sensor features to be fused within a unified space, serving as the primary approach for achievi