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Evaluating the relationship between regularity and learnability in recursive numeral systems using Reinforcement Learning

arXiv:2602.21720v2 Announce Type: replace-cross Abstract: Human recursive numeral systems (i.e., counting systems such as English base-10 numerals), like many other grammatical systems, are highly reg

tutorialsarxiv-cs-ai

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Evaluation Revisited: A Taxonomy of Evaluation Concerns in Natural Language Processing

DGX agent

arXiv:2604.25923v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have prompted a growing body of work that questions the methodology of prevailing evaluation practices.

researcharxiv-cs-cl

30 Apr 2026

Event-based Liveness Detection using Temporal Ocular Dynamics: An Exploratory Approach

arXiv:2604.26285v1 Announce Type: new Abstract: Face liveness detection has been extensively studied using RGB cameras, achieving strong performance under controlled conditions but often failing to ge

Evergreen: Efficient Claim Verification for Semantic Aggregates

model-releasesarxiv-cs-ai

arXiv:2604.26180v1 Announce Type: cross Abstract: With recent semantic query processing engines, semantic aggregation has become a primitive operator, enabling the reduction of a relation into a natur

EvoDev: An Iterative Feature-Driven Framework for End-to-End Software Development with LLM-based Agents

model-releasesarxiv-cs-ai

arXiv:2511.02399v2 Announce Type: replace-cross Abstract: Recent advances in large language model agents offer the promise of automating end-to-end software development from natural language requireme

Agents

EvolvingAgent: Curriculum Self-evolving Agent with Continual World Model for Long-Horizon Tasks

arXiv:2502.05907v3 Announce Type: replace Abstract: Completing Long-Horizon (LH) tasks in open-ended worlds is an important yet difficult problem for embodied agents. Existing approaches suffer from t

agentsarxiv-cs-ro

EvoSelect: Data-Efficient LLM Evolution for Targeted Task Adaptation

arXiv:2604.26170v1 Announce Type: new Abstract: Adapting large language models (LLMs) to a targeted task efficiently and effectively remains a fundamental challenge. Such adaptation often requires ite

safetyarxiv-cs-cl

Explainable Representation of Finite-Memory Policies for POMDPs using Decision Trees

arXiv:2411.13365v2 Announce Type: replace Abstract: Partially Observable Markov Decision Processes (POMDPs) are a fundamental framework for decision-making under uncertainty and partial observability.

tutorialsarxiv-cs-ai

Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework

arXiv:2604.26762v1 Announce Type: cross Abstract: The Probabilistic Transformer (PT) establishes that the Transformer's self-attention plus its feed-forward block is mathematically equivalent to Mean-

ext{PKS}^4:Parallel Kinematic Selective State Space Scanners for Efficient Video Understanding

model-releasesarxiv-cs-cv

arXiv:2604.26461v1 Announce Type: new Abstract: Temporal modeling remains a fundamental challenge in video understanding, particularly as sequence lengths scale. Traditional video models relying on de

FaaSMoE: A Serverless Framework for Multi-Tenant Mixture-of-Experts Serving

arXiv:2604.26881v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models offer high capacity with efficient inference cost by activating a small subset of expert models per input. However, de

researcharxiv-cs-lg

Factorizable joint shift revisited

arXiv:2601.15036v3 Announce Type: replace Abstract: Factorizable joint shift (FJS) represents a type of distribution shift (or dataset shift) that comprises both covariate and label shift. Recently, i

researcharxiv-cs-lg

Failure Modes of Maximum Entropy RLHF

arXiv:2509.20265v3 Announce Type: replace-cross Abstract: In this paper, we show that Simple Preference Optimization (SimPO) can be derived as Maximum Entropy Reinforcement Learning, providing a theor

researcharxiv-cs-cl

Local Ai

FalconApp: Rapid iPhone Deployment of End-to-End Perception via Automatically Labeled Synthetic Data

arXiv:2604.25949v1 Announce Type: new Abstract: Reliable perception for robotics depends on large-scale labeled data, yet real-world datasets rely on heavy manual annotation and are time-consuming to

local-aiarxiv-cs-ro

FASH-iCNN: Making Editorial Fashion Identity Inspectable Through Multimodal CNN Probing

arXiv:2604.26186v1 Announce Type: new Abstract: Fashion AI systems routinely encode the aesthetic logic of specific houses, editors, and historical moments without disclosing it. We present FASH-iCNN,

FASTER: Rethinking Real-Time Flow VLAs

arXiv:2603.19199v2 Announce Type: replace-cross Abstract: Real-time execution is crucial for deploying Vision-Language-Action (VLA) models in the physical world. Existing asynchronous inference method

applicationsarxiv-cs-cv

Featurising Pixels from Dynamic 3D Scenes with Linear In-Context Learners

arXiv:2604.26488v1 Announce Type: new Abstract: One of the most exciting applications of vision models involve pixel-level reasoning. Despite the abundance of vision foundation models, we still lack r

Federated Medical Image Classification under Class and Domain Imbalance exploiting Synthetic Sample Generation

arXiv:2604.26324v1 Announce Type: new Abstract: Exploiting deep learning in medical imaging faces critical challenges, including strict privacy constraints, heterogeneous imaging devices with varying

FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utility

arXiv:2510.26841v2 Announce Type: replace-cross Abstract: Federated Learning (FL) enables collaborative model training without data sharing, yet participants face a fundamental challenge, e.g., simult

safetyarxiv-cs-ai

Fitting Large Nonlinear Mixed Effects Models Using Variational Expectation Maximization

arXiv:2604.26160v1 Announce Type: cross Abstract: Nonlinear Mixed Effects models (NLME) models are widely used in pharmacometrics and related fields to analyze hierarchical and longitudinal data. Howe

researcharxiv-cs-lg

FLARE: Fully Integration of Vision-Language Representations for Deep Cross-Modal Understanding

arXiv:2504.09925v3 Announce Type: replace Abstract: We introduce FLARE, a family of vision language models (VLMs) with a fully vision-language alignment and integration paradigm. Unlike existing appro

safetyarxiv-cs-cv

Flashback: A Reversible Bilateral Run-Peeling Decomposition of Strings

arXiv:2604.26190v1 Announce Type: cross Abstract: We introduce Flashback, a reversible string decomposition that repeatedly peels the maximal leading and trailing character runs from a sentinel-wrappe

researcharxiv-cs-cl

FloatSOM: GPU-Accelerated, Distributed, Topology-Flexible Self-Organizing Maps

model-releasesarxiv-cs-lg

arXiv:2604.26555v1 Announce Type: cross Abstract: GPU-accelerated Self-Organizing Map (SOM) implementations are among the most competitive options for large-scale SOM analysis, but growing dataset siz

FlowBot: Inducing LLM Workflows with Bilevel Optimization and Textual Gradients

arXiv:2604.26258v1 Announce Type: new Abstract: LLM workflows, which coordinate structured calls to individual LLMs (each augmented with varying instructions and tools) to achieve a particular goal, o

applicationsarxiv-cs-cl

FlowS: One-Step Motion Prediction via Local Transport Conditioning

model-releasesarxiv-cs-ro

arXiv:2604.26065v1 Announce Type: new Abstract: Generative motion prediction must satisfy three simultaneous requirements for real-world autonomy: high accuracy, diverse multimodal futures, and strict

Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference

model-releasesarxiv-cs-cl

arXiv:2604.26294v1 Announce Type: new Abstract: We present tensor and sequence parallelism (TSP), a parallel execution strategy that folds tensor parallelism and sequence parallelism onto a single dev

Foundation Model-Driven Semantic Change Detection in Remote Sensing Imagery

arXiv:2602.13780v2 Announce Type: replace Abstract: Remote sensing (RS) change detection is essential for interpreting surface dynamics. Semantic change detection (SCD) further enables pixel-level und

From Black-Box Confidence to Measurable Trust in Clinical AI: A Framework for Evidence, Supervision, and Staged Autonomy

arXiv:2604.26671v1 Announce Type: cross Abstract: Trust in clinical artificial intelligence (AI) cannot be reduced to model accuracy, fluency of generation, or overall positive user impression. In med

From Intent to Evidence: A Categorical Approach for Structural Evaluation of Deep Research Agents

model-releasesarxiv-cs-lg

arXiv:2603.25342v2 Announce Type: replace Abstract: Deep Research Agents (DRAs) aim to answer complex questions by searching the web, checking evidence, and synthesizing conclusions across heterogeneo

From Prompt Risk to Response Risk: Paired Analysis of Safety Behavior of Large Language Model

arXiv:2604.26052v1 Announce Type: new Abstract: Safety evaluations of large language models (LLMs) typically report binary outcomes such as attack success rate, refusal rate, or harmful/not-harmful re

safetyarxiv-cs-cl

FruitProM-V2: Robust Probabilistic Maturity Estimation and Detection of Fruits and Vegetables

arXiv:2604.26084v1 Announce Type: cross Abstract: Accurate fruit maturity identification is essential for determining harvest timing, as incorrect assessment directly affects yield and post-harvest qu

Fundamental Physics, Existential Risks and Human Futures

arXiv:2604.26530v1 Announce Type: cross Abstract: Over the past 25 years, I have been involved in some intriguing developments in the foundations of physics, exploring the quantum reality problem, the

safetyarxiv-cs-ai

FunFace: Feature Utility and Norm Estimation for Face Recognition

arXiv:2604.26598v1 Announce Type: new Abstract: Face Recognition (FR) is used in a variety of application domains, from entertainment and banking to security and surveillance. Such applications rely o

FutureWorld: A Live Environment for Training Predictive Agents with Real-World Outcome Rewards

model-releasesarxiv-cs-ai

arXiv:2604.26733v1 Announce Type: new Abstract: Live future prediction refers to the task of making predictions about real-world events before they unfold. This task is increasingly studied using larg

GaitKD: A Universal Decoupled Distillation Framework for Efficient Gait Recognition

arXiv:2604.26255v1 Announce Type: new Abstract: Gait recognition is an attractive biometric modality for long-range and contact-free identification, but high-performing gait models often rely on deep

applicationsarxiv-cs-cv

GateMOT: Q-Gated Attention for Dense Object Tracking

arXiv:2604.26353v1 Announce Type: new Abstract: While large models demonstrate the strong representational power of vanilla attention, this core mechanism cannot be directly applied to Dense Object Tr

Generalized Disguise Makeup Presentation Attack Detection Using an Attention-Guided Patch-Based Framework

arXiv:2604.26025v1 Announce Type: new Abstract: Despite significant advances in facial recognition systems, they remain vulnerable to face presentation attacks. Among them, disguise makeup attacks are

tutorialsarxiv-cs-cv

Generative AI-Based Virtual Assistant using Retrieval-Augmented Generation: An evaluation study for bachelor projects

arXiv:2604.25924v1 Announce Type: cross Abstract: Large Language Models have been increasingly employed in the creation of Virtual Assistants due to their ability to generate human-like text and handl

Generative Bid Shading in Real-Time Bidding Advertising

arXiv:2508.06550v3 Announce Type: replace-cross Abstract: Bid shading plays a crucial role in Real-Time Bidding (RTB) by adaptively adjusting the bid to avoid advertisers overspending. Existing mainst

safetyarxiv-cs-lg

Generative models on phase space

arXiv:2604.02415v2 Announce Type: replace-cross Abstract: Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of learning and sampling from high-dime

tutorialsarxiv-cs-ai

Geometric Inverse Flight Dynamics on SO(3) and Application to Tethered Fixed-Wing Aircraft

arXiv:2602.17166v2 Announce Type: replace Abstract: We present a robotics-oriented, coordinate-free formulation of inverse flight dynamics for fixed-wing aircraft on SO(3). Translational force balance

researcharxiv-cs-ro

GIFGuard: Proactive Forensics against Deepfakes in Facial GIFs via Spatiotemporal Watermarking

model-releasesarxiv-cs-cv

arXiv:2604.26519v1 Announce Type: new Abstract: The rapid evolution of deepfake technology poses an unprecedented threat to the authenticity of Graphics Interchange Format (GIF) imagery, which serves

Glance-or-Gaze: Incentivizing LMMs to Adaptively Focus Search via Reinforcement Learning

arXiv:2601.13942v2 Announce Type: replace-cross Abstract: Large Multimodal Models (LMMs) have achieved remarkable success in visual understanding, yet they struggle with knowledge-intensive queries in

safetyarxiv-cs-ai

Agents

GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents

arXiv:2604.26752v1 Announce Type: new Abstract: We present GLM-5V-Turbo, a step toward native foundation models for multimodal agents. As foundation models are increasingly deployed in real environmen

agentsarxiv-cs-cv

GNC-Pose: Geometry-Aware GNC-PnP for Accurate 6D Pose Estimation

arXiv:2512.06565v2 Announce Type: replace Abstract: We present GNC-Pose, a fully learning-free monocular 6D object pose estimation pipeline for textured objects that combines rendering-based initializ

safetyarxiv-cs-cv

GoViG: Goal-Conditioned Visual Navigation Instruction Generation via Multimodal Reasoning

arXiv:2508.09547v2 Announce Type: replace-cross Abstract: We introduce Goal-Conditioned Visual Navigation Instruction Generation (GoViG), a new task that aims to generate contextually coherent navigat

applicationsarxiv-cs-ai

Graph-based Semantic Calibration Network for Unaligned UAV RGBT Image Semantic Segmentation and A Large-scale Benchmark

model-releasesarxiv-cs-cv

arXiv:2604.26893v1 Announce Type: new Abstract: Fine-grained RGBT image semantic segmentation is crucial for all-weather unmanned aerial vehicle (UAV) scene understanding. However, UAV RGBT semantic s

Graph Construction and Matching for Imperative Programs using Neural and Structural Methods

arXiv:2604.26578v1 Announce Type: cross Abstract: Reusing verification artefacts requires identifying structural and semantic similarities across programs and their specifications. In this paper, we f