Search: “framework”

arXiv:2601.12964v2 Announce Type: replace Abstract: Self-supervised pretraining in remote sensing is mostly done using mid-spatial resolution (MR) image datasets due to their high availability. Given

tutorialsarxiv-cs-cv

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Safety

CycleRL: Sim-to-Real Deep Reinforcement Learning for Robust Autonomous Bicycle Control

DGX agent

arXiv:2603.15013v2 Announce Type: replace Abstract: Autonomous bicycles offer a promising agile solution for urban mobility and last-mile logistics. However, conventional control strategies often stru

safetyarxiv-cs-ro

5 May 2026

Delayed homomorphic reinforcement learning for environments with delayed feedback

arXiv:2604.03641v2 Announce Type: replace Abstract: Reinforcement learning in real-world systems often involves delayed feedback, which breaks the Markov assumption and impedes both learning and contr

Differentiable Multiphysics Co-Optimization via Implicit Neural Representations: A Transient Hamburger-Cooking Benchmark

model-releasesarxiv-cs-lg

arXiv:2605.01040v1 Announce Type: cross Abstract: The co-optimization of geometry and physical parameters remains challenging in transient multiphysics systems involving moving boundaries, nonlinear m

Does it Really Count? Assessing Semantic Grounding in Text-Guided Class-Agnostic Counting

arXiv:2605.02752v1 Announce Type: new Abstract: Open-world text-guided class-agnostic counting (CAC) has emerged as a flexible paradigm for counting arbitrary object classes by using natural language

applicationsarxiv-cs-cv

Energy-Based Constraint Networks: Learning Structural Coherence Across Modalities

arXiv:2605.00960v1 Announce Type: cross Abstract: We introduce energy-based constraint networks -- a modality-agnostic architecture that learns structural coherence from contrastive pairs. The system

local-aiarxiv-cs-cl

Enhancing Multimodal In-Context Learning via Inductive-Deductive Reasoning

arXiv:2605.02378v1 Announce Type: new Abstract: In-context learning (ICL) allows large models to adapt to tasks using a few examples, yet its extension to vision-language models (VLMs) remains fragile

Exploring Entropy-based Active Learning for Fair Brain Segmentation

arXiv:2605.01706v1 Announce Type: new Abstract: Active learning (AL) has emerged as a crucial strategy for reducing the prohibitive costs associated with medical image segmentation. However, standard

safetyarxiv-cs-cv

Feedback Motion Planning for Stochastic Nonlinear Systems with Signal Temporal Logic Specifications

arXiv:2605.02361v1 Announce Type: new Abstract: We study feedback motion planning for continuous-time stochastic nonlinear systems under signal temporal logic (STL) specifications. We propose a framew

applicationsarxiv-cs-ro

Flexi-LoRA with Input-Adaptive Ranks: Efficient Finetuning for Speech and Reasoning Tasks

model-releasesarxiv-cs-cl

arXiv:2605.01959v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning methods like Low-Rank Adaptation (LoRA) have become essential for deploying large language models, yet their static pa

Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding

arXiv:2605.00857v1 Announce Type: cross Abstract: Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled tar

From Fold to Function: Simulation-Driven Design of Origami Mechanisms

arXiv:2511.10580v3 Announce Type: replace Abstract: Origami-inspired mechanisms can transform flat sheets into functional three-dimensional dynamic structures that are lightweight, compact, and capabl

applicationsarxiv-cs-ro

Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs

arXiv:2601.22709v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) achieve strong multimodal performance but are costly to deploy, and post-training quantization often causes significan

GAZE: Grounded Agentic Zero-shot Evaluation with Viewer-Level Tools and Literature Retrieval on Rare Brain MRI

arXiv:2605.00876v1 Announce Type: cross Abstract: Vision-language models (VLMs) read an image and produce text in a single forward pass, whereas radiologists typically inspect an image several times a

Generative Interfaces for Language Models

arXiv:2508.19227v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly seen as assistants, copilots, and consultants, capable of supporting a wide range of tasks through nat

researcharxiv-cs-cl

Graph Query Generation with Constraint-guided Large Language Agents

arXiv:2605.00845v1 Announce Type: cross Abstract: Knowledge Graph Question Answering (KGQA) has advanced through structured query generation, yet most efforts target RDF/SPARQL, leaving Cypher and pro

applicationsarxiv-cs-cl

Grounding Synthetic Data Generation With Vision and Language Models

arXiv:2603.09625v2 Announce Type: replace Abstract: Deep learning models benefit from increasing data diversity and volume, motivating synthetic data augmentation to improve existing datasets. However

Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design

arXiv:2605.00931v1 Announce Type: new Abstract: Federated learning (FL) is fundamentally a distributed optimization problem executed by communicating agents with local data, local computation, and par

local-aiarxiv-cs-lg

Injecting Distributional Awareness into MLLMs via Reinforcement Learning for Deep Imbalanced Regression

arXiv:2605.01402v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) struggle with numerical regression under long-tailed target distributions. Token-level supervised fine-tuning (

safetyarxiv-cs-cl

InstructMoLE: Instruction-Guided Mixture of Low-rank Experts for Multi-Conditional Image Generation

arXiv:2512.21788v3 Announce Type: replace Abstract: Parameter-Efficient Fine-Tuning of Diffusion Transformers (DiTs) for diverse, multi-conditional tasks often suffers from task interference when usin

Kernel Treatment Effects with Adaptively Collected Data

arXiv:2510.10245v2 Announce Type: replace-cross Abstract: Adaptive experiments improve efficiency by adjusting treatment assignments based on past outcomes, but this adaptivity breaks the i.i.d. assum

Lateral String Stability for Vehicle Platoons: Formulation, Definition, and Analysis

arXiv:2605.01731v1 Announce Type: new Abstract: Platooning of connected and automated vehicles provides significant benefits in terms of energy efficiency, traffic throughput, and, most critically, sa

safetyarxiv-cs-ro

Led to Mislead: Adversarial Content Injection for Attacks on Neural Ranking Models

arXiv:2605.01591v1 Announce Type: cross Abstract: Neural Ranking Models (NRMs) are central to modern information retrieval but remain highly vulnerable to adversarial manipulation. Existing attacks of

applicationsarxiv-cs-cl

Leveraging Imperfect Medical Data: A Manifold-Consistent Spatio-Temporal Network for Sensor-based Human Activity Recognition

arXiv:2605.00913v1 Announce Type: new Abstract: Sensor-based Human Activity Recognition (HAR) has attracted increasing attention in medical and healthcare monitoring, particularly with the growth of I

Ligandformer: A Graph Neural Network for Predicting Compound Property with Robust Interpretation

arXiv:2202.10873v4 Announce Type: replace-cross Abstract: Robust and efficient interpretation of QSAR methods is quite useful to validate AI prediction rationales with subjective opinion (chemist or b

LinMU: Multimodal Understanding Made Linear

arXiv:2601.01322v2 Announce Type: replace Abstract: Modern Vision-Language Models (VLMs) achieve impressive performance but are limited by the quadratic complexity of self-attention, which prevents th

Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers

arXiv:2509.15113v2 Announce Type: replace Abstract: The growing demand for energy-efficient, high-performance AI systems has led to increased attention on alternative computing platforms (e.g., photon

local-aiarxiv-cs-lg

Machine Learning as Iterated Belief Change a la Darwiche and Pearl

arXiv:2506.13157v3 Announce Type: replace-cross Abstract: Artificial Neural Networks (ANNs) are powerful machine-learning models capable of capturing intricate non-linear relationships. They are widel

applicationsarxiv-cs-lg

Agents

MedScribe: Clinically Grounded CT Reporting through Agentic Workflows

arXiv:2605.01779v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown potential for automated radiology report generation, yet existing approaches rely on global embedding compressi

agentsarxiv-cs-cv

Mesh Based Simulations with Spatial and Temporal awareness

arXiv:2605.01542v1 Announce Type: new Abstract: Machine Learning surrogates for Computational Fluid Dynamics (CFD), particularly Graph Neural Networks (GNNs) and Transformers, have become a new import

Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading

arXiv:2605.01954v1 Announce Type: cross Abstract: Many sequential decision-making problems exhibit hierarchical structure, where high-level semantic choices constrain downstream actions and feedback i

applicationsarxiv-cs-cl

Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare

arXiv:2605.01961v1 Announce Type: new Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents. However

Tutorials

Natural Gradient Bayesian Filtering: Geometry-Aware Filter for Dynamical Systems

arXiv:2605.02306v1 Announce Type: new Abstract: Bayesian filtering is a cornerstone of state estimation in complex systems such as aerospace systems, yet exact solutions are available only for linear

tutorialsarxiv-cs-ro

NaviMaster: Learning a Unified Policy for GUI and Embodied Navigation Tasks

arXiv:2508.02046v4 Announce Type: replace-cross Abstract: Recent advances in Graphical User Interface (GUI) and embodied navigation have driven progress, yet these domains have largely evolved in isol

Tutorials

Online Generalised Predictive Coding

arXiv:2605.02675v1 Announce Type: cross Abstract: This paper introduces an extension of generalised filtering for online applications. Generalised filtering refers to data assimilation schemes that jo

tutorialsarxiv-cs-lg

Provable Benefit of Curriculum in Transformer Tree-Reasoning Post-Training

arXiv:2511.07372v3 Announce Type: replace Abstract: Recent curriculum techniques in the post-training stage of LLMs have been empirically observed to outperform non-curriculum approaches in improving

Robotic Desk Organization: A Multi-Primitive Approach to Manipulating Heterogeneous Objects via Environmental Constraints

arXiv:2605.02135v1 Announce Type: new Abstract: Desktop organization remains challenging for service robots because of heterogeneous objects and diverse manipulation objectives, such as collection and

applicationsarxiv-cs-ro

Robust Cross-Domain WiFi Fall Detection via Physics-Driven Attention-Enhanced Transformers

arXiv:2605.00869v1 Announce Type: cross Abstract: Device-free fall detection utilizing WiFi Channel State Information (CSI) has emerged as a promising, privacy-preserving solution for elderly health m

local-aiarxiv-cs-cv

Scaling Sequence-to-Sequence Generative Neural Rendering

arXiv:2510.04236v3 Announce Type: replace Abstract: We present Kaleido, a family of generative models designed for photorealistic, unified object- and scene-level neural rendering. Kaleido operates on

Selector-Guided Autonomous Curriculum for One-Shot Reinforcement Learning from Verifiable Rewards

model-releasesarxiv-cs-lg

arXiv:2605.01823v1 Announce Type: new Abstract: Recently, Reinforcement Learning from Verifiable Rewards (RLVR) has been established as a highly effective technique for augmenting the math reasoning s

Self-Supervised Spatial And Zero-Shot Angular Super-Resolution by Spatial-Angular Implicit Representation For Rotating-View SNR-Efficient Diffusion MRI

arXiv:2605.02575v1 Announce Type: new Abstract: Rotating-view thick-slice acquisition is highly SNR-efficient for mesoscale diffusion MRI (dMRI) but requires numerous rotating views to satisfy Nyquist

Semantic Context-aware mOdality fUsion Transformer (SCOUT): A Context-Aware Multimodal Transformer for Concept-Grounded Pathology Report Generation

arXiv:2605.01144v1 Announce Type: new Abstract: Whole-slide images (WSIs) present a fundamental challenge for computational pathology due to their extreme resolution, multi-scale heterogeneity, and th

local-aiarxiv-cs-cv

Sentra-Guard: A Real-Time Multilingual Defense Against Adversarial LLM Prompts

arXiv:2510.22628v2 Announce Type: replace-cross Abstract: This paper presents a real-time modular defense system named Sentra-Guard. The system detects and mitigates jailbreak and prompt injection att

safetyarxiv-cs-ai

Agents

Sound Source Localization for Spatial Mapping of Surgical Actions in Dynamic Scenes

arXiv:2510.24332v3 Announce Type: replace-cross Abstract: Purpose: Surgical scene understanding is key to advancing computer-aided and intelligent surgical systems. Current approaches predominantly re

agentsarxiv-cs-cv

TCDA: Thread-Constrained Discourse-Aware Modeling for Conversational Sentiment Quadruple Analysis

model-releasesarxiv-cs-cl

arXiv:2605.01717v1 Announce Type: new Abstract: Conversational Aspect-based Sentiment Quadruple Analysis (DiaASQ) needs to capture the complex interrelationships in multiple rounds of dialogues. Exist

Training-Free Time Series Classification via In-Context Reasoning with LLM Agents

model-releasesarxiv-cs-ai

arXiv:2510.05950v2 Announce Type: replace Abstract: Time series classification (TSC) spans diverse application scenarios, yet labeled data are often scarce, making task-specific training costly and in

Unifying Deep Stochastic Processes for Image Enhancement

arXiv:2605.01568v1 Announce Type: new Abstract: Deep stochastic processes have recently become a central paradigm for image enhancement, with many methods explicitly conditioning the stochastic trajec

Unsupervised Machine Learning for Detecting Structural Anomalies in European Regional Statistics

arXiv:2605.02884v1 Announce Type: new Abstract: Ensuring the coherence of regional socio-economic statistics is a central task for national statistical institutes. Traditional validation tools, such a