Search: “papers”

ZeroSiam: An Efficient Asymmetry for Test-Time Entropy Optimization without Collapse

arXiv:2509.23183v3 Announce Type: replace Abstract: Test-time entropy minimization helps adapt a model to novel environments and incentivize its reasoning capability, unleashing the model's potential

safetyarxiv-cs-lg

19 May 2026

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

A Topology-Aware Spatiotemporal Handover Framework for Continuous Multi-UAV Tracking

DGX agent

arXiv:2605.15779v1 Announce Type: cross Abstract: The integration of Unmanned Aerial Vehicles(UAVs) into Intelligent Transportation Systems (ITS) offers synoptic visibility for traffic monitoring, yet

researcharxiv-cs-ai

18 May 2026

A Unified View of Score-Based and Drifting Models

arXiv:2603.07514v3 Announce Type: replace-cross Abstract: Drifting models train one-step generators by optimizing a kernel-induced mean-shift discrepancy between the data and model distributions, with

tutorialsarxiv-cs-ai

Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making

arXiv:2605.16054v1 Announce Type: cross Abstract: Recent work has framed decision-making as a sequence modeling problem using generative models such as diffusion models. Although promising, these appr

AGC: Adaptive Geodesic Correction for Adversarial Robustness on Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2605.15584v1 Announce Type: new Abstract: Vision-language models like CLIP have demonstrated remarkable zero-shot transfer capabilities. However, their susceptibility to imperceptible adversaria

Agents

Autonomous Intelligent Agents for Natural-Language-Driven Web Execution with Integrated Security Assurance

arXiv:2605.15281v1 Announce Type: cross Abstract: Modern web test suites rot. A UI refactor breaks locators, a timing change causes race conditions, and within weeks developers abandon the suite entir

agentsarxiv-cs-ai

Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty

arXiv:2507.16806v2 Announce Type: replace-cross Abstract: When language models (LMs) are trained via reinforcement learning (RL) to generate natural language 'reasoning chains', their performance impr

Beyond Performance Disparities: A Three-Level Audit of Representational Harm in CelebA

arXiv:2605.15312v1 Announce Type: cross Abstract: Large-scale facial datasets like CelebA are widely used in computer vision, yet the cultural biases embedded in their labels remain underexplored. Fai

safetyarxiv-cs-cv

Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling

arXiv:2507.01679v3 Announce Type: replace-cross Abstract: Existing LLMs-post-training techniques are broadly categorized into supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). Each par

Can We Trust AI-Inferred User States. A Psychometric Framework for Validating the Reliability of Users States Classification by LLMs in Operational Environments

model-releasesarxiv-cs-ai

arXiv:2605.15734v1 Announce Type: new Abstract: The use of large language models to assess user states in conversational and adaptive systems is based on the assumption that the metrics used for such

CAX-Agent: A Lightweight Agent Harness for Reliable APDL Automation

model-releasesarxiv-cs-ai

arXiv:2605.15218v1 Announce Type: new Abstract: Large language models deployed for MAPDL finite-element simulation face practical reliability challenges: without structured execution control, tool enc

Centralized vs Decentralized Federated Learning: A trade-off performance analysis

arXiv:2605.16089v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising paradigm for collaborative model training across distributed edge devices while preserving data pri

CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models

arXiv:2601.21798v2 Announce Type: replace Abstract: Large Language Models(LLMs) have revolutionized text generation and multimodal perception,but their capabilities in 3D content generation remain und

researcharxiv-cs-cv

Context-aware Entity-Relation Extraction for Threat Intelligence Knowledge Graphs

model-releasesarxiv-cs-lg

arXiv:2605.15904v1 Announce Type: new Abstract: Cybersecurity Knowledge Graphs (CKGs) unify diverse Cyber Threat Intelligence (CTI) sources into structured, queryable formats, offering scalable soluti

Contexting as Recommendation: Evolutionary Collaborative Filtering for Context Engineering

arXiv:2605.15721v1 Announce Type: new Abstract: Large Language Models (LLMs) are highly sensitive to their input contexts, motivating the development of automated context engineering. However, existin

tutorialsarxiv-cs-cl

Continual Learning of Domain-Invariant Representations

model-releasesarxiv-cs-lg

arXiv:2605.15775v1 Announce Type: new Abstract: Continual learning (CL) aims to train models sequentially over multiple domains without forgetting previously learned knowledge. However, existing CL me

Local Ai

Cross-Modal Registration Between 3D and 2D Fingerprints via Pose-Aware Unwrapping and Point-Cloud Fusion

arXiv:2605.15796v1 Announce Type: new Abstract: Three-dimensional (3D) fingerprints preserve global finger geometry and local ridge structure while avoiding contact-induced deformation, but they remai

local-aiarxiv-cs-cv

DebiasRAG: A Tuning-Free Path to Fair Generation in Large Language Models through Retrieval-Augmented Generation

arXiv:2605.16113v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved unprecedented success due to their exceptional generative capabilities. However, because they depend on kno

Decentralized LoRA augmented transformer with multi-scale feature learning for secured eye diagnosis

model-releasesarxiv-cs-cv

arXiv:2505.06982v3 Announce Type: replace Abstract: Accurate and privacy-preserving diagnosis of ophthalmic diseases remains a critical challenge in medical imaging, particularly given the limitations

Deep Double Q-learning

arXiv:2507.00275v2 Announce Type: replace-cross Abstract: Double Q-learning is a classical control algorithm that mitigates the maximization bias of Q-learning. To do so, it explicitly trains two inde

Deterministic Coreset for Lp Subspace

model-releasesarxiv-cs-lg

arXiv:2601.00361v3 Announce Type: replace-cross Abstract: We introduce the first iterative algorithm for constructing a arepsilon-coreset that guarantees deterministic ell_p subspace embedding for any

DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo

model-releasesarxiv-cs-ro

arXiv:2605.16257v1 Announce Type: new Abstract: Achieving human-level manipulation requires dexterous robotic hands capable of complex object interactions. Advancing such capabilities further demands

Diffusion Policy for Coordinated Control of a Nonholonomic Mobile Base and Dual Arms in Door Opening and Passing

arXiv:2605.15352v1 Announce Type: new Abstract: Opening heavy, self closing doors, especially those that require pulling remains a long standing challenge in robotics. Humans naturally employ both arm

safetyarxiv-cs-ro

DiLA: Disentangled Latent Action World Models

arXiv:2605.15725v1 Announce Type: cross Abstract: Latent Action Models (LAMs) enable the learning of world models from unlabeled video by inferring abstract actions between consecutive frames. However

DiscoExplorer: An Open Interface for the Study of Multilingual Discourse Relations

arXiv:2605.15304v1 Announce Type: new Abstract: The relations connecting propositions in discourse such as cause (A because B) or concession (A although B) are a subject of intense interest in Computa

researcharxiv-cs-cl

Discretizing Group-Convolutional Neural Networks for 3D Geometry in Feature Space

arXiv:2605.15368v1 Announce Type: new Abstract: Group-convolutional neural networks (GCNNs) are among the most important methods for introducing symmetry as an inductive bias in deep learning: In each

safetyarxiv-cs-cv

Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations

arXiv:2605.15205v1 Announce Type: new Abstract: Improving the Theory of Mind (ToM) capability of Large Language Models (LLMs) is crucial for effective social interactions between these AI models and h

applicationsarxiv-cs-ai

Domain-Independent Game Abstraction using Word Embedding Techniques

arXiv:2605.15543v1 Announce Type: cross Abstract: Many games of interest in the real world are often intractably large, thereby necessitating the use of game abstraction to shrink them in size, typica

applicationsarxiv-cs-ai

Drawback of Enforcing Equivariance and its Compensation via the Lens of Expressive Power

arXiv:2512.09673v3 Announce Type: replace-cross Abstract: Equivariant neural networks encode the intrinsic symmetry of data as an inductive bias, which has achieved impressive performance in wide doma

Effective Harness Engineering for Algorithm Discovery with Coding Agents

arXiv:2605.15221v1 Announce Type: cross Abstract: AlphaEvolve and FunSearch have demonstrated the potential of combining large language models (LLMs) with evolutionary search for automated algorithm d

Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes

arXiv:2605.15586v1 Announce Type: cross Abstract: Complementary-label learning (CLL) is a weakly supervised paradigm where instances are labeled with classes they do not belong to. Despite a decade of

EMFusion: An Uncertainty-Aware Conditional Diffusion Framework for Frequency-Selective EMF Forecasting in Wireless Networks

arXiv:2512.15067v3 Announce Type: replace-cross Abstract: The rapid growth in wireless infrastructure has increased the need to accurately estimate and forecast electromagnetic field (EMF) levels to e

tutorialsarxiv-cs-ai

Enabling Adversarial Robustness in AI Models through Kubeflow MLOps

arXiv:2605.15249v1 Announce Type: cross Abstract: AI models are increasingly deployed in cloud-native environments to support scalable and automated services. However, while platforms such as Kubernet

researcharxiv-cs-lg

Ensuring Logic in the Fog: Sound POMDP Synthesis with LTL Objectives

model-releasesarxiv-cs-ai

arXiv:2605.12581v1 Announce Type: cross Abstract: Synthesising autonomous agents that can navigate uncertain environments while adhering to complex temporal constraints remains a fundamental challenge

Evaluating Design Video Generation: Metrics for Compositional Fidelity

arXiv:2605.16223v1 Announce Type: cross Abstract: Generative video models are increasingly used in design animation tasks, yet no standardized evaluation framework exists for this domain. Unlike natur

Hardware

FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization

arXiv:2605.15824v1 Announce Type: new Abstract: Human-centric video customization, particularly at the garment level, has shown significant commercial value. However, existing approaches cannot suppor

hardwarearxiv-cs-cv

FedOptima: Optimizing Resource Utilization in Federated Learning

arXiv:2504.13850v2 Announce Type: replace-cross Abstract: Federated learning (FL) systems facilitate distributed machine learning across a server and multiple devices. However, FL systems have low res

applicationsarxiv-cs-lg

From Pairs to Sequences: Track-Aware Policy Gradients for Keypoint Detection

arXiv:2602.20630v4 Announce Type: replace Abstract: Keypoint-based matching is a fundamental component of modern 3D vision systems, such as Structure-from-Motion (SfM) and SLAM. Most existing learning

safetyarxiv-cs-cv

FSCM: Frequency-Enhanced Spatial-Spectral Coupled Mamba for Infrared Hyperspectral Image Colorization

arXiv:2605.15880v1 Announce Type: cross Abstract: Thermal infrared imaging is robust to illumination variations and smoke interference, making it important for all-weather perception. However, the lac

Gaussian Relational Graph Transformer

arXiv:2605.15575v1 Announce Type: new Abstract: Relational graph learning models relational databases as graphs and has demonstrated superior performance on a wide range of relational predictive tasks

safetyarxiv-cs-lg

Hallucinations are inevitable but can be made statistically negligible

arXiv:2502.12187v3 Announce Type: replace Abstract: Hallucinations, a phenomenon where a language model (LM) generates nonfactual content, pose a significant challenge to the practical deployment of L

researcharxiv-cs-cl

How to Choose Your Teacher for Fine Grained Image Recognition

arXiv:2605.15689v1 Announce Type: new Abstract: Fine-grained image recognition classifies subcategories such as bird species or car models. While state-of-the-art (SOTA) models are accurate, they are

tutorialsarxiv-cs-cv

Inevitable Encounters: Backdoor Attacks Involving Lossy Compression

arXiv:2603.13864v2 Announce Type: replace-cross Abstract: Real-world backdoor attacks often require poisoned datasets to be stored and transmitted before being used to compromise deep learning systems

applicationsarxiv-cs-cv

Is One Score Enough? Rethinking the Evaluation of Sequentially Evolving LLM Memory

arXiv:2605.15384v1 Announce Type: cross Abstract: Memory plays a central role in enabling large language models (LLMs) to operate over sequential tasks by accumulating and reusing experience over time

ITGPT: Generative Pretraining on Irregular Timeseries

arXiv:2605.16069v1 Announce Type: new Abstract: Timeseries regression models often struggle to leverage large volumes of labeled multimodal data, particularly when the data are irregularly sampled or

applicationsarxiv-cs-lg

Local Ai

JAM-Flow: Joint Audio-Motion Synthesis with Flow Matching

arXiv:2506.23552v2 Announce Type: replace Abstract: The intrinsic link between facial motion and speech is often overlooked in generative modeling, where talking head synthesis and text-to-speech (TTS

local-aiarxiv-cs-cv

Layer-wise Derivative Controlled Networks

arXiv:2605.15463v1 Announce Type: new Abstract: As machine learning models grow in complexity, they increasingly struggle with three conflicting demands: the need for high accuracy, the requirement fo

applicationsarxiv-cs-lg

Learn Where Outcomes Diverge: Efficient VLA RL via Probabilistic Chunk Masking

arXiv:2605.16154v1 Announce Type: new Abstract: Reinforcement learning (RL) allows vision-language-action (VLA) policies to generalize beyond their training distribution by optimizing directly for tas

safetyarxiv-cs-lg