Search: “applications”

Toward Robust Semi-supervised Regression via Dual-stream Knowledge Distillation

arXiv:2508.14082v3 Announce Type: replace Abstract: Semi-supervised regression (SSR), which aims to predict continuous scores for samples while reducing the reliance on large-scale labeled data, has r

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Research

Transferable Graph Condensation from the Causal Perspective

DGX agent

arXiv:2601.21309v4 Announce Type: replace Abstract: The increasing scale of graph datasets has significantly improved the performance of graph representation learning methods, but it has also introduc

researcharxiv-cs-lg

28 May 2026

Utility-Aware Multimodal Contrastive Learning for Product Image Generation

arXiv:2605.28733v1 Announce Type: new Abstract: Product images strongly influence consumer decision-making in online marketplaces. Empowered by multimodal contrastive learning, generative AI can outpu

Variance-Adaptive Optimal Algorithm for Reinforcement Learning with Multinomial Logit Function Approximation

arXiv:2605.28364v1 Announce Type: cross Abstract: Reinforcement learning with multinomial logistic (MNL) function approximation has become an important framework due to its flexibility and broad appli

VITAL: Visual-Semantic Dual Supervision for Enhanced and Interpretable Latent Reasoning in Medical MLLMs

arXiv:2605.28422v1 Announce Type: cross Abstract: Latent reasoning enables reasoning over continuous hidden states rather than explicit tokens, avoiding the language bottleneck and inference overhead

Who Uses AI? Platform Selection and the Measurement of Occupational AI Exposure

arXiv:2605.21743v2 Announce Type: replace Abstract: Conversation logs from AI platforms are increasingly used to measure occupational exposure to artificial intelligence, but the users observed in the

2-ASP(Q) programs with weak constraints: Complexity and efficient implementation

arXiv:2605.27338v1 Announce Type: new Abstract: ASP(Q) extends Answer Set Programming (ASP) with Quantifiers over answer sets. In this paper we focus on the class of ASP(Q) programs with two quantifie

researcharxiv-cs-ai

A Bioinspired Underwater Robot with a Latch-Mediated Soft Bistable Mechanism

arXiv:2605.26936v1 Announce Type: new Abstract: Underwater robotics has advanced significantly over recent decades. however, the development of miniaturized underwater robots remains limited by low en

researcharxiv-cs-ro

A Dataset of Robot-Patient and Doctor-Patient Medical Dialogues for Spoken Language Processing Tasks

arXiv:2605.26747v1 Announce Type: new Abstract: Large Language Models (LLMs) have brought huge improvements to Artificial Intelligence (AI), which can be applied to general-purpose tasks. However, the

A Unified Framework for Diffusion Model Unlearning with f-Divergence

arXiv:2509.21167v2 Announce Type: replace-cross Abstract: Most existing methods for concept unlearning in text-to-image diffusion models minimize a mean squared error (MSE) loss between the denoiser o

Adaptive Reinforcement Learning for Robust Open Quantum System Control: A Multi-Task Framework with Temporal Optimization

arXiv:2605.26925v1 Announce Type: cross Abstract: We present a Multi-task Soft Actor-Critic (SAC) Reinforcement Learning framework designed for open-system quantum control across diverse Hamiltonians,

Tutorials

An investigation of AI integration in sound designer workflows and experiences

arXiv:2605.27174v1 Announce Type: cross Abstract: Artificial intelligence is increasingly being integrated into professional audio production workflows, yet a gap persists between the tools developers

tutorialsarxiv-cs-ai

AnySurf: Any Surface Generation with Directed Edge

arXiv:2605.26149v1 Announce Type: cross Abstract: Open surface components prevail in real industrial 3D content and support rendering, physical simulation and geometric editing. Garments serve as a ty

Approximate Equivariance via Projection-based Regularisation

arXiv:2601.05028v2 Announce Type: replace Abstract: Equivariance is a powerful inductive bias in neural networks, improving generalisation and physical consistency. Recently, however, non-equivariant

Agents

Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems

arXiv:2502.14321v3 Announce Type: replace-cross Abstract: Large language model-based multi-agent systems have recently gained significant attention due to their potential for complex, collaborative, a

agentsarxiv-cs-cl

Bilevel Optimization over Saddle Points of Zero-Sum Markov Games

arXiv:2605.26654v1 Announce Type: cross Abstract: Reinforcement learning (RL) often has a hierarchical structure, where an upper-level (UL) learner selects model parameters and a lower-level (LL) deci

Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial

arXiv:2605.26577v1 Announce Type: cross Abstract: Learning-based methods for synthesizing controllers have gained popularity due to their high expressiveness and strong empirical performance. However,

CktGen: Automated Analog Circuit Design with Generative Artificial Intelligence

model-releasesarxiv-cs-lg

arXiv:2410.00995v3 Announce Type: replace Abstract: The automatic synthesis of analog circuits presents significant challenges. Most existing approaches formulate the problem as a single-objective opt

Constrained Meta Reinforcement Learning with Provable Test-Time Safety

arXiv:2601.21845v2 Announce Type: replace Abstract: Meta reinforcement learning (RL) allows agents to leverage experience across a distribution of tasks on which the agent can train at will, enabling

Conv-to-Bench: Evaluating Language Models Via User-Assistant Dialogues In Code Tasks

arXiv:2605.26440v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) has outpaced the scalability of traditional evaluation benchmarks, which remain heavily dependent

safetyarxiv-cs-cl

Credit-assigned Policy Gradient for Early Stage Retrieval in Two-stage Ranking

arXiv:2605.26385v1 Announce Type: cross Abstract: Large-scale search, recommendation, and retrieval-augmented generation (RAG) systems typically employ a two-stage architecture: an early-stage ranker

Distribution-Aware Conformal Prediction: A Framework for generating efficient prediction intervals for time series

model-releasesarxiv-cs-lg

arXiv:2605.26569v1 Announce Type: new Abstract: We present Distribution-aware Conformal Prediction (DCP), a unified framework integrating probabilistic predictors like Monte Carlo dropout, deep ensemb

Doc-CoB: Enhancing Document Understanding with Visual Chain-of-Boxes Reasoning

arXiv:2505.18603v2 Announce Type: replace Abstract: Document understanding aims to perform question answering and information extraction over document images, where the visual content is highly inform

researcharxiv-cs-ai

DV-SFT: Direct Vision Supervision for Fine-Grained Visual Understanding

arXiv:2605.26656v1 Announce Type: new Abstract: Multimodal large language models are typically trained end-to-end to predict ground-truth answers, yet supervision signals are applied exclusively to te

safetyarxiv-cs-cv

Local Ai

Edge AI Deployment Beyond Models: A BSP-Aware Systems Framework for Industrial Embedded Platforms

arXiv:2605.26119v1 Announce Type: cross Abstract: Industrial Edge AI programs often begin with the model and only later confront the platform. That sequencing is attractive because it allows early dem

local-aiarxiv-cs-ai

EpiCurveBench: Evaluating VLMs on Epidemic Curve Digitization

model-releasesarxiv-cs-cl

arXiv:2605.27195v1 Announce Type: new Abstract: Chart-to-data extraction with vision-language models (VLMs) is increasingly evaluated on benchmarks that show diminishing headroom (frontier VLMs exceed

Local Ai

Experiments in Agentic AI for Science

arXiv:2605.26305v1 Announce Type: new Abstract: This paper details two novel frameworks for developing autonomous, agentic AI in scientific workflows. Both systems leverage a hybrid Local Body, Remote

local-aiarxiv-cs-ai

Agents

GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing

arXiv:2605.27360v1 Announce Type: cross Abstract: Cellular research and development (R&D) is throttled by six structural processes that each consume months of manual engineering work per iteration: (i

agentsarxiv-cs-ai

Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering

arXiv:2605.26620v1 Announce Type: new Abstract: Natural language conveys information at varying levels of granularity, from fine-grained references to broad descriptions. While granularity is fundamen

researcharxiv-cs-cl

Gumbel Machine: Counterfactual Student Writing Generation via Gumbel Noise Steering

arXiv:2605.27249v1 Announce Type: new Abstract: An effective method of teaching across disciplines is to provide examples of high-quality work. However, an example may be significantly different from

researcharxiv-cs-ai

Tutorials

How to Square Tensor Networks and Circuits Without Squaring Them

arXiv:2512.17090v2 Announce Type: replace-cross Abstract: Squared tensor networks (TNs) and their extension as computational graphs--squared circuits--have been used as expressive distribution estimat

tutorialsarxiv-cs-ai

InterSketch: An Interleaved Reasoning Model with Self-correcting Visual Sketch and Stepwise Reward

arXiv:2605.26520v1 Announce Type: cross Abstract: While vision-language models (VLMs) have exhibited multi-turn visual reasoning capabilities, their reasoning trajectories remain relatively shallow an

Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation

arXiv:2511.14993v3 Announce Type: replace-cross Abstract: This report introduces Kandinsky 5.0, a family of state-of-the-art foundation models for high-resolution image and 10-second video synthesis.

L-Learning : A Lyapunov-Based Approach Leveraging Lagrangian Mechanics for Efficient and Stable Robot Tracking

arXiv:2605.26648v1 Announce Type: new Abstract: This paper presents L-Learning, a novel data-driven control framework for robotics that integrates Lyapunov stability theory with Lagrangian mechanics t

researcharxiv-cs-ro

Learning Dynamic Graph Representations through Timespan View Contrasts

arXiv:2605.27063v1 Announce Type: new Abstract: The rich information underlying graphs has inspired further investigation of unsupervised graph representation. Existing studies mainly depend on node f

LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AV

model-releasesarxiv-cs-cv

arXiv:2605.26244v1 Announce Type: new Abstract: Audio-visual generation is rapidly advancing from short clips to minute-long content, while existing evaluation protocols remain largely confined to sho

Near-Optimal Regret in Adversarial Kernel Bandits

model-releasesarxiv-cs-lg

arXiv:2605.26585v1 Announce Type: new Abstract: We study the adversarial kernel bandit problem, in which the loss at each round is induced by an arbitrary bounded element of a reproducing kernel Hilbe

Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study

arXiv:2410.00357v2 Announce Type: replace Abstract: Neural scaling laws play a pivotal role in the performance of deep neural networks and have been observed in a wide range of tasks. However, a compl

Normalizing Flows on Quotient Manifolds via Boundary Quotients

arXiv:2511.22882v3 Announce Type: replace Abstract: We introduce boundary quotients and present a framework for learning densities on manifolds that arise as boundary quotients of simpler domains. We

Agents

Optimal Rates for Feasible Payoff Set Estimation in Games

arXiv:2602.04397v2 Announce Type: replace-cross Abstract: We study a setting in which two players play a (possibly approximate) Nash equilibrium of a bimatrix game, while a learner observes only their

agentsarxiv-cs-lg

Tutorials

Planning Neural Dynamics with Lie Group Embedding through Supervised Projective Manifold Learning

arXiv:2605.26167v1 Announce Type: cross Abstract: We propose Lie group embedded dynamical neural networks (LieEDNN) and the corresponding learning algorithms based on gradient descent and metric proje

tutorialsarxiv-cs-ai

Probabilistic Recurrent Intention Switching Model

arXiv:2605.26998v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL) recovers reward functions from observed behavior, yet traditional methods assume a single stationary reward that ca

Local Ai

Prototyping an End-to-End Multi-Modal Tiny-CNN for Cardiovascular Sensor Patches

arXiv:2510.18668v2 Announce Type: replace-cross Abstract: The vast majority of cardiovascular diseases may be preventable if early signs and risk factors are detected. Cardiovascular monitoring with b

local-aiarxiv-cs-cv

Pusa V1.0: Unlocking Temporal Control in Pretrained Video Diffusion Models via Vectorized Timestep Adaptation

arXiv:2507.16116v2 Announce Type: replace Abstract: The rapid advancement of video diffusion models has been hindered by fundamental limitations in temporal modeling, particularly the rigid synchroniz

ReasonOps: A Unified Operational Paradigm for Trustworthy Verified LLM Reasoning

arXiv:2605.27014v1 Announce Type: cross Abstract: Large Language Models (LLMs) have transformed artificial intelligence from primarily generative systems into increasingly capable reasoning agents. Re

Reformulation of RBM to Unify Linear and Nonlinear Dimensionality Reduction

arXiv:1909.08210v4 Announce Type: replace Abstract: A restricted Boltzmann machine (RBM) is a two-layer neural network with shared weights and has been extensively studied for dimensionality reduction

Revealing the core dimensions underlying representations in brains, behavior and AI

arXiv:2605.26921v1 Announce Type: new Abstract: The study of representations is widespread across fields, including neuroscience, psychology, and artificial intelligence. While representations are oft

Scaling, Benchmarking, and Reasoning of Vision-Language Agents for Mobile GUI Navigation

arXiv:2605.27134v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown rapid progress in mobile GUI navigation. This paper presents a systematic study of data scaling, benchmarking,