Showing 25 papers for 2026-07-15
OmniPM-Net uses ConvCNP to fuse CTM gridded forecasts with GNN-based site forecasts into a shared spatial representation. The goal is to deliver both station-scale accuracy and continuous spatial fields, particularly under dust storms. The terrain-aware Gaussian framework helps reconcile biases and produce coherent gridded PM10 maps.
The authors construct a dataset of 131,406 Cayley graphs covering groups up to order 767 (except 512), with exact algebraic labels and rich graph statistics. This census aims to provide benchmarks for how finite-group properties manifest in graph structure and spectral statistics. The data enables novel learning tasks in algebraic graph theory.
We systematically evaluate five image encoders to understand how encoder choice shapes graph construction for GCN-based breast ultrasound classification. The study investigates how image features propagate to graph connectivity and homophily, and how these factors influence diagnostic performance under noise and variability.
We propose Variational Mixture of Graph Neural Experts (VMoGE) that integrates multi-band EEG with variational GNNs and a mixture-of-experts architecture to better separate dementia subtypes and stages across frequency bands.
Addresses multitask algorithmic reasoning by learning branching networks to handle multiple execution traces; differences across execution traces pose a core challenge. The paper proposes branching architectures to enable efficient multitask reasoning.
Introduces Graph Regularized PCA (GR-PCA), which uses a learned sparse precision graph and biases loadings toward low-frequency Fourier modes of the graph Laplacian. This suppresses high-frequency noise while preserving graph-coherent signals for interpretable components.
Event-based Temporal Graph Neural Networks (ETGNNs) are explained by tracing the complete information flow from features to outputs, not just embeddings, to improve explainability. The approach identifies key pathways through which information travels in the model.
This survey reviews dynamic AI agent skills and how skill libraries evolve over time, presenting a lifecycle-managed, verified, evolving artifact store framework. It analyzes a corpus of 124 papers from 2023–2026 to synthesize trends.
UNIT addresses graph continual learning challenges by enforcing semantic-structural separation and improving imbalanced knowledge transfer, particularly for streaming graph data in real-world multimodal web settings.
NVAITC AI Scientist (NAIS) is a governed end-to-end agentic research system that supports domain-general scientific workflows while keeping protected data within governed boundaries; demonstrated via a hypertension GWAS case study.
Comparative analysis of GAT and BERT for human-like playtesting investigates how graph attention networks vs. language-model-based predictors capture player strategies, particularly under new game mechanics and with limited feature engineering.
A large-scale MCP implementation dataset from GitHub is presented, built with a hybrid pipeline that combines GitHub REST/GraphQL APIs and Python verification scripts to capture and validate MCP implementations.
GAE (Graph-Augmented Evolution) integrates a relational GNN with evolutionary search to overcome structurally blind parent selection, sparse rewards, and static mutation operators, guiding automated scientific discovery with graph structure.
SALT-GNN introduces statistics-aware attention to handle dense neighborhoods in AML graphs, addressing the issue that high-activity recipient accounts concentrate many transactions and hinder detection.
Anamnesis is an open-source platform for backstory-conditioned survey simulation, enabling non-technical users to prototype surveys on virtual populations. It operationalizes Anthology and Alterity to condition model responses within a web interface.
Vinci2 frames proactive assistance in continuous egocentric videos as a context-dependent decision problem, where the agent decides when to speak based on the user’s history and current activity.
Structure-Feature Aligned Graph Learning proposes a two-view framework that decouples structure-conditioned GNN embeddings from a structure-free feature prior learned by an independent anchor model, using alternating constrained optimization.
InqEduAgent introduces an LLM-powered framework that uses Gaussian process augmentation to adaptively select learning partners for inquiry-based learning, improving personalization and learning outcomes.
Graph Optimization Foundation Model (GFM) tokenizes graphs into a language-model paradigm, enabling a first-of-its-kind bridge between LLMs and graph optimization problems.
Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search uses a GNN surrogate with uncertainty calibration (MC dropout) and an expected improvement criterion to efficiently search variational quantum circuits in a hybrid quantum-classical loop.
GenGNN introduces a modular message-passing backbone for discrete graph generation that preserves edge fields through latent refinement of coupled node-edge graph states, avoiding the need for global attention. When paired with diffusion models, GenGNN-based generators achieve high validity, reporting over 90% on standard benchmarks.
The paper investigates how generative AI changes students' study time and durable learning outcomes using a decade of data from 3.2 million ALEKS learning interactions and placement assessments. It analyzes time on task, proctoring context, and learning outcomes to assess AI influence, reporting nuanced findings that reconcile self report with behavioral evidence.
Diffusion-GR2 presents a diffusion based generative reasoning re-ranker that decodes many positions in parallel to reduce inference cost, significantly speeding up compared with autoregressive approaches. It tackles two accuracy gaps that arise when naively converting autoregressive re-rankers into diffusion decoders by aligning reasoning traces with the final rankings.
The study examines how millions coordinate at scale in Reddit r/place across three editions (2017, 2022, 2023), focusing on engagement, collaboration, and conflict. It analyzes mass participation as a socio technical system shaped by decentralized coordination, platform design, and resource competition.
SkillSelect-Serve studies QoS aware budgeted skill service recommendation for LLM agents, noting that skills convey input–output assumptions, tool dependencies, context costs, and risk metadata. Small LLM agents face context limits that make fixed Top-k recommendations inadequate, motivating a planning based or cost aware selection method that accounts requirements and risk.