The paper list of "Memory in the Age of AI Agents: A Survey"

A web-based collaborative LaTeX editor (CE extended with free premium features)

Code for TH-GNN (paper accepted by KDD2022)

A Survey on Large Language Model-Based Game Agents

An LLM-based Multi-Agent Framework for Financial Crime & Suspicious Matter Reporting

📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程

12 Weeks, 24 Lessons, AI for All!

📚 从零开始的大语言模型原理与实践教程

🛠️ DeepAgent: A General Reasoning Agent with Scalable Toolsets

Continuously updated paper list on advancements in Data Agents. Companion repo to our paper "A Survey of Data Agents: Emerging Paradigm or Overstated Hype?"

[ICML 2025] Implementation of "GNN+ (ModernGNN): Can Classic GNNs Be Strong Baselines for Graph-level Tasks?"

[NeurIPS 2024] Implementation of "Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification"

Official Repository of "LLM × DATA" Survey Paper

Network pings and other utilities

主要记录大语言大模型（LLMs） 算法（应用）工程师相关的知识及面试题

PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides [EMNLP 2025]

[NeurIPS 2025 D&B] Open-source Multi-agent Poster Generation from Papers

A collection of graph foundation models including papers, codes, and datasets.

This repo contains papers, books, tutorials and resources on Riemannian deep learning.

MLNLP社区用来帮助大家避免论文投稿小错误的整理仓库。 Paper Writing Tips

A final sanity checklist to help your CS paper get accepted, not desk rejected.

Code for: "A Generalised Signature Method for Time Series"

CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)

Papers about explainability of GNNs

PaSa -- an advanced paper search agent powered by large language models. It can autonomously make a series of decisions, including invoking search tools, reading papers, and selecting relevant refe…

GraphAny: Fully-inductive Node Classification on Arbitrary Graphs

Learning Fraud Detection from research papers and industry applications.

A LLM-based Agent that predict its tasks proactively.

This collection of papers can be used to summarize research about graph reinforcement learning for the convenience of researchers.