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ThetaEvolve: Test-time Learning on Open Problems
The open-source code for the NeurIPS 2025 paper, "Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning."
[Survey] A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
Open-source implementation of AlphaEvolve
ShinkaEvolve: Towards Open-Ended and Sample-Efficient Program Evolution
KDD2025, Generative Next POI Recommendation with Semantic ID
[NeurIPS 2025] CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension
The code for NeurIPS 2025 paper "A-MEM: Agentic Memory for LLM Agents"
A Survey of Personalization: From RAG to Agent
Search-R1: An Efficient, Scalable RL Training Framework for Reasoning & Search Engine Calling interleaved LLM based on veRL
This repository introduce a comprehensive paper list, datasets, methods and tools for memory research.
[IJCAI'24] Official code for our paper "Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction".
tmlr-group / MC-GRA
Forked from AndrewZhou924/MC-GRA[ICML 2023] "On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation"
Code for paper “Domain-Informed Negative Sampling Strategies for Dynamic Graph Embedding in Meme Stock-Related Social Networks”
Negative Sampling in Next-POI Recommendations: Observation, Approach, and Evaluation [ACM WWW-24]
ckpassenger / DyGLib_CNEN
Forked from yule-BUAA/DyGLibDyGLib with CNEN (KDD 2024)
code and data for Improving Temporal Link Prediction via Temporal Walk Matrix Projection, NeurIPS 2024
ExNext: Self-Explainable Next POI Recommendation, ACM SIGIR Conference on Research and Development in Information Retrieval, 2024
The official PyTorch implementation for 2025-AAAI-Integrating Personalized Spatio-Temporal Clustering for Next POI Recommendation
Codes for CIKM'24 paper: DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning