I am a Master's student at the University of Science and Technology of China (USTC), focusing on intelligent systems that combine retrieval, reasoning, and adaptive memory. My current research centers on personalized agentic RAG systems with autonomous decision-making and self-correction loops.
I am also deeply interested in reinforcement learning for LLM agents, long-tailed recognition, and sharpness-aware minimization (SAM) methods for imbalanced datasets. I enjoy building end-to-end systems and open-sourcing my work when possible.
Designing memory-augmented agentic RAG with adaptive retrieval, user modeling, confidence estimation, and self-correction loops. Built with LangGraph, LangChain, FAISS, and LLMs.
A RAG prototype for campus knowledge retrieval using Ollama embeddings, Chroma vector database, and a Gradio-based user interface.
A class-adaptive focal loss combined with Sharpness-Aware Minimization (SAM) for long-tailed visual recognition. Evaluated on CIFAR-10/100-LT, TinyImageNet, ImageNet-LT, and iNaturalist 2018.
A 3-agent workflow (Research / Writer / Editor) with real USDC nanopayments on Arc Testnet. Includes on-chain verification via web3.py and hits both Usage-Based Compute Billing and Agent-to-Agent Payment Loop tracks.
An ML-powered resume screening and candidate ranking system built during a remote internship. Uses NLP and transformer-based models for automated candidate evaluation.
Hands-on implementation of Dueling DQN and modern RL algorithms based on ARENA, UC Berkeley CS 285, and Spinning Up. Exploring RL for LLM agents and mechanistic interpretability.
Weather trend forecasting using time series analysis and ML models.
An interactive AI-powered study assistant with personalized learning paths, quiz generation, and progress tracking using LLM-based techniques.
Coming soon — publications in preparation.