Hi, I'm Mingfei. 👋
I'm a PhD student at the University of Technology Sydney, advised by Prof. Yi Zhang. My research is on Multi-Agent Systems and Agent Memory for large language models — broadly, how should agents remember, reason, and coordinate so that they actually help people?
Always happy to chat about agent memory architectures, multi-agent systems, and legal agentic systems. Reach out →
🔥 What's new
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2026New preprint: Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection — diffusion-guided learning for few-shot graph fraud detection.
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2026Our paper From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation is accepted to ACL 2026 (Oral) 🎉
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2026New preprint: Choosing How to Remember: Adaptive Memory Structures for LLM Agents — a framework (FluxMem) that lets agents pick the right memory structure per context. +9.18% / +6.14% on PERSONAMEM / LoCoMo.
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2025From Newborn to Impact: Bias-Aware Citation Prediction accepted to The Web Conference 2026 🎉
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2025Joined UTS as a PhD student under Prof. Yi Zhang. Research focus: agent memory and multi-agent reasoning.
📝 Selected Publications
From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation
Annual Meeting of the Association for Computational Linguistics (ACL Main), 2026. CCF-A
We construct JurisCQAD, a large-scale, expert-verified dataset of 43,000 real-world Chinese legal consultation entries, and propose JurisMA, a modular multi-agent framework based on legal element graphs that supports dynamic routing, statute alignment, and style optimization. JurisMA significantly outperforms both general-purpose and legal-domain LLM baselines on LawBench.
From Newborn to Impact: Bias-Aware Citation Prediction
Proceedings of the ACM Web Conference (WWW), 2026, pp. 7600–7609. CCF-A
A bias-aware citation prediction framework addressing the challenge of forecasting impact for newly published papers under sparse early citation signals and long-tail distributions. By combining multi-agent feature extraction with robust graph representation learning, the model reduces MALE / RMSLE by ~13% and improves NDCG by 5.5%.
Choosing How to Remember: Adaptive Memory Structures for LLM Agents
arXiv preprint arXiv:2602.14038, 2026.
Existing agent memory systems use a one-size-fits-all structure and don't treat structure selection as a context-adaptive decision. We propose FluxMem, which equips agents with multiple complementary memory structures and learns to pick among them per interaction, with a three-level hierarchy and a Beta-Mixture-Model probabilistic gate replacing brittle similarity thresholds. +9.18% on PERSONAMEM, +6.14% on LoCoMo.
For the live citation count, see my Google Scholar.
📖 Education
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Jan 2025 — presentPh.D. in Artificial Intelligence, University of Technology Sydney
Australian Artificial Intelligence Institute (AAII) · Advisor: Prof. Yi Zhang
👩🏫 Teaching
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Feb 2025 — presentAcademic Tutor · Computer Science Studio 1 (41078)
Faculty of Engineering and Information Technology, University of Technology Sydney
🤝 Academic Service
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