Welcome!
I am an Applied Scientist at Amazon, on the LLM training and evaluation stack for customer-facing agentic systems, spanning dense and MoE architectures up to 400B+. I led two tracks: mid-training for the capability substrate — long-context and fundamental agent capability — shipped in Rufus model releases in 2024–2025; and post-training, RL, and rubric-based evaluation for model behavior — personalization, faithfulness, and multi-turn consistency — supporting releases in 2025–2026, closed by a production data flywheel that turns live-traffic failures into continuous improvement.
I obtained my Ph.D. in Computer Science from Texas A&M University, with 20+ publications (8 first-authored) at NeurIPS (Spotlight), ACL, EMNLP, and NAACL; representative first-author work includes HYTREL (NeurIPS Spotlight, set-attention for tabular LMs) and CoMM (NAACL, early multi-agent reasoning). One US patent; Area Chair at ARR.
My current research focuses on building reliable, safe, and self-evolving agentic LLMs that stay consistent over multi-turn and long-horizon interactions — through verifiable evaluation and eval-driven RL optimization.
Email: chenpei.net@gmail.com
Links: LinkedIn
Google Scholar
Office: Santa Clara, CA
- LLM Mid/Post-training (Long-context, Agentic Capability)
- Rubric-based Evaluation & RL for Agentic Systems
- Personalization, Memory, Multi-turn and Long-horizon Modeling
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Ph.D. in Computer Science, 2019 - 2024
Texas A&M University
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MS in Finance
Southwestern University of Finance and Economics
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B.Eng. in Simulation Engineering
National University of Defense Technology
News
- 2026: 3 papers on multi-turn modeling (via GRPO), personalization, and agentic systems accepted to ACL 2026. 🎉
- 2026: Serving as Area Chair for ARR. 🎉
- 2026: US Patent 12,530,529 granted for Domain-specific NER via Graph Neural Networks.
- 2025: 6 papers accepted to NAACL-2025, ACL-2025, and EMNLP-2025, covering long-context modeling, agents, RAG, and post-training data flywheel. ✨
- 2024: First-authored long paper (CoMM) accepted to NAACL-2024 — a pioneering multi-agent prompting framework for complex LLM reasoning. 👋
- 2023: First-authored paper (HYTREL) accepted to NeurIPS-2023 as a Spotlight presentation (top 5%). ✨
Selected Publications
LLM & Foundation Model Training
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HYTREL: Hypergraph-enhanced Tabular Data Representation Learning
Pei Chen, Soumajyoti Sarkar, Leonard Lausen, Balasubramaniam Srinivasan, Sheng Zha, et al.
NeurIPS 2023, Spotlight (top 5%) -
Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning
Ming Li, Pei Chen, Chenguang Wang, Hongyu Zhao, Yijun Liang, et al.
ACL 2025, Findings -
ItD: Large Language Models Can Teach Themselves Induction through Deduction
Wangtao Sun, Haotian Xu, Xuanqing Yu, Pei Chen, Shizhu He, et al.
ACL 2024, long paper -
Aligning Large Language Models with Implicit Preferences from User-Generated Content
Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Pei Chen, et al.
ACL 2025, long paper
Agent
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CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving
Pei Chen, Shuai Zhang, Boran Han
NAACL 2024, Findings -
Hephaestus: Improving Fundamental Agent Capabilities of LLMs through Continual Pre-Training
Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Pei Chen, et al.
NAACL 2025, long paper -
Improving LLMs Function Calling and Interpretability via Guided-Structured Templates
Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Pei Chen, et al.
EMNLP 2025, long paper
RAG & Long-context & Personalization
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LongLeader: A Comprehensive Leaderboard for LLMs in Long-context Scenarios
Pei Chen, Hongye Jin, Cheng-Che Lee, Rulin Shao, Jingfeng Yang, et al.
NAACL 2025, long paper -
ALERT: An LLM-powered Benchmark for Automatic Evaluation of Recommendation Explanations
Yichuan Li, Xinyang Zhang, Chenwei Zhang, Mao Li, Pei Chen, et al.
NAACL 2025, long paper -
UniConv: Unifying Retrieval and Response Generation for LLMs in Conversations
Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Pei Chen, et al.
ACL 2025, long paper
Experience
Misc.
🔬 By day: a creative and hands-on LLM scientist with a strong research mindset and a builder’s instinct, excited by bold new ideas in LLMs, AI assistants, and agentic systems. Enjoys exploring emerging directions and turning them into practical solutions for real-world industry problems.
💻 By night: a geek who reads papers for fun, tinkers with side projects, and has strong opinions about post-training recipes.
🥊 On weekends: 1st DAN in ITF Taekwon-Do, active in swimming, badminton, and boxing — because the best debugging happens after a good workout.