Tenghao Huang

Senior Applied Scientist, Office of Applied Research, Microsoft.

Tenghao Huang

I build language-model agents that learn from experience, use tools effectively, and help people tackle complex tasks.

Joining the University of Utah as an Assistant Professor in Fall 2027. I am recruiting Ph.D. students — see hiring details.

Research interests

  • LLM agents and post-training: long-horizon planning, tool use, agentic memory, and proactive information gathering.
  • Human-centered AI: social reasoning, grounded dialogue, and systems that collaborate effectively with people.
  • AI creativity: evaluating creative outputs, generating compelling narratives, and balancing diversity with quality. I coauthored the NAACL tutorial Creative Planning with Language Models.

Background

At USC Viterbi, I worked with Muhao Chen and Jonathan May. I earned my bachelor’s degree at UNC-Chapel Hill, where I worked with Snigdha Chaturvedi and Colin Raffel. I have also interned at Amazon Alexa AI, IBM Research, and Salesforce AI Research. My research has been reported by MIT Technology Review, and I received the EMNLP 2024 Outstanding Paper Award.

Hiring

I am recruiting Ph.D. students to join my lab at the University of Utah in Fall 2027.

news

Feb 20, 2026 Invited talk at Microsoft on Generative Learning via Adversarial Reward Estimation.
Aug 25, 2025 I am excited to return as a part-time research intern at Microsoft in Fall 2025. My spring intern paper Teaching Language Models to Gather Information Proactively is accepted by EMNLP-Findings 2025. I will keep working on post-training LLMs to align with human preferences:)
May 15, 2025 Our paper R2D2: Remembering, Replaying and Dynamic Decision Making with a Reflective Agentic Memory is accepted by ACL 2025!
May 04, 2025 We’re so excited to have our tutorial presented at NAACL 2025. This is a tutorial on Creative Planning in Large Language Models.

selected publications

  1. arXiv
    Evaluating LLM Creativity through Long-Tail Performance
    Yichen Wang ,  Tenghao Huang ,  Vishakh Padmakumar ,  and  Mina Lee
    arXiv preprint arXiv:2610.12998, 2026
  2. arXiv
    GLARE: Generative Learning via Adversarial Reward Estimation For Social Dynamics Forecasting
    Tenghao Huang ,  Zhaoxuan Tan ,  Muhao Chen ,  Jonathan May ,  Mengting Wan ,  Longqi Yang ,  Pei Zhou ,  and  Sihao Chen
    arXiv preprint arXiv:2609.12165, 2026
  3. ACL
    GTA: Generating Long-Horizon Tasks for Web Agents at Scale
    Tenghao Huang ,  Kung-Hsiang Huang ,  Prafulla Kumar Choubey ,  Yilun Zhou ,  Muhao Chen ,  Jonathan May ,  and  Chien-Sheng Wu
    Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026
  4. ICML
    Optimizing Diversity and Quality through Base-Aligned Model Collaboration
    Yichen Wang ,  Chenghao Yang ,  Tenghao Huang ,  Muhao Chen ,  Jonathan May ,  and  Mina Lee
    Proceedings of the 43rd International Conference on Machine Learning (ICML), 2026
  5. EMNLP
    Teaching Language Models To Gather Information Proactively
    Tenghao Huang ,  Sihao Chen ,  Muhao Chen ,  Jonathan May ,  Longqi Yang ,  Mengting Wan ,  and  Pei Zhou
    Findings of the Association for Computational Linguistics: EMNLP, 2025
  6. ACL
    R2D2: Remembering, Replaying and Dynamic Decision Making with a Reflective Agentic Memory
    Tenghao Huang ,  Kinjal Basu ,  Ibrahim Abdelaziz ,  Pavan Kapanipathi ,  Jonathan May ,  and  Muhao Chen
    Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL), 2025
  7. EMNLP
    Are Large Language Models Capable of Generating Human-Level Narratives?
    Yufei Tian* ,  Tenghao Huang* ,  Miri Liu ,  Derek Jiang ,  Alexander Spangher ,  Muhao Chen ,  Jonathan May ,  and  Nanyun Peng
    Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024
    Outstanding Paper Award