Deqing Fu

profile.jpg

This is Deqing Fu and I’m a final-year PhD candidate in Computer Science at the University of Southern California (USC). My research studies the theoretical foundations of large language models and ways to interpret, align, and extend them beyond text. I’m (co-)advised by Prof. Vatsal Sharan of USC Theory Group and Prof. Robin Jia of Allegro Lab within USC NLP Group, and I’m working closely with Prof. Mahdi Soltanolkotabi. I’m currently a student researcher at Google Research, and I previously interned with the Llama team at Meta and visited the Simons Institute at UC Berkeley. Before USC, I completed my undergraduate degree in Mathematics (with honors) and my master’s in Statistics at the University of Chicago.

Availability

I am on the job market in 2026–2027. Please reach out!

Research Highlights

Algorithmic Perspectives on Large Language Models
Interpretability and Alignment
  • Decision theory for LLM reasoning under uncertainty (ICLR 2025 Spotlight, EMNLP 2026)
  • Steering vectors for improved visual understanding (ACL 2026), and for efficient and privacy-preserving synthetic data generation (ICML 2026)
  • Faithful generation under distribution shift via token-level rewards (COLM 2026)
  • Mechanistic interpretability via SAEs and transcoders (COLM 2026, Tech Report)
Foundation Models Beyond Text
  • Multimodal rewards for improving generation quality: token-level hallucination reduction (ICLR 2025) and text-to-image alignment (NAACL 2025)
  • Modality sensitivity in Multimodal LLMs (COLM 2024)
  • Large-scale dataset for visual reasoning with images (ICLR 2026)
  • Zero-shot tabular foundation models and self-evolving agentic pipelines built on them (Tech Report, Preprint)

News

All news →
Nov 05, 2026 Gave talks on Emergent Algorithms in Foundation Models: Theory, Discovery, and Design at Harvard, Boston University, Northeastern, Cornell Tech, and Columbia.
Sep 30, 2026 The TabFM technical report is now on arXiv, along with our new preprint TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models, which pairs a coding agent with a frozen TabFM and ranks #1 on TabArena!
Sep 24, 2026 Our paper VASE: Value-Aware Stochastic KV Cache Eviction for Reasoning Models is accepted to NeurIPS 2026!
Aug 29, 2026 Our paper Are LLM Decisions Faithful to Verbal Confidence? was accepted to EMNLP 2026 Main Conference!
Jul 21, 2026 New paper TOPL: Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift, accepted to COLM 2026!

Selected Publications

All publications →

2026

  1. Preprint
    TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models
    Preprint
    Deqing Fu, Huangyuan Su, Rajat Sen, Taman Narayan, Sujay Sanghavi, Abhimanyu Das, and Weihao Kong
    2026
    Paper Website
  2. Tech Report
    TabFM: A Zero-shot Foundation Model for Tabular Data
    Tech Report
    Weihao Kong, Erez Louidor Ilan, Shuxin Nie, Taman Narayan, Rajat Sen , Yichen Zhou, Deqing Fu, Samet Oymak, and Abhimanyu Das
    2026
    Paper Blog
  3. ICML
    graph.png
    Transformers Provably Learn Algorithmic Solutions for Graph Connectivity, But Only with the Right Data
    ICML
    Qilin Ye*, Deqing Fu*, Robin Jia, and Vatsal Sharan
    In International Conference on Machine Learning (ICML), 2026
    Paper PDF
  4. NeurIPS
    vase.png
    Value-Aware Stochastic KV Cache Eviction for Reasoning Models
    NeurIPS
    Ting-Yun Chang* , Harvey Yiyun Fu*, Deqing Fu*, Chenghao Yang, Jesse Thomason, and Robin Jia
    In Conference on Neural Information Processing Systems (NeurIPS), 2026
    Paper Code
  5. COLM
    convergent.png
    Convergent Evolution: How Different Language Models Learn Similar Number Representations
    COLM
    Deqing Fu, Tianyi Zhou, Mikhail Belkin, Vatsal Sharan, and Robin Jia
    In Conference on Language Modeling (COLM), 2026
    Paper Blog Website
  6. ICLR
    zebra-cot.png
    Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning
    ICLR
    Ang Li*, Charles Wang*, Deqing Fu*, Kaiyu Yue*, Zikui Cai*, Wang Bill Zhu*, Ollie Liu* , Peng Guo*, Willie Neiswanger, Furong Huang, Tom Goldstein, and Micah Goldblum
    In International Conference on Learning Representations (ICLR), 2026
    Paper Dataset
  7. ICLR
    fone.png
    FoNE: Precise Single-Token Number Embeddings via Fourier Features
    ICLR
    Tianyi Zhou, Deqing Fu, Mahdi Soltanolkotabi, Robin Jia, and Vatsal Sharan
    In International Conference on Learning Representations (ICLR), 2026
    Paper Website
  8. ACL
    saegull.png
    Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models
    ACL
    Woody Haosheng Gan*, Deqing Fu*, Julian Asilis*, Ollie Liu*, Vatsal Sharan, Robin Jia, and Willie Neiswanger
    In Association for Computational Linguistics (ACL), 2026
    Paper Blog

2025

  1. ICLR
    tldr.png
    TLDR: Token-Level Detective Reward Model for Large Vision Language Models
    ICLR
    Deqing Fu, Tong Xiao , Rui Wang, Wang Zhu, Pengchuan Zhang, Guan Pang, Robin Jia, and Lawrence Chen
    In International Conference on Learning Representations (ICLR), 2025
    Paper
  2. ICLR
    sensitivity.png
    Transformers Learn Low Sensitivity Functions: Investigations and Implications
    ICLR
    Bhavya Vasudeva*, Deqing Fu*, Tianyi Zhou, Elliot Kau , You-Qi Huang, and Vatsal Sharan
    In International Conference on Learning Representations (ICLR), 2025
    Paper
  3. ICLR
    dellma.png
    DeLLMa: Decision Making Under Uncertainty with Large Language Models
    ICLR
    Ollie Liu*, Deqing Fu*, Dani Yogatama, and Willie Neiswanger
    In International Conference on Learning Representations (ICLR), 2025
    Spotlight (Top 5.1%)
    Paper Code Website
  4. NAACL
    dreamsync.png
    DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback
    NAACL
    Jiao Sun*, Deqing Fu*, Yushi Hu* , Su Wang, Royi Rassin, Da-Cheng Juan, Dana Alon, Charles Herrmann, Sjoerd Steenkiste, Ranjay Krishna, and Cyrus Rashtchian
    In Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL), 2025
    Paper

2024

  1. NeurIPS
    transformer-icl.png
    Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression
    NeurIPS
    Deqing Fu, Tian-Qi Chen, Robin Jia, and Vatsal Sharan
    In Conference on Neural Information Processing Systems (NeurIPS), 2024
    SoCalNLP Symposium 2023 Best Paper Award
    Paper Code
  2. NeurIPS
    fourier.png
    Pre-trained Large Language Models Use Fourier Features to Compute Addition
    NeurIPS
    Tianyi Zhou, Deqing Fu, Vatsal Sharan, and Robin Jia
    In Conference on Neural Information Processing Systems (NeurIPS), 2024
    Paper
  3. COLM
    isobench.png
    IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations
    COLM
    Deqing Fu*, Ruohao Guo*, Ghazal Khalighinejad*, Ollie Liu*, Bhuwan Dhingra, Dani Yogatama, Robin Jia, and Willie Neiswanger
    In Conference on Language Modeling (COLM), 2024
    Paper Website

* Equal contribution.

Teaching and Mentorship

Teaching

  • Introduction to Machine Learning Spring 2025 CSCI 467 · Teaching Assistant · USC
  • Deep Learning and its Applications Spring 2023 CSCI 566 · Teaching Assistant · USC
  • Statistical Insight Fall 2021 BUSN 41301 · Teaching Assistant · UChicago Booth
  • Fundamentals of Deep Learning Fall 2021 CMSC 31230 · Course Assistant · UChicago
  • Analysis in Rn I Spring 2018 MATH 20300 · Course Assistant · UChicago

Mentorship