CV

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Basics

Name Shouren Wang
Label PhD Student in Computer Science
Email sxw992@case.edu
Phone +1 (929) 302-1790
Url https://sr-a-w.github.io
Summary PhD student at Case Western Reserve University working on large language model reasoning, post-training, efficient language models, and AI agent systems.

Education

  • 2024.08 - Present

    Cleveland, OH

    Ph.D.
    Case Western Reserve University
    Computer Science
  • 2021.09 - 2023.05

    Brooklyn, NY

    M.S.
    New York University
    Computer Engineering
    • GPA: 3.93/4.0
  • 2015.09 - 2019.06

    Changsha, China

    Undergraduate and B.E.
    Hunan University
    Software Engineering (2015–2017); Digital Media Technology, a specialization of Software Engineering focusing on visual software and algorithm development (2017–2019)
    • GPA: 83.16/100.0

Work

  • 2024.09 - 2026.05

    Cleveland, OH

    Teaching Assistant
    Case Western Reserve University
    Teaching assistant for Large Language Models and Database Systems: graded assignments and held office hours.
    • Professors: Xiaotian Han (Large Language Models); Yinghui Wu (Database Systems)
  • 2023.08 - 2024.07

    Brooklyn, NY

    Research Intern
    NYU Game Innovation Lab
    Explored research and methods for creative AI for video games.
    • Developed enjoyment-focused game-playing agents for Street Fighter 2 via an extended PPO model and self-play
    • Advisor: Julian Togelius, Professor, New York University
  • 2019.09 - 2020.09

    Nanjing, China

    Research and Development Engineer
    AsiaInfo Technology
    Tested and enhanced the performance of the CTDI project as a QA group member, contributing to project efficiency.
    • Manager: Ted Wang

Projects

  • 2026.04 - Present
    Agent Team Work Zone
    LLM Agents · Claude Code · Multi-Agent Workflow
    • Built and maintain an open-source persistence layer for Claude Code's Agent Teams, whose teammates vanish on session restart with no native recovery
    • Designed per-agent workstations (role, working context, checkpoints, commitments) and a team registry, so one command rebuilds an interrupted team from disk
    • Shipped 13 slash-command skills and hook-driven auto-checkpointing across four public releases, maintaining parallel editions through a breaking Claude Code API change
    • Dogfood the layer end-to-end: the agent team that develops it runs on it
  • 2026.01 - 2026.07
    Path-Lock Expert: Efficient Hybrid Thinking Architecture
    LLMs · Reasoning
    • Accepted at COLM 2026
    • Developed a parameter-efficient architecture approach to improve hybrid-thinking mode controllability (thinking vs. fast) without compromising overall task performance, together with an efficient training pipeline using curated data
    • Experiments on AIME24, MATH500, and GPQA-Diamond indicate improved mode separation and controllability with competitive accuracy, and greatly improved accuracy for no-think mode
  • 2025.09 - 2026.02
    Demystifying Hybrid Thinking
    LLMs · Reasoning · Post-Training
    • Accepted at EMNLP 2026 Main
    • Systematically analyzed the controllability of hybrid-thinking LLMs (think vs. no-think), showing incomplete mode separation and quantifying reasoning leakage via output length and reflection-token counts across MATH500, AIME24, GPQA, and MMLU-STEM
    • Conducted controlled SFT ablations on data scale (20k–140k), pairing strategy, think:no-think ratio, and two-phase training; proposed a practical recipe that preserves accuracy while cutting no-think verbosity (1085→585 tokens on MATH500) and reflection-token frequency (5917→522)
  • 2025.11 - 2026.01
    Mid-Think: Token-Level Reasoning Budget Control
    LLMs · Reasoning · RL
    • Accepted at ACL 2026 Findings
    • Identified via attention analysis that hybrid-thinking mode switching is dominated by a few token-level triggers—‘Okay’ activates reasoning while </think>\n\n suppresses it—rather than high-level Think/No-think instructions
    • Proposed Mid-Think, a training-free prompting format combining both triggers to achieve Pareto-optimal accuracy–length trade-offs (MATH500: 92.1% accuracy at 2589 tokens vs. 94.4% at 4904), outperforming fixed-token and prompt-based baselines across Qwen3-4B/8B/14B/32B
    • Applied Mid-Think to GRPO-based RL after SFT, cutting training time by about 15% (54h→46h) while improving Qwen3-8B on AIME (69.8%→72.4%) and GPQA (58.5%→61.1%)

Publications

Skills

Languages
Python
Java
MATLAB
Frameworks
PyTorch
Transformers
vLLM
Llama-Factory
Stable-Baselines3
OpenAI Gym
Scikit-Learn
Developer Tools
Git
Slurm
Anaconda
Docker
Singularity
Linux
VS Code
AI Development Tools
Claude Code
OpenClaw
Codex
Cursor