CV
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Basics
| Name | Shouren Wang |
| Label | PhD Student in Computer Science |
| 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
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2024.08 - Present Cleveland, OH
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2021.09 - 2023.05 Brooklyn, NY
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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
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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)
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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
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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
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2026 Path-Lock Expert: Separating Reasoning Mode in Hybrid Thinking via Architecture-Level Separation
COLM 2026
Shouren Wang*, Wang Yang*, Chuang Ma, Debargha Ganguly, Vikash Singh, Chaoda Song, Xinpeng Li, Xianxuan Long, Vipin Chaudhary†, Xiaotian Han†
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2026 Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?
EMNLP 2026 Main
Shouren Wang*, Wang Yang*, Xianxuan Long, Qifan Wang, Vipin Chaudhary†, Xiaotian Han†
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2026 Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers
ACL 2026 Findings
Wang Yang*, Shouren Wang*, Debargha Ganguly, Xinpeng Li, Chaoda Song, Vikash Singh, Vipin Chaudhary†, Xiaotian Han†
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2026 A Survey on Efficient Protein Language Models
TMLR 2026
Shouren Wang, Debargha Ganguly, Vinooth Rao Kulkarni, Wang Yang, Zhuoran Qiao, Daniel Blankenberg, Vipin Chaudhary†, Xiaotian Han†
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2026 When Domains Interact: Asymmetric and Order-Sensitive Cross-Domain Effects in Reinforcement Learning for Reasoning
Under Review
Wang Yang, Shouren Wang, Chaoda Song, Chuang Ma, Xinpeng Li, Nengbo Wang, Kaixiong Zhou, Vipin Chaudhary†, Xiaotian Han†
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2026 A Survey on Agent Skills for LLMs: A Lifecycle Perspective from Construction to Ecosystems
Under Review
Wang Yang, Chaoda Song, Xinpeng Li, Shouren Wang, Nengbo Wang, Yanyan Zhang, Chuang Ma, Debargha Ganguly, Vikash Singh, Shuai Xu, Jing Ma, Yu Yin, Vipin Chaudhary†, Xiaotian Han†
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2026 AgentCE-Bench: Agent Configurable Evaluation with Scalable Horizons and Controllable Difficulty under Lightweight Environments
Under Review
Wang Yang, Chaoda Song, Xinpeng Li, Debargha Ganguly, Chuang Ma, Shouren Wang, Zhihao Dou, Yuli Zhou, Vipin Chaudhary†, Xiaotian Han†
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2026 MemTrace: Probing What Final Accuracy Misses in Long-Term Memory
Under Review
Xianxuan Long, Zhikai Chen, Shenglai Zeng, Shouren Wang, Kai Guo, Jiliang Tang†
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2026 Reliability-Gated Source Anchoring for Continual Test-Time Adaptation
Under Review
Vikash Singh, Debargha Ganguly, Weicong Chen, Sabyasachi Sahoo, Sreehari Sankar, Biyao Zhang, Mohsen Hariri, Shouren Wang, Osama Zafar, Christian Gagné, Vipin Chaudhary†
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2026 CausalGuard: Conformal Inference under Graph Uncertainty
Under Review
Vikash Singh, Weicong Chen, Debargha Ganguly, Yanyan Zhang, Nengbo Wang, Sreehari Sankar, Mohsen Hariri, Alexander Nemecek, Chaoda Song, Shouren Wang, Biyao Zhang, Van Yang, Erman Ayday, Jing Ma, Vipin Chaudhary†
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2026 Enhancing Player Enjoyment with a Two-Tier DRL and LLM-Based Agent System for Fighting Games
Under Review
Shouren Wang, Zehua Jiang, Fernando Sliva, Sam Earle, Julian Togelius†
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 |