Howdy! Iām Weijia Zhang
I am an incoming M.S. student in Computer Science at Yale University (2026 - 2028), admitted to the Two-Year MS Degree with Full Scholarship.
I graduated from UIUC in Math + Computer Science with C.W. Gear Outstanding Undergraduate Student award (2 people per year).
Currently, I worked as a research assistant in UIUC U Lab on LLM agents, multimodal agents, and agentic RL, advised by Prof. Jiaxuan You.
News
- 2026.05: šš Graduated from UIUC in Math + Computer Science with the C.W. Gear Outstanding Undergraduate Student award! š
- 2026.01: šš Paper on bias inheritance was accepted to ACL 2026 as an Oral! šāØ
- 2025.10: š SeeingEye was released as an arXiv preprint and is under review at EMNLP 2026! šš
- 2025.07: š Joined Microsoft Research Asia as a research intern! š¬š¼
Research
My research interests center on LLM agents, especially next-generation AI agents that bridge virtual and physical worlds through socially intelligent, tool-agnostic, and ethically grounded architectures.
- Multimodal agents: memory, reasoning, tool use, and multi-agent systems
- Conversational AI: anthropomorphism and social intelligence
- Post-training: agent SFT and RL
Gamedev
Beyond research, I am a passoinate indie game developer, feel free to check my game work on the game page. I am also willing to discuss the future of AI X Game.
Publications
Research Articles
How Much Vision Does Multimodal Reasoning Need? Vision-Stripping for Multimodal Benchmarks
Published in Under review at NeurIPS 2026, 2026
Vision-stripping for multimodal benchmarks and multimodal reasoning.
Recommended citation: Weijia Zhang, Zijia Liu, Tianyi Zhang, Ruiqi Chen, Lian Zhang, Haoru Li, Haoqi Chen, and Jiaxuan You. (2026). "How Much Vision Does Multimodal Reasoning Need? Vision-Stripping for Multimodal Benchmarks." Under review at NeurIPS 2026.
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Every Act Has Its Price: Compressed Moral Composition in Frontier LLMs
Published in Under review at EMNLP 2026, 2026
A study of compressed moral composition in frontier LLMs.
Recommended citation: Weijia Zhang, Ruiqi Chen, Yunze Xiao, and Weihao Xuan. (2026). "Every Act Has Its Price: Compressed Moral Composition in Frontier LLMs." Under review at EMNLP 2026.
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CUADebug: Diagnosing and Repairing Computer-Use Agent Failures
Published in Under review at EMNLP 2026, 2026
A framework for diagnosing and repairing computer-use agent failures with a CUA-specific error taxonomy, benchmark, and tool-augmented debugger.
Recommended citation: Weijia Zhang et al. (2026). "CUADebug: Diagnosing and Repairing Computer-Use Agent Failures." Under review at EMNLP 2026.
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SeeingEye: Agentic Information Flow Unlocks Multimodal Reasoning in Text-only LLMs
Published in arXiv preprint arXiv:2510.25092; under review at EMNLP 2026, 2025
Agentic information flow for unlocking multimodal reasoning in text-only LLMs.
Recommended citation: Weijia Zhang*, Zijia Liu*, Haoru Li*, Haoqi Chen*, and Jiaxuan You. (2025). "SeeingEye: Agentic Information Flow Unlocks Multimodal Reasoning in Text-only LLMs." arXiv preprint arXiv:2510.25092. Under review at EMNLP 2026.
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Where LLM Agents Fail and How They Can Learn From Failures
Published in arXiv preprint arXiv:2509.25370, 2025
A study of LLM agent failures and how agents can learn from failed trajectories.
Recommended citation: Kunlun Zhu, Zijia Liu, Bingxuan Li, Muxin Tian, Yingxuan Yang, Jiaxun Zhang, Pengrui Han, Qipeng Xie, Fuyang Cui, Weijia Zhang, Xiaoteng Ma, Xiaodong Yu, Gowtham Ramesh, Jialian Wu, Zicheng Liu, Pan Lu, James Zou, and Jiaxuan You. (2025). "Where LLM Agents Fail and How They Can Learn From Failures." arXiv preprint arXiv:2509.25370.
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Conference Papers
Understanding and Mitigating the Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks
Published in ACL 2026, Oral, 2026
A systematic study of how bias can be inherited through LLM-generated synthetic data and how mitigation strategies behave across tasks.
Recommended citation: Miaomiao Li, Hao Chen, Yang Wang, Tingyuan Zhu, Weijia Zhang, Kaijie Zhu, Kam-Fai Wong, and Jindong Wang. (2026). "Understanding and Mitigating the Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks." ACL 2026. Oral.
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Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory
Published in The First Workshop on AI Behavioral Science, ACM SIGKDD 2024, 2024
A simulated LLM agent society for studying emergent social contracts through Hobbesian social contract theory.
Recommended citation: Gordon Dai*, Weijia Zhang*, Jinhan Li, Siqi Yang, Srihas Rao, Arthur Caetano, and Misha Sra. (2024). "Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory." The First Workshop on AI Behavioral Science, ACM SIGKDD 2024.
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