Houcheng Jiang
Houcheng Jiang is a researcher on model editing and safety for large language models and a master's student at the University of Science and Technology of China. After beginning his master's degree and joining the university's Lab for Data Science in 2023, he coauthored AlphaEdit in October 2024, which won an Outstanding Paper Award at ICLR 2025. As first author, he wrote Neuron-Level Sequential Editing for ACL 2025, AnyEdit for ICML 2025, DualEdit for ICLR 2026, and a 2025 paper on contrastive weak-to-strong generalization. In February 2026, he coauthored the NExT-Guard streaming safeguard, and he continues his research at the Lab for Data Science.
Timeline
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October 2024
The AlphaEdit paper
In October 2024, he coauthors AlphaEdit with Junfeng Fang, Xiang Wang, Xiangnan He, Tat-Seng Chua and others. The method projects the perturbation of a model edit onto the null space of preserved knowledge, and it raises the performance of most locating-then-editing methods by an average of 36.7% on LLaMA3, GPT2-XL and GPT-J.
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2024–2025
Neuron-level sequential editing
He is first author of Neuron-Level Sequential Editing (NSE), posted on arXiv in October 2024. The method optimizes the target layer's hidden states and selects neurons by activation value to limit failure and forgetting in multi-round model editing, and it appears in the ACL 2025 proceedings in Vienna in July 2025.
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February 2025
AnyEdit for long-form knowledge
In February 2025, he is first author of AnyEdit, an autoregressive method that splits long-form knowledge such as poetry, code snippets and mathematical derivations into chunks and edits the key token in each one. It outperforms strong baselines by 21.5% on benchmarks including UnKEBench, AKEW and the new EditEverything dataset, and it appears at ICML 2025.
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2025
An ICLR 2025 Outstanding Paper
In 2025, AlphaEdit wins one of three Outstanding Paper Awards at ICLR 2025, selected from 3,704 submissions.
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2025–2026
DualEdit on backdoor editing
In June 2025, he posts DualEdit as first author. The framework addresses safety fallback in model-editing backdoors, where an edited model starts with an affirmative prefix and later reverts to refusals, and it improves attack success by 10% and reduces the fallback rate by 11% over baselines on safety-aligned LLMs. The paper is an ICLR 2026 poster in April 2026.
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October 2025–July 2026
Contrastive weak-to-strong generalization
In October 2025, he posts Contrastive Weak-to-strong Generalization on arXiv as first author with Junfeng Fang, and revises the paper in July 2026.
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February 2026
Streaming safeguards with NExT-Guard
In February 2026, he coauthors NExT-Guard: Training-Free Streaming Safeguard without Token-Level Labels with Junfeng Fang and Nachuan Chen, introducing a training-free streaming safeguard that operates without token-level labels.
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Since 2023
Master's studies at USTC
He begins a master's degree at the University of Science and Technology of China in 2023 and joins its Lab for Data Science (LDS). He is a master's student and researcher at the university focusing on model editing and large language models.
Career
- Since 2023University of Science and Technology of ChinaMember
Education
- From 2023University of Science and Technology of ChinaMaster's
Sources and credits
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- [2410.02355] AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models
- [2410.04045] Neuron-Level Sequential Editing for Large Language Models
- Neuron-Level Sequential Editing for Large Language Models - ACL Anthology
- [2502.05628] AnyEdit: Edit Any Knowledge Encoded in Language Models
- GitHub - jianghoucheng/AnyEdit: AnyEdit: Edit Any Knowledge Encoded in Language Models, ICML 2025
- Announcing the Outstanding Paper Awards at ICLR 2025 – ICLR Blog
- Xiang Wang | Professor, USTC
- [2506.13285] DualEdit: Mitigating Safety Fallback in LLM Backdoor Editing via Affirmation-Refusal Regulation
- ICLR Poster DualEdit: Mitigating Safety Fallback in LLM Backdoor Editing via Affirmation-Refusal Regulation
- [2510.07884] Contrastive Weak-to-strong Generalization
- [2603.02219] NExT-Guard: Training-Free Streaming Safeguard without Token-Level Labels
- Houcheng Jiang on alphaXiv
