Papers by Hong Wan
Taming "Zombie" Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution (2026.acl-long)
Copied to clipboard
Taolin Zhang, Pukun Zhao, Qizhou Chen, Jiuheng Wan, Chen Chen, Xiaofeng He, Chengyu Wang, Richang Hong
| Challenge: | Existing approaches to improve efficiency of multi-agent systems rely on aggressive graph topology evolution . however, such hard pruning overlooks the potential for "zombie" agents to recover and contribute in subsequent discussion rounds. |
| Approach: | They propose a Markov state-aware framework for resilient multi-agent evolution that manages agent collaboration through soft state transitions. |
| Outcome: | The proposed framework outperforms baselines and significantly reduces token consumption through state-aware agent scheduling. |
LUME: LLM Unlearning with Multitask Evaluations (2025.findings-emnlp)
Copied to clipboard
Anil Ramakrishna, Yixin Wan, Xiaomeng Jin, Kai-Wei Chang, Zhiqi Bu, Bhanukiran Vinzamuri, Volkan Cevher, Mingyi Hong, Rahul Gupta
| Challenge: | Unlearning aims to remove copyrighted, sensitive, or private content from large language models without a full retraining. |
| Approach: | They propose a multi-task unlearning benchmark LUME that unlearns short novels, biographies and public biographie . |
| Outcome: | The proposed benchmark unlearns short novels, biographies and public biographie . it also releases fine-tuned models with 1B and 7B parameter sizes as targets . |
PrinciplismQA: A Philosophy-Grounded Approach to Assessing LLM-Human Clinical Medical Ethics Alignment (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks lack systematic approaches to integrate philosophical frameworks and expert validation for ethical reasoning assessment. |
| Approach: | They propose a philosophy-grounded approach to assess medical ethics alignment . PrinciplismQA comprises 3,648 expert-validated questions spanning knowledge assessment and clinical reasoning . |
| Outcome: | PrinciplismQA provides a philosophy-grounded approach to assessing medical ethics alignment. |
Fix-Filter-Fix: Intuitively Connect Any Models for Effective Bug Fixing (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches for bug fixing lack generality and use only textual or structured information. |
| Approach: | They propose an intuitive yet effective general framework called Fix-Filter-Fix for bug fixing that connects models with their filter mechanism to filter out the last model’s unchanged fix to the next. |
| Outcome: | The proposed framework can quantify and accurately calculate the lifting effect of the model. |
Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards (2026.acl-long)
Copied to clipboard
Youliang Yuan, Qiuyang Mang, Jingbang Chen, Hong Wan, Xiaoyuan Liu, Junjielong Xu, Jen-tse Huang, Wenxuan Wang, Wenxiang Jiao, Pinjia He
| Challenge: | Existing models are susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning ability. |
| Approach: | They propose a Rubric Reward Model that rewards the entire reasoning trajectory against problem-specific rubrics. |
| Outcome: | The proposed model outperforms outcome-only supervision on four math benchmarks and boosts Verified Pass@1024 from 26.7% to 62.6% and reduces the incidence of Miracle Steps by 71%. |
AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering (2026.acl-long)
Copied to clipboard
Taolin Zhang, Dongyang Li, Chen Chen, Qizhou Chen, Jiuheng Wan, Xiaofeng He, Chengyu Wang, Richang Hong
| Challenge: | Empirical results show that AMATA outperforms baseline approaches, knowledge-augmented frameworks, and LLMs on knowledge-intensive QA benchmarks. |
| Approach: | They propose an Adaptive Multi-Agent Trajectory Alignment framework that integrates external knowledge to improve response interpretability and factual grounding. |
| Outcome: | The proposed framework outperforms baseline approaches, knowledge-augmented frameworks, and LLM-based trajectory systems on five established knowledge-intensive QA benchmarks. |
Disentangled Code Representation Learning for Multiple Programming Languages (2021.findings-acl)
Copied to clipboard
| Challenge: | Developing effective distributed representations of source code is challenging . current code embedding approaches that represent the semantic and syntax of code are less interpretable . |
| Approach: | They propose a disentangled code representation learning approach to separate the semantic from the syntax of source code under a multi-programming-language setting. |
| Outcome: | The proposed approach achieves better interpretability and generalizability over existing methods. |
Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition (2022.naacl-main)
Copied to clipboard
| Challenge: | a novel AI-empowered chat bot for learning as conversation can be applied to various domains without in-domain dialogue data. |
| Approach: | They propose a novel task where a user does not read a passage but gains information and knowledge through conversation with a teacher bot. |
| Outcome: | The proposed system can be transferred to various domains without in-domain dialogue data and can carry out conversations both informative and attentive to users. |
LAMB: A Training-Free Method to Enhance the Long-Context Understanding of SSMs via Attention-Guided Token Filtering (2025.acl-short)
Copied to clipboard
Zhifan Ye, Zheng Wang, Kejing Xia, Jihoon Hong, Leshu Li, Lexington Whalen, Cheng Wan, Yonggan Fu, Yingyan Celine Lin, Souvik Kundu
| Challenge: | Recent work attributes performance degradation to an exponential decay in hidden-state memory. |
| Approach: | They propose a token filtering strategy that is training-free and attention-guided . they propose 'LAMB' to preserve critical tokens during inference . |
| Outcome: | The proposed token filtering improves long-context performance by 30.35% over state-of-the-art methods on benchmarks. |