Xiaofeng He
Xiaofeng He has 4 papers in our library, in 2025–2026, most of them on Question answering, Language models & pretraining, Reasoning; often with Chengyu Wang, Qizhou Chen, Taolin Zhang.
OpenAlex · ORCID · h-index 32 · 156 works · 4,947 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 22 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #11240
Affiliations
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 1 work
🧭 Topics 3Research subjects and languages the work is about.Question answering · 2 worksReasoning · 1 workLanguage models & pretraining · 1 work
Works by year
2020s
Papers
- BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question Answering (2025 · 9 citations)
Multi-hop question answering requires collecting several pieces of evidence and chaining them together, but existing LLM-based approaches usually apply one fixed strategy to every question. - AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering (2026)
LLMs answering knowledge-intensive questions often hallucinate and struggle with long-tail facts. Existing multi-agent QA systems are trained in one of three ways: module by module, end to end, or in a global-local scheme. None of these adapts which agents run for a given question or models how agents depend on each… - QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs (2026)
Large language models still produce factual errors. Model editing can patch individual facts, but in sequential model editing (SME), where corrections arrive one after another, earlier edits may conflict with new ones and the model's general abilities can degrade. The authors propose QueueEDIT, which combines two… - Taming “Zombie” Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution (2026)
LLM-based multi-agent systems often save compute by pruning agents or links from the communication graph. These cuts are permanent. An agent that fails for a moment, through a hallucination or a knowledge gap, is removed even if it could have helped in later rounds. The authors call such agents "zombie" agents. Their…
All works
- A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances ↗Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · no summary yetChengyu Wang, Xiaofeng He, Aoying Zhou2017 · 87 citations
- BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question AnsweringAnnual Meeting of the Association for Computational Linguistics (ACL)Taolin Zhang, Dongyang Li, Qizhou Chen, et al.2025 · 9 citations
- AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question AnsweringAnnual Meeting of the Association for Computational Linguistics (ACL)Taolin Zhang, Dongyang Li, Chen Chen, et al.2026
- QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMsFindings of the Association for Computational LinguisticsTaolin Zhang, Haidong Kang, Dongyang Li, et al.2026
- Taming “Zombie” Agents: A Markov State-Aware Framework for Resilient Multi-Agent EvolutionAnnual Meeting of the Association for Computational Linguistics (ACL)Taolin Zhang, Pukun Zhao, Qizhou Chen, et al.2026
Co-authors here
- Chengyu Wang ×4
- Qizhou Chen ×4
- Taolin Zhang ×4
- Dongyang Li ×3
- Richang Hong ×3
- Chen Chen ×2
- Jiuheng Wan ×2
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×3: ACL 2026 ×2, ACL 2025
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Cited by
Updated
This page in Markdown → https://md.research.elamur.ai/free/en/authors/xiaofeng-he/