🧑‍🔬 Yiqun Zhang

🏛️ Affiliations

🎓Northeastern UniversityShenyang · China2026 · 4 paperscurrent🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2026 · 3 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Nature-Inspired Population-Based Evolution of Large Language Models (2026 · 8 citations)
   The paper frames a new problem: how to improve a whole group of large language models by treating it as an evolving population rather than tuning one model.
2. Why Do More Experts Fail? A Theoretical Analysis of Model Merging (2026 · 2 citations)
   Merging several fine-tuned expert models into one multi-task model saves storage and compute. However, adding more experts stops helping quickly: state-of-the-art methods such as GENOME level off at around six merged models on most datasets. This paper asks why that ceiling exists. Using high-dimensional geometry…
3. DeepMed: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & Inference (2026 · 1 citation)
   General-purpose DeepResearch (DR) agents, which interleave web search with reasoning, improve only modestly when moved to medicine.
4. SAD: A Large-Scale Strategic Argumentative Dialogue Dataset (2026)
   Most argument-generation corpora treat argumentation as a one-shot task: produce an argument for a topic or rebut a single claim.

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

1. Nature-Inspired Population-Based Evolution of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yiqun Zhang, Peng Ye, Xiaocui Yang, et al.2026 · 8 citations
2. Why Do More Experts Fail? A Theoretical Analysis of Model MergingAnnual Meeting of the Association for Computational Linguistics (ACL)Zijing Wang, Xingle Xu, YongKang Liu, et al.2026 · 2 citations
3. DeepMed: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & InferenceFindings of the Association for Computational LinguisticsZihan Wang, Hao Wang, Shi Feng, et al.2026 · 1 citation
4. SAD: A Large-Scale Strategic Argumentative Dialogue DatasetAnnual Meeting of the Association for Computational Linguistics (ACL)YongKang Liu, Jiayang Yu, Mingyang Wang, et al.2026
5. MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetYiqun Zhang et al.2026

🤝 Co-authors here

12 of 20 co-authors with a page, the most papers together first.

• Shi Feng ×4
• Daling Wang ×3
• Xiaocui Yang ×3
• Hinrich Schuetze ×2
• YongKang Liu ×2
• Ercong Nie
• Haihua Yang
• Hao Wang
• Jinghao Lin
• Kaisong Song
• Lei Bai
• Mingyang Wang

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Annual Meeting of the Association for Computational Linguistics (ACL) ×4: ACL 2026 ×3
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026

🔗 Cited by

Papers of our library cite their works 10 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated 1 November 2026

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