🧑‍🔬 Xingle Xu

🏛️ Affiliations

🎓Northeastern UniversityShenyang · China2024–2026 · 7 paperscurrent🎓Jiangnan UniversityWuxi · China2016 · 2 papers🔬China Education and Research NetworkBeijing · ChinaEmployment

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. 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…
2. MoLAN: A Unified Modality-Aware Noise Dynamic Editing Framework for Multimodal Sentiment Analysis (2026 · 1 citation)
   Multimodal sentiment analysis (MSA) models often get misled by irrelevant visual or acoustic signals, such as background laughter that contradicts a speaker's mood.
3. Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning (2026)
   Parameter-efficient fine-tuning (PEFT) methods such as LoRA often match full fine-tuning (FFT) on standard benchmarks. It is less clear whether they hold up on harder tasks like reasoning and instruction-following, or under adversarial inputs. The authors address this with a theoretical analysis backed by experiments.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. 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
2. MoLAN: A Unified Modality-Aware Noise Dynamic Editing Framework for Multimodal Sentiment AnalysisFindings of the Association for Computational LinguisticsXingle Xu, YongKang Liu, Dexian Cai, et al.2026 · 1 citation
3. Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-TuningAnnual Meeting of the Association for Computational Linguistics (ACL)YongKang Liu, Xingle Xu, Ercong Nie, et al.2026
4. RATION: Entropy-Driven Task-Adaptive Visual Attention Allocation Framework for Multimodal Reasoning ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetXingle Xu et al.2026

🤝 Co-authors here

11 co-authors with a page, the most papers together first.

• Daling Wang ×3
• Shi Feng ×3
• YongKang Liu ×3
• Hinrich Schuetze ×2
• Xiaocui Yang ×2
• Zijing Wang ×2
• Ercong Nie
• Peiqin Lin
• Qian Li
• Yifei Zhang
• Yiqun Zhang

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) ×2: ACL 2026 ×2
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
• Findings of the Association for Computational Linguistics: ACL 2026

🔗 Cited by

Papers of our library cite their works 3 times (of the 149,439 papers whose bibliographies we read).

Updated 1 November 2026

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