🧑‍🔬 YongKang Liu

🏛️ Affiliations

🎓Northeastern UniversityShenyang · China2026 · 6 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. SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning (2026 · 3 citations)
   Most phone-scam detectors work on ASR transcripts. This means recognition errors carry through to the decision, and acoustic signals such as tone, stress or staged background noise are lost. SAFE-QAQ addresses this by running a large audio-language model (LALM) directly on raw call audio, so no transcription step is…
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. 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.
4. NEAT: Neuron-Based Early Exit for Large Reasoning Models (2026 · 1 citation)
   Large Reasoning Models frequently keep producing reasoning after they have already reached a correct solution.
5. PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMs (2026 · 1 citation)
   Multimodal instruction fine-tuning tends to weaken the text reasoning that MLLMs inherit from their base language models.
6. 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.
7. 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.

🤝 Co-authors here

12 of 17 co-authors with a page, the most papers together first.

• Daling Wang ×7
• Shi Feng ×7
• Xiaocui Yang ×5
• Hinrich Schuetze ×4
• Ercong Nie ×3
• Xingle Xu ×3
• Yifei Zhang ×3
• Zijing Wang ×3
• Mingyang Wang ×2
• Peidong Wang ×2
• Yiqun Zhang ×2
• Kaisong Song

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 ×4
• Findings of the Association for Computational Linguistics ×3: Findings of the Association for Computational Linguistics 2026 ×3

🔗 Cited by

Papers of our library cite their works 7 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated 1 November 2026

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