🧑‍🔬 Daling Wang

🏛️ Affiliations

🎓Northeastern UniversityShenyang · China2000–2026 · 177 paperscurrent🎓Northeastern UniversityBoston · United States2009–2026 · 28 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

🧭 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. 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. 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.
5. 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.
6. 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.
7. Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation (2026 · 1 citation)
   The paper tackles multimodal sentiment analysis of text-image posts when only a small model can be deployed.
8. CIRAG: Construction–Integration Retrieval and Adaptive Generation for Multi-hop Question Answering (2026)
   Triple-based iterative RAG for multi-hop QA has two weaknesses. It keeps only one best triple per step, so early mistakes compound and parallel evidence is lost. It also uses a single evidence granularity, which can be too noisy or too sparse depending on the question. CIRAG draws on Kintsch's…
9. GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing (2026)
   Psychological profiling of persona-conditioned LLM agents (PC-Agents) usually relies on self-report questionnaires such as BFI, BDI or MBTI.
10. 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.
11. 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

15 works of theirs in our library, the most cited first; 11 with a page here, the others link their original until we summarise them.

1. Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks ↗Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yetXiaocui Yang et al.2021 · 147 citations
2. A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness ↗Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · no summary yetXiangju Li et al.2018 · 140 citations
3. SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Peidong Wang, Zhiming Ma, Xin Dai, et al.2026 · 3 citations
4. 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
5. 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
6. 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
7. NEAT: Neuron-Based Early Exit for Large Reasoning ModelsFindings of the Association for Computational LinguisticsKang Liu, YongKang Liu, Xiaocui Yang, et al.2026 · 1 citation
8. PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMsFindings of the Association for Computational LinguisticsZijing Wang, YongKang Liu, Mingyang Wang, et al.2026 · 1 citation
9. Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and DistillationFindings of the Association for Computational LinguisticsHaonan Shangguan, Xiaocui Yang, Shi Feng, et al.2026 · 1 citation
10. CIRAG: Construction–Integration Retrieval and Adaptive Generation for Multi-hop Question AnsweringAnnual Meeting of the Association for Computational Linguistics (ACL)Zili Wei, Yilin Wang, Xiaocui Yang, et al.2026
11. GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective TestingAnnual Meeting of the Association for Computational Linguistics (ACL)Ming Wang, Shuang Wu, Bixuan Wang, et al.2026
12. 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
13. 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
14. 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
15. 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 26 co-authors with a page, the most papers together first.

• Shi Feng ×11
• Xiaocui Yang ×9
• YongKang Liu ×7
• Yifei Zhang ×6
• Hinrich Schuetze ×4
• Ercong Nie ×3
• Xingle Xu ×3
• Yiqun Zhang ×3
• Zijing Wang ×3
• Mingyang Wang ×2
• Peidong Wang ×2
• Zihan Wang ×2

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) ×7: ACL 2026 ×6
• Findings of the Association for Computational Linguistics ×5: Findings of the Association for Computational Linguistics 2026 ×5
• Findings of the Association for Computational Linguistics: ACL 2026
• Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
• Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

🔗 Cited by

Papers of our library cite their works 47 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated 1 November 2026

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