Xiang Wang
Xiang Wang has 7 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning, Dialogue systems; often with An Zhang, Junfeng Fang, Sihang Li.
OpenAlex · ORCID · Wikidata · h-index 51 · 277 works · 18,639 citations OpenAlex, 2026-10-14
📄 7 works in our library · 🔗 Cited 111 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #6536
Affiliations
Fields, topics and concepts
📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 7 worksComputer Science · 7 worksArtificial Intelligence · 7 worksLanguage and Linguistics · 7 worksComputer Vision and Pattern Recognition · 3 worksInformation Systems · 1 workSignal Processing · 1 work
🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 3 worksDialogue systems · 1 workReasoning · 2 worksInformation retrieval & search · 1 work
Works by year
2020s
Papers
- Hello Again! LLM-powered Personalized Agent for Long-term Dialogue (2025 · 55 citations)
Most open-domain chatbots are built for short, single-session exchanges. Real companionship, however, requires remembering past conversations and keeping track of who the user and the bot are. The authors present LD-Agent, a model-agnostic framework that splits long-term dialogue into three separately trainable… - START: Self-taught Reasoner with Tools (2025 · 21 citations)
Large Reasoning Models (LRMs) handle multi-step reasoning well but often make mistakes in exact arithmetic, enumeration and algorithmic steps. - Search and Refine During Think: Facilitating Knowledge Refinement for Improved Retrieval-Augmented Reasoning (2025 · 18 citations)
Retrieval-augmented LLMs trained with reinforcement learning tend to reason over whole retrieved documents, which are often noisy. - Neuron-Level Sequential Editing for Large Language Models (2025 · 15 citations)
Large language models store facts that go out of date, and full retraining is expensive. Parameter-editing methods such as ROME and MEMIT work well for a single round of edits. When edits are applied one after another, however, they tend to lose earlier edits ("model forgetting") and can collapse into incoherent… - Route Sparse Autoencoder to Interpret Large Language Models (2025 · 11 citations)
Sparse autoencoders (SAEs) are a popular way to break LLM activations into interpretable, mostly monosemantic features. - SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs (2026 · 1 citation)
Modern LLMs advertise very long context windows, yet they handle long sequences of numbers poorly. Tasks such as counting, filtering, or spotting trends need exact access to every value. The authors trace this failure to attention dispersion: as inputs grow, Softmax spreads attention over many similar numeric tokens… - FusionFlow: Enabling Deep Structural Exploration for Automated Agentic Workflow Generation (2026)
Automated generators of agentic workflows, such as ADAS and AFlow, usually improve one evolving workflow through small local edits or tree search.
All works
- LightGCN ↗International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yetXiangnan He et al.2020 · 4,432 citations
- Explainable Reasoning over Knowledge Graphs for Recommendation ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetXiang Wang et al.2019 · 770 citations
- Smart Contract Vulnerability Detection using Graph Neural Network ↗International Joint Conference on Artificial Intelligence · no summary yetYuan Zhuang et al.2020 · 405 citations
- Micro Tells Macro ↗ACM International Conference on Multimedia (ACM MM) · no summary yetJingyuan Chen et al.2016 · 160 citations
- Hello Again! LLM-powered Personalized Agent for Long-term DialogueConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Hao Li, Chenghao Yang, An Zhang, et al.2025 · 55 citations
- START: Self-taught Reasoner with ToolsConference on Empirical Methods in Natural Language Processing (EMNLP)Chengpeng Li, Mingfeng Xue, Zhenru Zhang, et al.2025 · 21 citations
- Search and Refine During Think: Facilitating Knowledge Refinement for Improved Retrieval-Augmented ReasoningConference on Neural Information Processing Systems (NeurIPS)Yaorui Shi, Sihang Li, Chang Wu, et al.2025 · 18 citations
- Neuron-Level Sequential Editing for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Houcheng Jiang, Junfeng Fang, Tianyu Zhang, et al.2025 · 15 citations
- Route Sparse Autoencoder to Interpret Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Wei Shi, Sihang Li, Tao Liang, et al.2025 · 11 citations
- SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMsFindings of the Association for Computational LinguisticsJie Sun, Yu Liu, Lu Han, et al.2026 · 1 citation
- FusionFlow: Enabling Deep Structural Exploration for Automated Agentic Workflow GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Xiang Wang, Zongtao Yang, Zhuojian Hong, et al.2026
Co-authors here
- An Zhang ×3
- Junfeng Fang ×2
- Sihang Li ×2
- Tao Liang ×2
- Baolong Bi
- Beichen Zhang
- Binyuan Hui
- Bowen Yu
- Chenghao Yang
- Chengpeng Li
- Dayiheng Liu
- Hao Li
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026, ACL 2025
- Conference on Empirical Methods in Natural Language Processing (EMNLP) ×2: EMNLP 2025 ×2
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
- Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): NAACL 2025
- Conference on Neural Information Processing Systems (NeurIPS): NeurIPS 2025
- ACM International Conference on Multimedia (ACM MM)
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
- International Joint Conference on Artificial Intelligence
- Proceedings of the AAAI Conference on Artificial Intelligence
Cited by
Updated
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