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Siyuan Wang

Siyuan Wang has 5 papers in our library, in 2025–2026, most of them on Reasoning, Evaluation & benchmarks, Vision & language; often with Zhongyu Wei, Chenglin Li, Dianyi Wang.

5 papers in our library · 2025–2026

OpenAlex · h-index 0 · 3 works · 0 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 39 times in our library · ⭐ Paper Stars ranking: #8026

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of Southern CaliforniaLos Angeles · United States2026 · 1 papercurrent

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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 5 worksArtificial Intelligence · 5 worksArts and Humanities · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 3 worksHuman-Computer Interaction · 1 work

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 1 workReasoning · 2 worksVision & language · 1 work

 Works by year

2020s

20264 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation (2025 · 30 citations)
    Static benchmarks age quickly as LLMs improve. Their test sets may also leak into training data, which inflates scores. Rather than annotating new datasets from scratch, the authors update existing ones automatically. Their framework has three goals:
  2. Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better (2026 · 7 citations)
    Most large vision-language models (LVLMs) such as LLaVA receive training signal only on their text answers.
  3. Interleaved Latent Visual Reasoning with Selective Perceptual Modeling (2026 · 5 citations)
    Multimodal LLMs can reason better when they interleave intermediate images with their textual chain of thought.
  4. MAGNET: Towards Adaptive GUI Agents with Memory-Driven Knowledge Evolution (2026)
    Mobile GUI agents built on multimodal LLMs tend to break when apps are updated. Icons get redesigned ("appearance drift") and navigation paths get reorganized ("workflow drift"), so models trained on older interface snapshots stop working. The authors observe that what an element does, and what a user wants to…
  5. Simple-VGC: Enhancing Visual Grounding in Multimodal Reasoning via Adaptive Tool Composition (2026)
    Multimodal large language models (MLLMs) often lose track of visual evidence while reasoning. The authors identify three distinct grounding failures. Long-Context Grounding Error occurs when the image's influence fades over long outputs. Fine-Grained Grounding Error occurs when low resolution hides details. Regional…

 Co-authors here

12 of 15 co-authors with a page, the most papers together first.

Venues

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

 Cited by

Papers of our library cite their works 39 times (of the 149,439 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated

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