---
title: "Siheng Chen — author profile and papers — Elamur Research"
description: "Siheng Chen, Shanghai Jiao Tong University: 6 papers on Elamur Research (2025–2026), h-index 1 — affiliations, topics, co-authors and…"
canonical: "https://research.elamur.ai/free/en/authors/siheng-chen/"
---
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# Siheng Chen

Siheng Chen has 6 papers in our library, in 2025–2026, most of them on [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/); often with [WenHao Wang](https://md.research.elamur.ai/free/en/authors/wenhao-wang/), [Guangyi Liu](https://md.research.elamur.ai/free/en/authors/guangyi-liu/), [Pengxiang Zhao](https://md.research.elamur.ai/free/en/authors/pengxiang-zhao/).

6 papers in our library · 2025–2026

[OpenAlex](https://openalex.org/A5139753358) · [**h-index 1**](https://openalex.org/authors/A5139753358) · [7 works](https://openalex.org/works?filter=author.id:A5139753358) · [1 citations](https://openalex.org/works?filter=author.id:A5139753358&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 6 works in our library](https://md.research.elamur.ai/free/en/authors/siheng-chen/#papers) · [🔗 Cited 29 times in our library](https://md.research.elamur.ai/free/en/authors/siheng-chen/cited-by/) · [⭐ Paper Stars ranking: #8254](https://md.research.elamur.ai/free/en/paper-stars/page/83/#star-8254)

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

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Siheng+Chen) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Siheng+Chen) · [dblp](https://dblp.org/search?q=Siheng+Chen) · [ACL Anthology](https://aclanthology.org/search/?q=Siheng+Chen)

## Affiliations

[🎓**Shanghai Jiao Tong University** Shanghai · China 2025–2026 · 5 papers*current*](https://md.research.elamur.ai/free/en/universities/shanghai-jiao-tong-university/)

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 7 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Human-Computer Interaction · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/human-computer-interaction/) · [Computer Networks and Communications · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-networks-and-communications/) · [Computer Science Applications · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-science-applications/)

🧭 Topics 1 Research subjects and languages the work is about.[Evaluation & benchmarks · 1 work](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/)

## Works by year

### 2020s

[**2026** 5 papers](https://md.research.elamur.ai/free/en/authors/siheng-chen/year/2026/) · [**2025** 1 paper](https://md.research.elamur.ai/free/en/authors/siheng-chen/year/2025/)

## Papers

The most cited first (OpenAlex and our library).

1. **[Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation](https://md.research.elamur.ai/free/en/papers/tang-2025-synthesizing-post-training-data-llms/)** *(2025 · 19 citations)* Post-training turns base LLMs into instruction followers, but it needs high-quality instruction data. Such data is expensive to annotate, scarce, and often limited by privacy. Existing synthetic approaches such as Self-Instruct, WizardLM and Magpie usually produce instructions that are not tied to any realistic user…
2. **[LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark](https://md.research.elamur.ai/free/en/papers/liu-2026-learnact-few-shot-mobile-gui/)** *(2026 · 9 citations)* Mobile GUI agents built on large language models often fail in rare, app-specific situations that pre-training or fine-tuning cannot fully cover.
3. **[MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools](https://md.research.elamur.ai/free/en/papers/wang-2026-mcp-flow-facilitating-llm-agents/)** *(2026 · 7 citations)* LLM agents increasingly need to work with tools exposed through the Model Contextual Protocol (MCP), but even strong models handle real MCP servers poorly.
4. **[FedGUI: Benchmarking Federated GUI Agents across Heterogeneous Platforms, Devices, and Operating Systems](https://md.research.elamur.ai/free/en/papers/wang-2026-fedgui-benchmarking-federated-gui-agents/)** *(2026)* GUI agents built on vision–language models usually depend on centrally gathered, manually labelled interaction data, which is expensive and hard to scale.
5. **[TeachMaster: Generative Teaching via Code](https://md.research.elamur.ai/free/en/papers/wang-2026-teachmaster-generative-teaching-code/)** *(2026)* Producing online course videos by hand is costly and slow; typical courses cost around $30,000. Pixel-level video generators such as Sora 2 are expensive to run, hard to edit, and weak on pedagogical structure. The authors propose "Generative Teaching," in which educators act as directors who state teaching goals…
6. **[Text2Mem: A Unified Memory Operation Language for Memory Operating System](https://md.research.elamur.ai/free/en/papers/wang-2026-text2mem-unified-memory-operation-language/)** *(2026)* LLM agents that work across long, multi-session interactions need memory. Yet current memory frameworks mostly offer basic create/read/delete-style primitives. Higher-level controls such as merging, promoting, locking or expiring memories are absent or implemented differently from system to system. Text2Mem addresses…

## Co-authors here

12 of 21 co-authors with a page, the most papers together first.

- [WenHao Wang](https://md.research.elamur.ai/free/en/authors/wenhao-wang/) ×3
- [Guangyi Liu](https://md.research.elamur.ai/free/en/authors/guangyi-liu/) ×2
- [Pengxiang Zhao](https://md.research.elamur.ai/free/en/authors/pengxiang-zhao/) ×2
- [Xianghe Pang](https://md.research.elamur.ai/free/en/authors/xianghe-pang/) ×2
- [Yanfeng Wang](https://md.research.elamur.ai/free/en/authors/yanfeng-wang/) ×2
- [Chen Qian](https://md.research.elamur.ai/free/en/authors/chen-qian/)
- [Feiyu Xiong](https://md.research.elamur.ai/free/en/authors/feiyu-xiong/)
- [Hanzhang Zhou](https://md.research.elamur.ai/free/en/authors/hanzhang-zhou/)
- [Hao Wang](https://md.research.elamur.ai/free/en/authors/hao-wang/)
- [Jian Du](https://md.research.elamur.ai/free/en/authors/jian-du/)
- [Jie Zhang](https://md.research.elamur.ai/free/en/authors/jie-zhang/)
- [Rui Ye](https://md.research.elamur.ai/free/en/authors/rui-ye/)

## 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)](https://md.research.elamur.ai/free/en/venues/acl/) ×3: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ×2, [ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)
- [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/) ×3: [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) ×3

## Cited by

Papers of our library cite their works 29 times (of the 149,439 papers whose bibliographies we read); [13 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/siheng-chen/cited-by/).

Updated 1 November 2026
