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Changran Hu

Changran Hu has 3 papers in our library, in 2023–2025, most of them on Language models & pretraining, Reasoning; often with Fenglu Hong, Urmish Thakker, Bo Li.

3 papers in our library · 2023–2025

OpenAlex · h-index 4 · 13 works · 131 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 129 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7591

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

🏢xAISan Francisco2026 · 1 papercurrent🎓Tsinghua UniversityBeijing · China2018–2021 · 3 papers🎓University of California, BerkeleyBerkeley · United States2021–2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 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 · 3 worksArts and Humanities · 2 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 2 worksInformation Systems · 1 workComputational Theory and Mathematics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 1 work

 Works by year

2020s

20251 paper20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. On the Tool Manipulation Capability of Open-source Large Language Models (2023 · 92 citations)
    Most work on LLM-driven tool use relies on proprietary APIs. That is a problem for companies that cannot send internal workflows and data to external services. The authors ask whether open-source LLMs can match closed models at turning natural-language goals into executable API calls without heavy human effort.
  2. Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models (2025 · 39 citations)
    LLM systems such as agents and domain-specific reasoners often improve by changing their input context rather than their weights.
  3. SubgoalXL: Subgoal-based Expert Learning for Theorem Proving (2024 · 10 citations)
    SubgoalXL targets two obstacles for LLM-based formal theorem proving in Isabelle: proof data in this domain is scarce, and models struggle with long multi-step reasoning.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. On the Tool Manipulation Capability of Open-source Large Language ModelsarXiv:2305.16504Qiantong Xu, Fenglu Hong, Bo Li, et al.2023 · 92 citations
  2. Agentic Context Engineering: Evolving Contexts for Self-Improving Language ModelsarXiv:2510.04618Qizheng Zhang, Changran Hu, Shubhangi Upasani, et al.2025 · 39 citations
  3. SubgoalXL: Subgoal-based Expert Learning for Theorem ProvingarXiv:2408.11172Xueliang Zhao, Lin Zheng, Haige Bo, et al.2024 · 10 citations
  4. Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills ↗arXiv (Cornell University) · no summary yetJiang, Pengcheng et al.2025 · 3 citations

 Co-authors here

10 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 129 times (of the 149,439 papers whose bibliographies we read); 50 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/changran-hu/