--- title: "Guanhua Chen — author profile and papers — Elamur Research" description: "Guanhua Chen, Southern University of Science and Technology: 11 papers on Elamur Research (2025–2026), h-index 1 — affiliations, topics…" canonical: "https://research.elamur.ai/free/en/authors/guanhua-chen/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/guanhua-chen/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Guanhua Chen # Guanhua Chen Guanhua Chen has 11 papers in our library, in 2025–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/); often with [Yun Chen](https://md.research.elamur.ai/free/en/authors/yun-chen/), [Yang Liu](https://md.research.elamur.ai/free/en/authors/yang-liu/), [Peng Li](https://md.research.elamur.ai/free/en/authors/peng-li/). 11 papers in our library · 2025–2026 [OpenAlex](https://openalex.org/A5139763038) · [**h-index 1**](https://openalex.org/authors/A5139763038) · [32 works](https://openalex.org/works?filter=author.id:A5139763038) · [2 citations](https://openalex.org/works?filter=author.id:A5139763038&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 11 works in our library](https://md.research.elamur.ai/free/en/authors/guanhua-chen/#papers) · [🔗 Cited 34 times in our library](https://md.research.elamur.ai/free/en/authors/guanhua-chen/cited-by/) · [⭐ Paper Stars ranking: #9461](https://md.research.elamur.ai/free/en/paper-stars/page/95/#star-9461) 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=Guanhua+Chen) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Guanhua+Chen) · [dblp](https://dblp.org/search?q=Guanhua+Chen) · [ACL Anthology](https://aclanthology.org/search/?q=Guanhua+Chen) ## Affiliations [🎓**Southern University of Science and Technology** Shenzhen · China 2025–2026 · 28 papers*current*](https://md.research.elamur.ai/free/en/universities/southern-university-of-science-and-technology/) 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 8 The field of science this work belongs to (OpenAlex fields).[Computer Science · 11 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 10 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 8 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 8 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Control and Systems Engineering · 1 work](https://md.research.elamur.ai/free/en/shelves/engineering/control-and-systems-engineering/) · [Engineering · 1 work](https://md.research.elamur.ai/free/en/shelves/engineering/) · [Software · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/software/) 🧭 Topics 5 Research subjects and languages the work is about.[Language models & pretraining · 7 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning · 3 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Code & programming languages · 1 work](https://md.research.elamur.ai/free/en/topics/code-and-programming/) · [Evaluation & benchmarks · 1 work](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Semantic parsing & meaning representation · 1 work](https://md.research.elamur.ai/free/en/topics/semantic-parsing/) ## Works by year ### 2020s [**2026** 9 papers](https://md.research.elamur.ai/free/en/authors/guanhua-chen/year/2026/) · [**2025** 2 papers](https://md.research.elamur.ai/free/en/authors/guanhua-chen/year/2025/) ## Papers The most cited first (OpenAlex and our library). 1. **[Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-harnessing-reasoning-economy-survey-efficient/)** *(2025 · 16 citations)* Large Reasoning Models such as OpenAI o1, DeepSeek R1 and QwQ solve hard math and coding problems by producing long chains of thought. 2. **[SPPO: Sequence-Level PPO for Long-Horizon Reasoning Tasks](https://md.research.elamur.ai/free/en/papers/wang-2026-sppo-sequence-level-ppo-long/)** *(2026 · 8 citations)* Reinforcement learning with verifiable rewards is a common way to improve LLM reasoning, but the two main options each have a cost. 3. **[G2: Guided Generation for Enhanced Output Diversity in LLMs](https://md.research.elamur.ai/free/en/papers/ruan-2025-g2-guided-generation-enhanced-output/)** *(2025 · 6 citations)* LLMs often return near-identical answers when asked the same question repeatedly. This hurts creative writing, best-of-N selection, synthetic data creation and test-time scaling for reasoning. Raising the sampling temperature adds variety but lowers quality, and re-prompting with earlier answers can drift away from… 4. **[No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning](https://md.research.elamur.ai/free/en/papers/li-2026-no-more-stale-feedback-co/)** *(2026 · 3 citations)* Critique-guided reinforcement learning supplements sparse outcome rewards for LLM agents with natural-language feedback. 5. **[Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem](https://md.research.elamur.ai/free/en/papers/xiao-2026-modeling-llm-unlearning-as-asymmetric/)** *(2026 · 2 citations)* LLM unlearning aims to erase targeted knowledge, such as hazardous biosecurity or cybersecurity facts or personal data, while keeping the model generally useful. 6. **[Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL](https://md.research.elamur.ai/free/en/papers/wang-2026-beyond-static-rules-automated-discovery/)** *(2026)* LLM-based Text-to-SQL systems score well on benchmarks such as Spider and BIRD, but they can still fail in hidden ways. 7. **[CAPruner: Conceptual-Adjacent Scene Graph Pruner for Enhancing 3D Spatial Reasoning of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2026-capruner-conceptual-adjacent-scene-graph/)** *(2026)* LLM-based systems for 3D vision-language (3D-VL) tasks often describe a scene to the model as a graph of objects and their pairwise spatial relations. 8. **[Evaluating Memory Capability in Continuous Lifelog Scenario](https://md.research.elamur.ai/free/en/papers/zheng-2026-evaluating-memory-capability-continuous-lifelog/)** *(2026)* Smart glasses and pocket recorders with always-on microphones can capture a person's everyday spoken interactions. 9. **[GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models](https://md.research.elamur.ai/free/en/papers/ruan-2026-gift-guided-fine-tuning-transfer/)** *(2026)* Adapting instruction-tuned LLMs to a specific domain is risky: fine-tuning them directly can disturb the balance of skills built during post-training. 10. **[InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning](https://md.research.elamur.ai/free/en/papers/su-2026-instructdiff-domain-adaptive-data-selection/)** *(2026)* Fine-tuning LLMs on entire instruction datasets is expensive and yields diminishing returns. Existing data-selection techniques also tend to work only in the domain they were designed for: methods tuned for general instruction-following transfer poorly to reasoning, and vice versa. InstructDiff addresses this with a… 11. **[Representation-Guided Parameter-Efficient LLM Unlearning](https://md.research.elamur.ai/free/en/papers/xiao-2026-representation-guided-parameter-efficient-llm/)** *(2026)* Removing memorized private or harmful content from LLMs without retraining is hard. LoRA-based unlearning methods such as FILA and VILA pick which weights to modify using Fisher-information importance scores. Because of superposition, a single parameter often serves many concepts, so these scores cannot cleanly… ## Co-authors here 12 of 39 co-authors with a page, the most papers together first. - [Yun Chen](https://md.research.elamur.ai/free/en/authors/yun-chen/) ×7 - [Yang Liu](https://md.research.elamur.ai/free/en/authors/yang-liu/) ×5 - [Peng Li](https://md.research.elamur.ai/free/en/authors/peng-li/) ×4 - [Yixia Li](https://md.research.elamur.ai/free/en/authors/yixia-li/) ×4 - [Jian Yang](https://md.research.elamur.ai/free/en/authors/jian-yang/) ×2 - [Jianjie Zheng](https://md.research.elamur.ai/free/en/authors/jianjie-zheng/) ×2 - [Jiehui Zhao](https://md.research.elamur.ai/free/en/authors/jiehui-zhao/) ×2 - [Lei Yang](https://md.research.elamur.ai/free/en/authors/lei-yang/) ×2 - [Zeguan Xiao](https://md.research.elamur.ai/free/en/authors/zeguan-xiao/) ×2 - [Zhiwen Ruan](https://md.research.elamur.ai/free/en/authors/zhiwen-ruan/) ×2 - [Boyang Xue](https://md.research.elamur.ai/free/en/authors/boyang-xue/) - [Derek F. Wong](https://md.research.elamur.ai/free/en/authors/derek-f-wong/) ## 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/) ×6: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ×6 - [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 - [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/): [EMNLP 2025](https://md.research.elamur.ai/free/en/venues/emnlp/2025/) ## Cited by Papers of our library cite their works 34 times (of the 149,439 papers whose bibliographies we read); [16 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/guanhua-chen/cited-by/). Updated 2 November 2026