--- title: "Baolong Bi — author profile and papers — Elamur Research" description: "Baolong Bi, Chinese Academy of Sciences: 6 papers on Elamur Research (2025–2026), h-index 3 — affiliations, topics, co-authors and who…" canonical: "https://research.elamur.ai/free/en/authors/baolong-bi/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/baolong-bi/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Baolong Bi # Baolong Bi Baolong Bi has 6 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/), [Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/); often with [Xueqi Cheng](https://md.research.elamur.ai/free/en/authors/xueqi-cheng/), [Lingrui Mei](https://md.research.elamur.ai/free/en/authors/lingrui-mei/), [Shenghua Liu](https://md.research.elamur.ai/free/en/authors/shenghua-liu/). 6 papers in our library · 2025–2026 [OpenAlex](https://openalex.org/A5094348722) · [ORCID](https://orcid.org/0009-0003-4027-1366) · [**h-index 3**](https://openalex.org/authors/A5094348722) · [47 works](https://openalex.org/works?filter=author.id:A5094348722) · [35 citations](https://openalex.org/works?filter=author.id:A5094348722&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 6 works in our library](https://md.research.elamur.ai/free/en/authors/baolong-bi/#papers) · [🔗 Cited 35 times in our library](https://md.research.elamur.ai/free/en/authors/baolong-bi/cited-by/) · [⭐ Paper Stars ranking: #9180](https://md.research.elamur.ai/free/en/paper-stars/page/92/#star-9180) 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=Baolong+Bi) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Baolong+Bi) · [dblp](https://dblp.org/search?q=Baolong+Bi) · [ACL Anthology](https://aclanthology.org/search/?q=Baolong+Bi) ## Affiliations [🏛️**Chinese Academy of Sciences** Beijing · China 2024–2026 · 37 papers*current*](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) · [🔬**Institute of Computing Technology** Beijing · China 2023–2026 · 37 papersEmployment since 2023*current*](https://md.research.elamur.ai/free/en/labs/institute-of-computing-technology/) · [🎓**University of Chinese Academy of Sciences** Beijing · China 2024–2026 · 17 papers*current*](https://md.research.elamur.ai/free/en/universities/university-of-chinese-academy-of-sciences/) · [🏢**Meituan** Beijing · China 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/companies/meituan/) · [🎓**Tsinghua University** Beijing · China 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/tsinghua-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 5 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/) · [Computer Vision and Pattern Recognition · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) 🧭 Topics 3 Research subjects and languages the work is about.[Language models & pretraining · 3 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Bias, fairness & safety · 1 work](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/) ## Works by year ### 2020s [**2026** 3 papers](https://md.research.elamur.ai/free/en/authors/baolong-bi/year/2026/) · [**2025** 3 papers](https://md.research.elamur.ai/free/en/authors/baolong-bi/year/2025/) ## Papers The most cited first (OpenAlex and our library). 1. **[Neuron-Level Sequential Editing for Large Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2025-neuron-level-sequential-editing-large/)** *(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… 2. **[Can Graph Descriptive Order Affect Solving Graph Problems with LLMs?](https://md.research.elamur.ai/free/en/papers/ge-2025-can-graph-descriptive-order-affect/)** *(2025 · 10 citations)* Studies of LLM graph reasoning usually turn a graph into text by listing its edges in an arbitrary sequence. 3. **[Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception](https://md.research.elamur.ai/free/en/papers/ni-2025-fully-exploiting-llm-internal-states/)** *(2025 · 9 citations)* LLMs often answer confidently even when they are wrong, which shows they do not reliably sense where their knowledge ends. 4. **[Gated Differentiable Working Memory for Long-Context Language Modeling](https://md.research.elamur.ai/free/en/papers/mei-2026-gated-differentiable-working-memory-long/)** *(2026 · 4 citations)* Long inputs remain difficult for transformer LLMs. Attention spreads thin over thousands of tokens, and evidence placed mid-document is often overlooked. Test-time training methods such as qTTT address this by briefly fine-tuning "fast weights" on the given context. However, they sample positions uniformly, so they… 5. **[HiddenGuard: Fine-Grained Safe Generation with Specialized Representation Router](https://md.research.elamur.ai/free/en/papers/mei-2026-hiddenguard-fine-grained-safe-generation/)** *(2026 · 4 citations)* Safety alignment in LLMs usually works as a yes/no gate: a prompt is either answered or refused. This tends to block harmless requests, such as a "killer slideshow," and to miss subtle or context-dependent harm. The authors propose HiddenGuard, which moderates output token by token instead of refusing whole answers. 6. **[a1: Steep Test-time Scaling Law via Environment Augmented Generation](https://md.research.elamur.ai/free/en/papers/mei-2026-a1-steep-test-time-scaling/)** *(2026 · 2 citations)* LLMs often fail at long multi-step reasoning. They usually generate a whole solution in one pass with no intermediate checks, so an early mistake carries through to the final answer. The authors propose Environment Augmented Generation (EAG), which casts reasoning as a Markov Decision Process. While generating, the… ## Co-authors here 12 of 15 co-authors with a page, the most papers together first. - [Xueqi Cheng](https://md.research.elamur.ai/free/en/authors/xueqi-cheng/) ×5 - [Lingrui Mei](https://md.research.elamur.ai/free/en/authors/lingrui-mei/) ×4 - [Shenghua Liu](https://md.research.elamur.ai/free/en/authors/shenghua-liu/) ×4 - [Yiwei Wang](https://md.research.elamur.ai/free/en/authors/yiwei-wang/) ×4 - [Yuyao Ge](https://md.research.elamur.ai/free/en/authors/yuyao-ge/) ×3 - [Jiafeng Guo](https://md.research.elamur.ai/free/en/authors/jiafeng-guo/) ×2 - [Jun Wan](https://md.research.elamur.ai/free/en/authors/jun-wan/) ×2 - [An Zhang](https://md.research.elamur.ai/free/en/authors/an-zhang/) - [Junfeng Fang](https://md.research.elamur.ai/free/en/authors/junfeng-fang/) - [Keping Bi](https://md.research.elamur.ai/free/en/authors/keping-bi/) - [Ruibin Yuan](https://md.research.elamur.ai/free/en/authors/ruibin-yuan/) - [Shiyu Ni](https://md.research.elamur.ai/free/en/authors/shiyu-ni/) ## 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/) ×5: [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/) ×3 - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) ## Cited by Papers of our library cite their works 35 times (of the 149,439 papers whose bibliographies we read); [14 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/baolong-bi/cited-by/). Updated 31 October 2026