🧑‍🔬 Tianpeng Li

🏛️ Affiliations

🏛️Chinese Academy of SciencesBeijing · China2007–2026 · 21 papersEducation 2017–2021 · Shenyang Institute of Applied Ecologycurrent🔬Institute of Applied EcologyShenyang · China2019–2026 · 17 paperscurrent🎓Yangzhou UniversityYangzhou · China2014–2026 · 10 papersEducation 2014–2017 · Master, College of environmental science and engineeringEmployment · College of Environmental Science and Engineeringcurrent🎓Gansu Agricultural UniversityLanzhou · China2026 · 1 papercurrent🎓Zaozhuang UniversityZaozhuang · China2017–2025 · 8 papers🎓Northwest Normal UniversityLanzhou · China2014–2017 · 7 papers🎓PLA Army Engineering UniversityNanjing · China2022–2025 · 6 papers🎓Qilu University of TechnologyJinan · China2021–2024 · 6 papers🎓Shandong Academy of SciencesJinan · China2021–2024 · 6 papers🎓Donghua UniversityShanghai · China2008–2017 · 6 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 21 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Baichuan-Omni-1.5 Technical Report (2025 · 28 citations)
   Open-source multimodal LLMs mostly pair vision with text. Voice support usually relies on separate ASR and TTS modules, which add latency. End-to-end omni models such as VITA-1.5 and Mini-Omni2 often lose quality when modalities compete, especially on text-only tasks. Baichuan-Omni-1.5 is a single 7B-scale model that…
2. Baichuan-M2: Scaling Medical Capability with Large Verifier System (2025 · 19 citations)
   Medical LLMs often do well on exam-style tests such as USMLE but less well in real consultations, where information comes in gradually over many turns and communication matters.
3. MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification (2025 · 19 citations)
   The paper targets two gaps in multimodal math reasoning. There is no strong verifier for checking answers from multimodal LLMs, and there is little long chain-of-thought (CoT) training data with images. The authors build two 7B models. MM-Verifier is an outcome reward model that judges whether a solution to an…
4. Baichuan-Omni Technical Report (2024 · 18 citations)
   GPT-4o showed what a single model handling several input types can do, but the open-source community had no comparable system.
5. Baichuan-Audio: A Unified Framework for End-to-End Speech Interaction (2025 · 16 citations)
   Cascaded voice assistants chain ASR, an LLM and TTS. This adds latency, compounds errors and discards paralinguistic cues. End-to-end alternatives such as Moshi and GLM-4-Voice tend to lose reasoning ability once speech is added. Baichuan-Audio is a 7B end-to-end audio LLM for real-time bilingual (Chinese/English)…
6. Baichuan Alignment Technical Report (2024 · 5 citations)
   Alignment methods for large language models are usually kept proprietary or described only in fragments. This technical report from Baichuan Inc. and Peking University sets out the full alignment pipeline behind the Baichuan models. The pipeline has three stages. A Prompt Augmentation System (PAS) automatically…
7. Baichuan-M3: Modeling Clinical Inquiry for Reliable Medical Decision-Making (2026 · 2 citations)
   Medical LLMs score well on static benchmarks but often stall in real consultations: they rarely ask for missing facts, and they may hallucinate when filling gaps.
8. BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline (2024 · 2 citations)
   Pre-training corpora largely determine what a large language model can do. Yet many developers, including some that release open weights, keep their data collection and mixing recipes private. Some models are also tuned toward particular benchmarks, which hides their real abilities.
9. Training Verifier to Assessing Complex Real-World Tool-Use Trajectories (2026)
   LLM agents that handle multi-turn customer-service style tasks need training dialogues that reflect real user–agent collaboration.

🤝 Co-authors here

12 of 36 co-authors with a page, the most papers together first.

• Fan Yang ×6
• Guosheng Dong ×4
• Haoze Sun ×4
• Mingan Lin ×4
• Tao Zhang ×4
• Da Pan ×3
• Linzhuang Sun ×3
• Mingyang Chen ×3
• Yijie Zhou ×3
• Bihui Yu ×2
• Chengfeng Dou ×2
• Chenzheng Zhu ×2

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): ACL 2025
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026

🔗 Cited by

Papers of our library cite their works 492 times (of the 149,439 papers whose bibliographies we read); 115 citing papers with a page here →.

Updated 1 November 2026

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