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Gaowen Liu

Gaowen Liu has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning, Bias, fairness & safety; often with Jayanth Srinivasa, Dawn Song, Kaiwen Zhou.

4 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 10 · 72 works · 522 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 38 times in our library · ⭐ Paper Stars ranking: #7990

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

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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. The Hidden Risks of Large Reasoning Models: A Safety Assessment of R1 (2025 · 30 citations)
    Open-weight large reasoning models such as DeepSeek-R1 have become much stronger. Because they are freely available, misuse is a real concern. This study audits their safety systematically. It compares three open reasoning models (DeepSeek-R1, the Llama 3.3-based distilled DeepSeek-R1-70b, and QwQ) with non-reasoning…
  2. SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning (2025 · 9 citations)
    Large Reasoning Models (LRMs) such as DeepSeek-R1 deliberate before answering, but they often comply with harmful requests and jailbreaks.
  3. Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge Injection (2026 · 4 citations)
    The paper asks how well language models can absorb new facts after pre-training. Earlier knowledge-injection studies mostly relied on synthetic data drawn from noisy Wikipedia snapshots, and their questions were often poorly specified.
  4. FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments (2026)
    LLM-driven agents in multi-turn customer-service tool-use settings often fail because early wrong decisions compound over the conversation.

 Co-authors here

12 of 15 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 38 times (of the 149,439 papers whose bibliographies we read); 11 citing papers with a page here →.

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