Peng Shu

Peng Shu has 4 papers in our library, in 2024–2026, most of them on Evaluation & benchmarks, Reasoning; often with Huan Yu, Jie Jiang, Liqun Liu.

4 papers in our library · 2024–2026

The profile is the library's match of the name; two people with one name are not always told apart.

Fields, topics and concepts

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

The most cited first (OpenAlex and our library).

Evaluation of OpenAI o1: Opportunities and Challenges of AGI (2024 · 64 citations)
The paper examines how close OpenAI's o1-preview comes to general-purpose intelligence. o1-preview is a model trained with reinforcement learning to work through chain-of-thought reasoning before it answers. A large multi-institution team tested it on 27 task categories, mostly built from public datasets. These span…
Revolutionizing Finance with LLMs: An Overview of Applications and Insights (2024 · 63 citations)
This survey examines how large language models can be applied to finance, a field where data is highly specialized and errors are costly.
Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training (2026 · 2 citations)
Agentic RAG systems trained with reinforcement learning, such as Search-R1, usually receive a single binary reward for the final answer.
SSR-A: Spatial- and Semantic-Aware Instructions and Curriculum Reinforcement for Advertisement Compliant Rectification (2026)
Ad platforms reject millions of non-compliant image ads every day, and fixing them by hand does not scale.

12 of 13 co-authors with a page, the most papers together first.

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

Papers of our library cite their works 66 times (of the 149,439 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated 1 November 2026