6 papers
Learning to Reason with Insight for Informal Theorem Proving
Yunhe Li, Hao Shi, Bowen Deng +8
Although most of the automated theorem-proving approaches depend on formal proof systems, informal theorem proving can align better with large language models' (LLMs) strength in n…
PARM: Pipeline-Adapted Reward Model
Xingyu Fan, Wei Shao, Jiacheng Liu +2
Reward models (RMs) are central to aligning large language models (LLMs) with human preferences, powering RLHF and advanced decoding strategies. While most prior work focuses on si…
SysTradeBench: An Iterative Build-Test-Patch Benchmark for Strategy-to-Code Trading Systems with Drift-Aware Diagnostics
Yuchen Cao, Hanlin Zhang, Jacky Wai Keung +2
Large language models (LLMs) are increasingly used as quantitative research copilots to translate natural-language strategy specifications into executable trading code. Yet most ex…
OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling
Zhicheng Yang, Yiwei Wang, Yinya Huang +7
Large language models (LLMs) have exhibited their problem-solving abilities in mathematical reasoning. Solving realistic optimization (OPT) problems in application scenarios requir…
FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving
Xiaohan Lin, Qingxing Cao, Yinya Huang +5
Formal verification (FV) has witnessed growing significance with current emerging program synthesis by the evolving large language models (LLMs). However, current formal verificati…
MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data
Yinya Huang, Xiaohan Lin, Zhengying Liu +6
Recent large language models (LLMs) have witnessed significant advancement in various tasks, including mathematical reasoning and theorem proving. As these two tasks require strict…