CodeMind: A Framework to Challenge Large Language Models for Code Reasoning
By Changshu Liu, Shizhuo Dylan Zhang, Reyhaneh Jabbarvand. 2024
72 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems, Code Generation & Program Synthesis
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Armando Solar-Lezama
- Koushik Sen
- Naman Jain
- Tianjun Zhang
- Wen-Ding Li
- Ion Stoica
- Swarat Chaudhuri
- Rishabh Singh
In Information Systems
- Armando Solar-Lezama
- Koushik Sen
- Naman Jain
- Tianjun Zhang
- Ion Stoica
- Rishabh Singh
- Mosharaf Chowdhury
- Charles Sutton
In Code Generation & Program Synthesis
Citations in the corpus
- LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code (2024, methodology): “Liu et al. (2024) evaluate the code comprehension capabilities of LLMs using execution.”
- AlgoSimBench: Identifying Algorithmically Similar Problems for Competitive Programming (2025, unknown): “Unlike previous methods [Xiong et al. , 2026; Shi et al. , 2022], ASM does not require any information from human-generated oracle solutions, and yet achieves comparable performance.”
- CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis (2025, unknown): “Beyond generation, tasks like I/O prediction (Gu et al., 2024; Liu et al., 2024a), execution prediction (Liu et al., 2023a; La Malfa et al., 2024; Ni et al., 2024a; Ding et al., 2024), bug localization (Suresh et al., 2025a), and program equivalence (Wei et al., 2025a) have also been studied.”