Evaluating LLMs Code Reasoning Under Real-World Context

• URL: http://arxiv.org/abs/2604.12881v1
• Date: Tue, 14 Apr 2026 15:32:07 GMT
• Title: Evaluating LLMs Code Reasoning Under Real-World Context
• Abstract summary: We present R2Eval1, a benchmark of 135 code reasoning problems drawn from ten widely used Python projects.<n>Unlike prior work, R2Eval serializes compound and custom types, preserving real-world data complexity.
• Score: 0.7953056533753116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Code reasoning tasks are increasingly crucial to evaluating large language models (LLMs). Yet most existing benchmarks rely on simplistic, LLM-generated snippets or human-written solutions to code challenges and often restrict inputs and outputs to primitive types, failing to reflect the structure and dependencies of real-world projects. These simplifications limit their ability to measure practical generalizability. We present R2Eval1, a benchmark of 135 code reasoning problems drawn from ten widely used Python projects. Unlike prior work, R2Eval serializes compound and custom types, preserving real-world data complexity and enabling a more realistic assessment of LLMs.

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