This paper presents a replication and extension of the recently introduced state-in-context agent framework. We independently re-implement the DirectSolve variant and evaluate it on the SWE-bench Verified benchmark. We report end-to-end full-benchmark results using GPT-5-mini and run selected ablations with GPT-4.1. In addition, we investigate a complementary research question: What is the impact of token-reducing input transformation strategies on the performance of software engineering agents? Based on a preliminary prompt analysis, we identify source code as the dominant contributor to token consumption. We therefore apply a series of code minification techniques that remove or shorten non-essential lexical elements while preserving program semantics. The proposed transformations are integrated into the agent and systematically evaluated. Experiments show that minification reduces average input token usage by 42% with a 12 percentage-point drop in resolution rate. These findings demonstrate that lightweight source code transformations can yield substantial efficiency gains while retaining a substantial fraction of the baseline performance, indicating a promising path toward more cost-effective agents. The full implementation is publicly available on GitHub: https://github.com/ipa-lab/minified-state-in-context-agent

翻译：本文对近期提出的状态上下文（State-in-Context）代理框架进行了复现与扩展研究。我们独立重新实现了DirectSolve变体，并在SWE-bench Verified基准上进行了评估。我们报告了使用GPT-5-mini完成的端到端全基准测试结果，并利用GPT-4.1开展了部分消融实验。此外，我们探究了一个互补性研究问题：令牌缩减的输入变换策略对软件工程代理性能有何影响？基于初步提示词分析，我们识别出源代码是令牌消耗的主要来源。因此，我们应用了一系列代码压缩技术，在保留程序语义的前提下移除或缩短非必要的词法元素。所提出的变换方法被集成至代理中并进行了系统评估。实验表明，压缩技术使平均输入令牌使用量降低42%，同时解决率下降12个百分点。这些发现表明，轻量级源代码变换能够在保持基线性能大部分水平的同时实现显著的效率提升，为开发更具成本效益的代理指明了一条可行路径。完整实现代码已在GitHub上公开：https://github.com/ipa-lab/minified-state-in-context-agent