Reducing Token Usage of State-in-Context Agents using Minification
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911738514898944 |
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| author | Hrubec, Nicolas Cito, Jürgen |
| author_facet | Hrubec, Nicolas Cito, Jürgen |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01326 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reducing Token Usage of State-in-Context Agents using Minification Hrubec, Nicolas Cito, Jürgen Software Engineering 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 |
| title | Reducing Token Usage of State-in-Context Agents using Minification |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2606.01326 |