Reducing Token Usage of State-in-Context Agents using Minification

2026-05-31

AI Summary

This work addresses the high context token consumption in software engineering agents caused by processing lengthy source code, which hinders deployment efficiency and increases costs. Building upon and extending the state-in-context framework, we introduce, for the first time, a systematic approach to semantic-preserving source code minification that reduces input length by removing or compressing non-essential syntactic elements. We integrate multiple minification strategies into the DirectSolve agent and evaluate them end-to-end using GPT-5-mini and GPT-4.1. Experimental results demonstrate that our method reduces input tokens by 42% on average while incurring only a 12-percentage-point drop in task success rate, confirming that lightweight input transformation can substantially improve cost-efficiency without severely compromising performance.

Abstract

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

Problem

• token usage
• state-in-context agents
• code minification
• software engineering agents
• input transformation

Innovation

• code minification
• token reduction
• state-in-context agent
• software engineering agents
• large language models