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The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management

Created by
  • Haebom

Author

Tobias Lindenbauer, Igor Slinko, Ludwig Felder, Egor Bogomolov, Yaroslav Zharov

Outline

To address the high cost of large-scale language model (LLM)-based software engineering (SWE) agents due to their long context histories, this paper compares and analyzes existing LLM-based summarization methods with a simple observation masking strategy. Experiments using five different model configurations demonstrate that the observation masking strategy halves the cost while maintaining a success rate similar to or slightly higher than the LLM summarization method. For example, in the Qwen3-Coder 480B model, observation masking improved the success rate from 53.8% to 54.8%. This suggests that the simplest approach may be the most effective and efficient way to manage context in SWE agents. For reproducibility, the code and data are made public.

Takeaways, Limitations

•
Takeaways: We demonstrate that a simple observation masking strategy can be more efficient and cost-effective than LLM summarization in context management for LLM-based SWE agents. This suggests that a simple approach can outperform complex summarization techniques.
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_Limitations: This study is limited to a specific SWE agent (SWE-agent) and benchmark (SWE-bench Verified), and generalizability to other agents and benchmarks is limited. Further research is needed on a variety of LLM models and tasks.
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