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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

This paper presents the results of a comparative analysis of long context history management strategies in a large-scale language model (LLM)-based software engineering (SWE) agent. We compared existing LLM-based summarization methods, such as OpenHands and Cursor, with observation-masking, a method that simply ignores previous observations, using various model configurations on the SWE-bench Verified dataset. We found that the observation-masking strategy achieved similar or slightly higher problem-solving rates than LLM-based summarization methods, while reducing the cost by half. For example, on the Qwen3-Coder 480B model, observation-masking improved the problem-solving rate from 53.8% to 54.8%, achieving similar performance to LLM summarization at a lower cost. This study suggests that, at least in SWE-agent and SWE-bench Verified environments, the most effective and efficient context management may be the simplest approach. For reproducibility, we make the code and data available.

Takeaways, Limitations

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Takeaways:
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We show that a simple observation-masking strategy can be more efficient and effective than complex summarization techniques in LLM-based SWE agents.
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We present a practical context management strategy that can simultaneously achieve cost reduction and performance improvement.
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It provides a new perspective on efficient context management in LLM-based agents.
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Limitations:
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The study was limited to a specific agent (SWE-agent) and dataset (SWE-bench Verified), which may limit generalizability.
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Results may vary for other LLMs or job types.
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Further research is needed on the long-term performance and stability of observation-masking strategies.
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