On Problems of Implicit Context Compression for Software Engineering Agents

• URL: http://arxiv.org/abs/2605.11051v1
• Date: Mon, 11 May 2026 14:47:07 GMT
• Title: On Problems of Implicit Context Compression for Software Engineering Agents
• Abstract summary: LLM-based Software Engineering agents face a critical bottleneck: context length limitations cause failures on complex, long-horizon tasks.<n>One promising solution is to encode context as continuous embeddings rather than discrete tokens, enabling denser information storage.<n>We apply the recently proposed In-Context Autoencoder for this purpose.<n>While the method performs well on single-shot common-knowledge and code-understanding tasks, our experiments demonstrate that it fails on multi-step agentic coding tasks.
• Score: 3.9397002735105566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LLM-based Software Engineering agents face a critical bottleneck: context length limitations cause failures on complex, long-horizon tasks. One promising solution is to encode context as continuous embeddings rather than discrete tokens, enabling denser information storage. We apply the recently proposed In-Context Autoencoder for this purpose. While the method performs well on single-shot common-knowledge and code-understanding tasks, our experiments demonstrate that it fails on multi-step agentic coding tasks. In this paper, we explore this phenomenon and discuss possible factors contributing to this failure.

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