arxiv:2605.11051

On Problems of Implicit Context Compression for Software Engineering Agents

Published on May 11

Authors:

Abstract

LLM-based software engineering agents encounter limitations due to context length constraints, prompting exploration of continuous embeddings via In-Context Autoencoders for improved information storage, though this approach shows reduced effectiveness in multi-step coding tasks.

AI-generated summary

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.