On Problems of Implicit Context Compression for Software Engineering Agents
For researchers developing LLM agents for software engineering, this paper highlights a critical failure mode of a promising context compression method.
The paper investigates the use of In-Context Autoencoders for implicit context compression in LLM-based software engineering agents, finding that while effective for single-shot tasks, it fails on multi-step agentic coding tasks.
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.