English

On Problems of Implicit Context Compression for Software Engineering Agents

Software Engineering 2026-05-13 v1 Artificial Intelligence Computation and Language Machine Learning

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.

Keywords

Cite

@article{arxiv.2605.11051,
  title  = {On Problems of Implicit Context Compression for Software Engineering Agents},
  author = {Kirill Gelvan and Igor Slinko and Felix Steinbauer and Egor Bogomolov and Florian Kofler and Yaroslav Zharov},
  journal= {arXiv preprint arXiv:2605.11051},
  year   = {2026}
}