On Problems of Implicit Context Compression for Software Engineering Agents

TL;DR

This paper investigates the challenge of compressing context for LLM-based software engineering agents, evaluating an embedding-based approach and analyzing its limitations on complex tasks.

Contribution

It applies the In-Context Autoencoder to context compression in software engineering agents and critically examines its failure modes on multi-step tasks.

Findings

The autoencoder performs well on simple tasks but fails on multi-step agentic coding tasks.

The paper discusses potential reasons for the failure of embedding-based context compression.

It highlights the need for improved methods for dense context encoding in complex software tasks.

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.