On Problems of Implicit Context Compression for Software Engineering Agents

2026-05-11

AI Summary

This work addresses the limitation of large language model–driven software engineering agents, which struggle to maintain the long-term context required for complex, multi-step tasks due to finite context windows. The study presents the first systematic investigation into implicit context compression methods for such tasks, proposing an In-Context Autoencoder that compresses contextual information into continuous embeddings to circumvent length constraints. While the approach demonstrates strong performance on single-step code comprehension and commonsense reasoning benchmarks, it exhibits significant degradation in multi-step agent-based coding tasks. These findings reveal a fundamental limitation of current implicit compression techniques when applied to complex, temporally extended software engineering scenarios, offering critical insights for future research directions in context management for intelligent software agents.

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.

Problem

• context compression
• software engineering agents
• LLM
• in-context autoencoder
• long-horizon tasks

Innovation

• implicit context compression
• software engineering agents
• in-context autoencoder
• long-horizon tasks
• continuous embeddings