AI RESEARCH
On Problems of Implicit Context Compression for Software Engineering Agents
arXiv CS.AI
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ArXi:2605.11051v1 Announce Type: cross 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 nstrate that it fails on multi-step agentic coding tasks.