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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.11051 |
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| _version_ | 1866917481593962496 |
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| author | Gelvan, Kirill Slinko, Igor Steinbauer, Felix Bogomolov, Egor Kofler, Florian Zharov, Yaroslav |
| author_facet | Gelvan, Kirill Slinko, Igor Steinbauer, Felix Bogomolov, Egor Kofler, Florian Zharov, Yaroslav |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_11051 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | On Problems of Implicit Context Compression for Software Engineering Agents Gelvan, Kirill Slinko, Igor Steinbauer, Felix Bogomolov, Egor Kofler, Florian Zharov, Yaroslav Software Engineering Artificial Intelligence Computation and Language Machine Learning 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. |
| title | On Problems of Implicit Context Compression for Software Engineering Agents |
| topic | Software Engineering Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2605.11051 |