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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.19644 |
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| _version_ | 1866917403998289920 |
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| author | Cherniuk, Daria Sukhorukov, Nikita Gusak, Danil Sushko, Nikita Sivtsov, Danil Tutubalina, Elena Frolov, Evgeny |
| author_facet | Cherniuk, Daria Sukhorukov, Nikita Gusak, Danil Sushko, Nikita Sivtsov, Danil Tutubalina, Elena Frolov, Evgeny |
| contents | Retrieval-augmented generation has emerged as one of the most effective approaches for code completion enhancement, especially when repository-level context is important. However, adding this extra retrieved context significantly increases sequence length, raises prefill cost, and degrades time-to-first-token (TTFT), which slows down inference -- a critical limitation for interactive settings such as IDEs. In this work, we introduce CoRoVA, a framework that compresses context into compact, semantically rich representations that remain interpretable to code LLMs. This improves generation quality while reducing prompt augmentation to only a few compressed single-token vectors. Our approach requires training only a small projector module and introduces negligible additional latency, yet it significantly improves the prediction quality of code LLMs. Our experiments show that CoRoVA enables a 20-38\% reduction in TTFT on completion tasks compared to uncompressed RAG. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19644 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoRoVA: Compressed Representations for Vector-Augmented Code Completion Cherniuk, Daria Sukhorukov, Nikita Gusak, Danil Sushko, Nikita Sivtsov, Danil Tutubalina, Elena Frolov, Evgeny Computation and Language Retrieval-augmented generation has emerged as one of the most effective approaches for code completion enhancement, especially when repository-level context is important. However, adding this extra retrieved context significantly increases sequence length, raises prefill cost, and degrades time-to-first-token (TTFT), which slows down inference -- a critical limitation for interactive settings such as IDEs. In this work, we introduce CoRoVA, a framework that compresses context into compact, semantically rich representations that remain interpretable to code LLMs. This improves generation quality while reducing prompt augmentation to only a few compressed single-token vectors. Our approach requires training only a small projector module and introduces negligible additional latency, yet it significantly improves the prediction quality of code LLMs. Our experiments show that CoRoVA enables a 20-38\% reduction in TTFT on completion tasks compared to uncompressed RAG. |
| title | CoRoVA: Compressed Representations for Vector-Augmented Code Completion |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.19644 |