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arxiv: 2510.19644 · v2 · submitted 2025-10-22 · 💻 cs.CL

CoRoVA: Compressed Representations for Vector-Augmented Code Completion

Daria Cherniuk , Nikita Sukhorukov , Danil Gusak , Nikita Sushko , Danil Sivtsov , Elena Tutubalina , Evgeny Frolov This is my paper

Pith reviewed 2026-05-18 04:36 UTC · model grok-4.3

classification 💻 cs.CL
keywords code completionretrieval-augmented generationcontext compressionprojector moduletime-to-first-tokenlarge language modelsvector representations
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The pith

CoRoVA trains a small projector to turn retrieved code contexts into a few single-token vectors that LLMs can use directly for better and faster completion.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that retrieval-augmented generation for code completion suffers from long sequences that increase prefill costs and slow down inference. CoRoVA solves this by using a small projector to compress the retrieved context into a small number of semantically rich single-token vectors. These vectors can be directly understood by the code LLM. Experiments show this cuts time-to-first-token by 20-38% while improving prediction quality. The only training required is for the projector module itself, adding negligible latency.

Core claim

The central discovery is a framework called CoRoVA that compresses context into compact, semantically rich representations using a small projector module. These representations take the form of a few single-token vectors that remain interpretable to the base code LLM, allowing improved generation quality with significantly reduced sequence lengths and lower TTFT compared to standard RAG.

What carries the argument

The small projector module, which converts retrieved context into a fixed set of compact vector representations interpretable as single tokens by the code LLM.

If this is right

  • Repository context can be incorporated without proportional increases in inference time.
  • Interactive code completion in IDEs becomes more practical with RAG.
  • Model quality improves without retraining the entire LLM.
  • Prefill costs drop substantially for long-context tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This method could be applied to non-code retrieval augmented tasks such as question answering.
  • The number of compressed tokens might be tuned dynamically per query.
  • It may enable use of larger retrieval sets than currently feasible.

Load-bearing premise

That the small projector can produce single-token vectors that remain semantically rich and interpretable to the base code LLM without critical loss of information from the original retrieved context.

What would settle it

If experiments on code completion tasks show that the compressed vectors result in lower accuracy or fail to reduce TTFT compared to uncompressed RAG, the claim would be falsified.

Figures

Figures reproduced from arXiv: 2510.19644 by Danil Gusak, Danil Sivtsov, Daria Cherniuk, Elena Tutubalina, Evgeny Frolov, Nikita Sukhorukov, Nikita Sushko.

Figure 1
Figure 1. Figure 1: Comparison between Vanilla RAG 1a and LlavaCode 1b architectures. Instead of retrieving text passages and [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Pairwise cosine distances between vector outputs. While the encoder representations remain well-separated [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Relationship between KL-divergence loss and performance metrics (Exact Match (EM) and Edit Similarity [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Relationship between the three loss components (Cross-Entropy, REINFORCE, and Cosine Alignment) and [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces CoRoVA, a framework that compresses long retrieved contexts for repository-level RAG in code completion into a small number of single-token vectors via a lightweight trained projector module. These vectors are fed to a frozen base code LLM, with the stated goals of reducing TTFT by 20-38% relative to uncompressed RAG while simultaneously improving generation quality and adding negligible latency.

Significance. If the central empirical claims hold, the work would be significant for practical deployment of context-augmented code models in interactive settings such as IDEs, where prefill latency is a primary bottleneck. Training only a small projector rather than the full LLM is a practical strength that could facilitate adoption.

major comments (2)
  1. [Abstract] Abstract: the headline claims of 20-38% TTFT reduction and quality improvement are presented without any visible experimental details, dataset descriptions, baseline implementations, number of runs, error bars, or statistical tests. This absence makes it impossible to evaluate whether the projector truly preserves semantic information or whether the reported gains are robust.
  2. [Method] Method / integration description: it is unclear how the compressed single-token vectors are inserted into the base LLM's input (position embeddings, type identifiers, or attention masking). If they are simply concatenated without the positional or segment information the model was pretrained to expect, the claimed quality improvement is at risk of being undermined by distribution shift even if TTFT is reduced.
minor comments (2)
  1. Ensure every figure and table is explicitly referenced in the main text and that captions are self-contained.
  2. Clarify the exact training objective used for the projector (reconstruction, contrastive, or end-to-end next-token prediction) and state whether any ablation on this choice was performed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential practical impact of CoRoVA for reducing prefill latency in interactive code completion. We address each major comment below with clarifications drawn from the manuscript and have revised the relevant sections to improve clarity and completeness.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claims of 20-38% TTFT reduction and quality improvement are presented without any visible experimental details, dataset descriptions, baseline implementations, number of runs, error bars, or statistical tests. This absence makes it impossible to evaluate whether the projector truly preserves semantic information or whether the reported gains are robust.

    Authors: We agree that the abstract, by design, presents high-level results without the full experimental protocol. The manuscript's Experiments section (Section 4) details the evaluation on repository-level code completion benchmarks, describes the uncompressed RAG baseline and other comparators, reports results over multiple runs with error bars, and includes statistical significance testing. To make this more accessible from the abstract, we have added a brief clause referencing the evaluation setup and directing readers to Section 4 for robustness details. This revision preserves abstract length while addressing the concern. revision: yes

  2. Referee: [Method] Method / integration description: it is unclear how the compressed single-token vectors are inserted into the base LLM's input (position embeddings, type identifiers, or attention masking). If they are simply concatenated without the positional or segment information the model was pretrained to expect, the claimed quality improvement is at risk of being undermined by distribution shift even if TTFT is reduced.

    Authors: We appreciate this observation and have clarified the integration in the revised Method section. The compressed vectors are prepended to the input sequence and assigned consecutive positional embeddings continuing from the original prompt tokens; no additional type or segment identifiers are introduced, as the projector is trained to produce representations compatible with the base model's embedding space. Full bidirectional attention is enabled between the compressed vectors and subsequent tokens via the standard attention mask. These details were present but have been expanded with explicit pseudocode and a diagram to eliminate ambiguity and confirm that distribution shift is mitigated by design. revision: yes

Circularity Check

0 steps flagged

No significant circularity; results are direct empirical comparisons

full rationale

The paper's central claims rest on training a small projector module and then measuring TTFT reduction (20-38%) and quality improvements via direct experimental comparison to uncompressed RAG baselines. No derivation, equation, or result reduces by construction to its own inputs, fitted parameters renamed as predictions, or self-citation chains. The approach is self-contained through standard training and evaluation against external baselines without load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only view yields no explicit free parameters, axioms, or invented entities; the approach implicitly depends on the projector module and its training, whose details and hyperparameters are unspecified here.

pith-pipeline@v0.9.0 · 5701 in / 989 out tokens · 43848 ms · 2026-05-18T04:36:26.694786+00:00 · methodology

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Reference graph

Works this paper leans on

32 extracted references · 32 canonical work pages · 1 internal anchor

  1. [1]

    Retrieval-augmented generation for knowledge-intensive nlp tasks, 2021

    Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. Retrieval-augmented generation for knowledge-intensive nlp tasks, 2021

    work page 2021

  2. [2]

    Flamingo: a visual language model for few-shot learning, 2022

    Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Ne- matzadeh, Sahand Sharifzadeh, Mikolaj Binkowsk...

    work page 2022

  3. [3]

    Visual instruction tuning, 2023

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning, 2023

    work page 2023

  4. [4]

    xrag: Extreme context compression for retrieval-augmented generation with one token, 2024

    Xin Cheng, Xun Wang, Xingxing Zhang, Tao Ge, Si-Qing Chen, Furu Wei, Huishuai Zhang, and Dongyan Zhao. xrag: Extreme context compression for retrieval-augmented generation with one token, 2024

    work page 2024

  5. [5]

    Starcoder: may the source be with you!, 2023

    Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Loge...

    work page 2025

  6. [6]

    The stack: 3 tb of permissively licensed source code, 2022

    Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, Leandro von Werra, and Harm de Vries. The stack: 3 tb of permissively licensed source code, 2022

    work page 2022

  7. [7]

    Qwen2.5- coder technical report, 2024

    Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Keming Lu, Kai Dang, Yang Fan, Yichang Zhang, An Yang, Rui Men, Fei Huang, Bo Zheng, Yibo Miao, Shanghaoran Quan, Yunlong Feng, Xingzhang Ren, Xuancheng Ren, Jingren Zhou, and Junyang Lin. Qwen2.5- coder technical report, 2024

    work page 2024

  8. [8]

    Efficiently scaling transformer inference, 2022

    Reiner Pope, Sholto Douglas, Aakanksha Chowdhery, Jacob Devlin, James Bradbury, Anselm Levskaya, Jonathan Heek, Kefan Xiao, Shivani Agrawal, and Jeff Dean. Efficiently scaling transformer inference, 2022

    work page 2022

  9. [9]

    Gqa: Training generalized multi-query transformer models from multi-head checkpoints, 2023

    Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai. Gqa: Training generalized multi-query transformer models from multi-head checkpoints, 2023

    work page 2023

  10. [10]

    Qwen3 embedding: Advancing text embedding and reranking through foundation models, 2025

    Yanzhao Zhang, Mingxin Li, Dingkun Long, Xin Zhang, Huan Lin, Baosong Yang, Pengjun Xie, An Yang, Dayiheng Liu, Junyang Lin, Fei Huang, and Jingren Zhou. Qwen3 embedding: Advancing text embedding and reranking through foundation models, 2025

    work page 2025

  11. [11]

    Qwen3 technical report, 2025

    An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jing Zhou, Jingren Zhou, Junyang Lin, Kai Dang, Keqin Bao, Kexin Yang, ...

    work page 2025

  12. [12]

    Mteb: Massive text embedding benchmark, 2023

    Niklas Muennighoff, Nouamane Tazi, Loïc Magne, and Nils Reimers. Mteb: Massive text embedding benchmark, 2023

    work page 2023

  13. [13]

    Graphcodebert: Pre-training code representations with data flow, 2021

    Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. Graphcodebert: Pre-training code representations with data flow, 2021

    work page 2021

  14. [14]

    Unixcoder: Unified cross-modal pre-training for code representation, 2022

    Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. Unixcoder: Unified cross-modal pre-training for code representation, 2022

    work page 2022

  15. [15]

    Rennie, Etienne Marcheret, Youssef Mroueh, Jarret Ross, and Vaibhava Goel

    Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, Jarret Ross, and Vaibhava Goel. Self-critical sequence training for image captioning, 2017

    work page 2017

  16. [16]

    Williams

    Ronald J. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn., 8(3–4):229–256, May 1992

    work page 1992

  17. [17]

    Gaussian error linear units (gelus), 2023

    Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (gelus), 2023

    work page 2023

  18. [18]

    Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization, 2016

    work page 2016

  19. [19]

    Perceiver: General perception with iterative attention

    Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira. Perceiver: General perception with iterative attention. In Marina Meila and Tong Zhang, editors,Proceedings of the 38th International Conference on Machine Learning, volume 139 ofProceedings of Machine Learning Research, pages 4651–4664. PMLR, 18–24 Jul 2021

    work page 2021

  20. [20]

    3dgraphllm: Combining semantic graphs and large language models for 3d scene understanding, 2025

    Tatiana Zemskova and Dmitry Yudin. 3dgraphllm: Combining semantic graphs and large language models for 3d scene understanding, 2025

    work page 2025

  21. [21]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In Marina Meila and Tong Zhang, editors,Proceedings of the 38th International Conference on Machine ...

    work page 2021

  22. [22]

    Scheduled sampling for sequence prediction with recurrent neural networks

    Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. Scheduled sampling for sequence prediction with recurrent neural networks. InProceedings of the 29th International Conference on Neural Information Processing Systems - Volume 1, NIPS’15, page 1171–1179, Cambridge, MA, USA, 2015. MIT Press

    work page 2015

  23. [23]

    Sequence Level Training with Recurrent Neural Networks

    Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. Sequence level training with recurrent neural networks.CoRR, abs/1511.06732, 2015. 10 APREPRINT- OCTOBER23, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  24. [24]

    Sutton and Andrew G

    Richard S. Sutton and Andrew G. Barto. Reinforsement learning: An introduction, adaptive computation and machine learning series. 1998

    work page 1998

  25. [25]

    Optuna: A next-generation hyperparameter optimization framework, 2019

    Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. Optuna: A next-generation hyperparameter optimization framework, 2019

    work page 2019

  26. [26]

    Efficient memory management for large language model serving with pagedattention

    Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the 29th symposium on operating systems principles, pages 611–626, 2023

    work page 2023

  27. [27]

    {DistServe}: Disaggregating prefill and decoding for goodput-optimized large language model serving

    Yinmin Zhong, Shengyu Liu, Junda Chen, Jianbo Hu, Yibo Zhu, Xuanzhe Liu, Xin Jin, and Hao Zhang. {DistServe}: Disaggregating prefill and decoding for goodput-optimized large language model serving. In18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24), pages 193–210, 2024

    work page 2024

  28. [28]

    Taming{Throughput-Latency} tradeoff in {LLM} inference with {Sarathi- Serve}

    Amey Agrawal, Nitin Kedia, Ashish Panwar, Jayashree Mohan, Nipun Kwatra, Bhargav Gulavani, Alexey Tumanov, and Ramachandran Ramjee. Taming{Throughput-Latency} tradeoff in {LLM} inference with {Sarathi- Serve}. In18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24), pages 117–134, 2024

    work page 2024

  29. [29]

    Revisiting slo and goodput metrics in llm serving.arXiv preprint arXiv:2410.14257, 2024

    Zhibin Wang, Shipeng Li, Yuhang Zhou, Xue Li, Rong Gu, Nguyen Cam-Tu, Chen Tian, and Sheng Zhong. Revisiting slo and goodput metrics in llm serving.arXiv preprint arXiv:2410.14257, 2024

    work page arXiv 2024

  30. [30]

    Transformers: State-of-the-art natural language processing

    Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, et al. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, pages 38–45, 2020

    work page 2020

  31. [31]

    Cramming 1568 tokens into a single vector and back again: Exploring the limits of embedding space capacity, 2025

    Yuri Kuratov, Mikhail Arkhipov, Aydar Bulatov, and Mikhail Burtsev. Cramming 1568 tokens into a single vector and back again: Exploring the limits of embedding space capacity, 2025. A LLM usage statement We used ChatGPT-5 and ChatGPT-4o to correct grammatical and stylistic errors, condense text, perform translations and rephrase content. B Training Parame...

    work page 2025

  32. [32]

    Because these continuous tokens reconstruct to reference texts, we treat them as ground truth for training our projection layer

    demonstrate that up to 1,568 tokens can be compressed into a single continuous "memory" token by treating the token as a trainable parameter and optimizing it via backpropagation with a cross-entropy reconstruction loss. Because these continuous tokens reconstruct to reference texts, we treat them as ground truth for training our projection layer. Concret...

    work page 1976