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VMS‑K85 demonstrates that a highly configurable synthetic data generator can produce datasets of sufficient quality to replace the majority of real‑world data in training modern deep learning systems. By exposing 85 tunable parameters and supporting community‑driven KARINA extensions, VMS‑K85 offers a scalable, cost‑effective, and ethically aware solution for data‑hungry AI research.

Future work will focus on:

The complete source code, documentation, and the VMS‑K85‑Bench suite are released at https://github.com/vmsk85 under the MIT License.

| Task | Real‑Data Baseline | Synthetic (VMS‑K85) | Gap | Relative Cost Reduction | |------|-------------------|----------------------|-----|--------------------------| | Object Detection (mAP) | 0.485 | 0.467 | −3.7 % | 82 % | | Speech‑to‑Text (WER) | 7.8 % | 8.4 % | +0.6 % | 78 % | | Anomaly Detection (AUROC) | 0.945 | 0.928 | −1.8 % | 85 % | | Medical Classification (AUC) | 0.872 | 0.859 | −1.5 % | 80 % |

Key observations

Ablation studies (see Appendix A) confirm that KARINA modules contribute the largest gain for the medical imaging task (+2.3 % AUC), whereas lighting variation is the dominant factor for object detection (+1.9 % mAP).

Statistical significance assessed via paired bootstrap (1 000 resamples, α = 0.05).

Synthetic data can alleviate privacy concerns; however, realistic generation of human faces or speech raises potential misuse (deepfakes). VMS‑K85 includes a responsibility flag that, when enabled, injects detectable watermarks into generated media and logs provenance metadata. Users are required to acknowledge the Responsible Synthetic Data Use Policy before accessing the repository.

+-------------------+        +-------------------+        +-------------------+
|  Parameter Engine | -----> |  Generator Core   | -----> |  Post‑Processing   |
+-------------------+        +-------------------+        +-------------------+
        |                         |                           |
        v                         v                           v
   YAML/JSON                Multi‑Modality                Augmentors
   Config Files            Generators (GAN, VAE,    (Noise, Blur, Mix,
                            Diffusion, ODE)          Label Corruption)
  • Post‑Processing applies deterministic or stochastic augmentations, optionally injecting label noise according to user‑defined distributions.

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