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| Issue | Conventional approach | KF‑FullNet solution | |-------|-----------------------|---------------------| | | Mixed‑precision (FP16/ BF16) training can suffer from overflow/underflow in very deep models. | Guarantees end‑to‑end FP32 (or FP64) arithmetic with optional loss‑scaling, eliminating gradient‑explosion without sacrificing throughput (thanks to hardware‑accelerated tensor‑wide operations). | | Network composability | Hard‑coded layer stacks; re‑use of sub‑graphs is manual. | FullNet Graph Language (FGL) – a domain‑specific language (DSL) that describes networks as directed acyclic graphs (DAGs). Sub‑graphs are first‑class objects that can be versioned, shared, and dynamically re‑wired at run‑time. | | Reproducibility & auditability | Model checkpoints are opaque; provenance is rarely tracked. | Integrated provenance engine records every transformation (data‑augmentation, optimizer step, hyper‑parameter change) in a cryptographically signed ledger (compatible with the emerging OpenAI‑Audit standard). |

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