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| Feature | Implementation Details | |---------|------------------------| | | All reduction ops (e.g., Sum , Mean ) use Kahan‑Compensated algorithms to reduce rounding error. | | Loss‑Scaling (optional) | For training on GPUs where FP32 throughput is higher, a dynamic loss‑scaling module can be inserted automatically without affecting final FP32 values. | | Deterministic RNG | Uses Philox counter‑based RNG; seed and counter are recorded in the provenance ledger. | | Overflow/Underflow Guard | Prior to each matmul, a range‑check kernel validates that operand magnitudes lie within [1e‑38, 3.4e38] (FP32). Violations raise a PrecisionException and trigger automatic gradient clipping . | | Mixed‑Mode Support | While the default is full‑precision, developers can explicitly declare Critical Risks | Feature | Implementation Details |

The is a newly‑emerging, open‑source deep‑learning framework that combines full‑precision (FP32/FP64) training with a modular, graph‑based network definition language. Conceived in late‑2024 by a consortium of university labs (University of Tehran, MIT Media Lab, and the Institute of Advanced Computing, Singapore), KF‑FullNet aims to address three persistent bottlene‑cks in modern AI research: