Project Genesis: A Cache-Resident Continuous Geometric Co-Processor for Efficient Language Model Inference

This paper introduces Project Genesis, a 0.51 MB continuous geometric co-processor designed to operate inline alongside standard pretrained language models with O(1) memory overhead and zero discrete token lookup tables.
Motivated by biomimetic principles of metabolic constraint and sparse geometric regularity, Project
Genesis applies orthogonality-constrained manifold projections and isometric state evolution in native
IEEE-754 double precision (FP64) to condition latent residual trajectories prior to token resolution.
We detail the systems engineering required to transition the co-processor from an interpreted Python
prototype—where host-device synchronization and kernel launch overhead imposed a 15.58 ms/token
penalty—to a fused, symbol-stripped C++/CUDA shared library (libgenesis_core.so) executing
in 0.46–0.57 ms/token within physical L2 cache on consumer NVIDIA Blackwell silicon. Under
compiled kernel execution, coupled host models demonstrated 16% to 30% reductions in inference
latency alongside commensurate gains in token throughput, while reducing wasted generation steps
on structured reasoning prompts.