On premises repository based large language models (LLMs) provide commands to a parallel coelomic oncology robot based on real time sensor data that is controlled via x, y, z coordinates to administer patient treatment and minimize error. This thesis was first initiated through a five phase Claude Code workflow (create instructions, generate code, code execution, draft generation, and full paper production) yielding a 60 second 8 arm robotic pancreaticoduodenectomy on a hypothetical 2030 Medtronic PancreSpeed 1.0 platform paired with the Daraxonrasib pan KRAS inhibitor (FDA Breakthrough Therapy 06/25, RASolute 302 + RASolve 301). PancreSpeed 1.0 closes a 5x to 500x SOTA gap (1,200 mm/s tip, 100 kHz force, 0.05 mm RMS, 3 ms E stop, 18 N cumulative cap, 640 sensor channels). A 32 iteration deterministic Latin hypercube sweep at seed 20260513 yields mean composite 93.298 (std 1.225, 95% CI half width 0.462), and the 4 entrant LLM tournament places PancreSpeed 1.0 first at 93.735 versus 2030 da Vinci SP and Hugo RAS successors. The 1001 record Phase 5 first 100 ms sensor_sample_- 8arm.jsonl is an exceptional processing feat; Daraxonrasib postop restart distributes as T+7d in 29 of 32 iterations. Practical adoption still requires bridging the synthetic patient gap, hypothetical hardware gap, and TRIPOD+AI plus CREMLS reporting floors.