FDRP

Planning for complex systems lacks the convergence metrics and defect detection that manufacturing achieved through six decades of statistical process control. FDRP addresses this gap by treating each planning decision as a manufactured artifact with measurable convergence and gate-reviewed quality, applying SPC, 5S, RAMS, Andon, and configuration freeze directly to planning. Applied across 70 production runs (1,701 decisions, 44 reached convergence) with 32 active subsystems, the system's strongest empirical contribution is the antimatter building programme: 68 LLM-generated expert specialists across two rounds produced 28,936 lines of analysis and 1,656 structured findings, discovering expert persistence as an emergent operational pattern. Cross-model verification with 3 independent LLMs revealed 4 systematic bias patterns, suggesting that N≥3 models are needed for blind spot detection. In April 2026 the platform completed R1027 “FDRP-on-FDRP”—the architecture applied reflexively to its own evolution, converging with CVT 0.000 and RAMS 0.953. The architecture has since grown to 846 database tables, 253 views, 81 system rules, and 668 knowledge index entries. We propose progressive disclosure as the unifying architecture—a hypothesis under test. By the end of April 2026 the platform grew to 846 database tables, 253 views, 101 skills, 54 active agents, and 18 concept-to-X pipelines. Run #1028 then re-validated the methodology against the new Claude Opus 4.7 model: 4 of 6 tested HIGH-priority methods survived with equal or sharper output, 0 outright content regressions, and no methods retired.

16 April 2026 — Run #1027 “fdrp-on-fdrp-v2” applied the Fractal Diamond Review Protocol reflexively to its own continued evolution and CONVERGED in a single day. The run closed with CVT 0.000 (zero velocity tension, every axis stable), convergence score 1.0000, and a RAMS verdict of PASS (overall 0.953 — Reliability 0.978, Availability 1.000, Maintainability 0.913, Safety 0.922). The pipeline was directed to execute “30 waves of megaexpert expansion, n-fractal views, n-fixes and fine-tuning”, fed its own corpus back into the expert panel, and self-converged to a stable fixed point. This is the first publicly documented case of the methodology auditing and improving itself under its own gates.

20 April 2026 — Claude Opus 4.7 (GA 2026-04-16) introduced three breaking API changes (temperature/top_p/top_k rejected, thinking.budget_tokens removed, assistant prefill removed), a new tokenizer with 1.0–1.35× token inflation on the same text, and a behavioural shift toward more literal instruction following. Run #1028 ran the full FDRP method catalogue against 4.7 head-to-head versus 4.6 to measure the impact.

Net verdict: NEUTRAL with selective IMPROVEMENT. Of 6 HIGH-priority methods tested head-to-head, 4 produced clean paired output, 3 improved on 4.7 (Six Thinking Hats, Bridges, Triage Cascade), 1 was structurally equivalent (Fractal-Out S0–S6), 1 broke on 4.7 only (DDⁿ — completion timeout, not content regression), and 0 methods were retired. Average 4.7 output is ~12 % shorter but carries more domain-specific citations per word — denser, not dumber.