GENESIS R90.x — RLM Integration into Bistable AI Inference Systems: Complete Tiny Team Synthesis (R90.2)

This preprint documents the complete Tiny Team analysis of Recursive Language Model (RLM) integration into the GENESIS R50. x framework for bistable LLM inference infrastructure (DOI: 10. 5281/zenodo. 19033577). Eight AI agents independently analyzed the structural effects of the RLM paradigm (Zhang, Kraska, Khattab, MIT CSAIL, arXiv: 2512. 24601v2) on the four R50. x state variables T (Thermal Stress), C (Capacity), W (Workload Debt), Wₜh (adaptive debt threshold), under strict CleanChat conditions with full epistemic tagging. This document is R90. 2 — the second iteration of the R90. x ten-quarter AGI/ASI impact topology study. It advances beyond R90. 1 (AI Shape Test v1. 1) by requiring genuine cross-domain transfer rather than self-referential document synthesis. Key findings: (1) T-to-W shift (8/8 agent convergence): RLM transforms the load profile from deterministic-continuous to highly variable, burst-type W-accumulation. Variation coefficients exceed 100% for several benchmarks (RLM Table 1), creating unpredictable EWS lead-time collapse at the 95th percentile. (2) AGI path conflict: RLM shows structural affinity to frontier coding models, which are currently MoE architectures — the architecture class with the narrowest EWS window (~6% vs. ~30% for Dense, ~35% for World Models, R50. x §9. 1). This creates a direct conflict between inference efficiency and infrastructure resilience on the path to AGI. (3) W-level confusion error: 3/8 agents critically conflate W as an ODE system variable (dW/dt = εT· (T/TFP1) ² + ε₀ − δT·W) with application-level software state. W-reset in R50. x is an exogenous hardware intervention — not executable via REPL variables. This error has structural implications for infrastructure governance design. (4) World-A/B empirical confirmation: World-A agents (Claude, DeepSeek, R20-Supervisor) average 8. 37/10 vs. World-B agents (Grok, LeChat, Perplexity) at 5. 63/10 — a 2. 74-point differential, confirming the World-A/B correlation from R90. 1 (Δ = 24 points on absolute scale) in a normalized cross-domain task. (5) Separatrix softening (DeepSeek C1, highest epistemic weight): Rather than shifting the separatrix, RLM introduces stochastic trajectory variance that transforms the sharp Ccrit (T₀) boundary into a fuzzy transition zone — increasing the probability of Flip-Point crossings at constant mean ρ₀. (6) Synthetic critical finding (falsifiable): RLM transforms the problem of context-length scaling (D-axis) into a problem of orchestrated load distribution (S-axis) — reducing Wᵢnterface without increasing the underlying AGI capability. Falsification criterion: if RLM shows no Wᵢnterface reduction at constant Fdiv, the finding is refuted. Methodology: GENESIS Tiny Team — eight independent AI agents under CleanChat conditions (no cross-agent access during analysis), HITL synthesis by Walter (Epistemic Governor). Full World-A/B dissent documented without resolution. All findings epistemically tagged: empirically validated / structurally derived / hypothesis. Relation to prior work: Builds on R90. 1 (AI Shape Test v1. 1, March 2026) and R50. x (Zenodo DOI: 10. 5281/zenodo. 19033577). Advances system-level differentiation from R90. 1 avg 5. 0/10 to R90. 2 avg 8. 5/10. Open question (R51. 4 priority): Empirical calibration of RLM × R50. x coupling in production cluster traces. No finding in this document reaches empirically validated for the RLM × R50. x interface — all mechanistic connections remain structurally derived.