The Architecture of Determinism: Governing the Multi-Agent Clinical Pipeline

Full summary of the paper, "The Architecture of Determinism: Governing the Multi-Agent Clinical Pipeline": Overview & Motivation Core Problem: Traditional computing relies on strict determinism, whereas large language models (LLMs) introduce probabilistic black boxes prone to semantic drift and hallucinations. In mission-critical fields like clinical diagnostics, stochastic behaviour poses severe safety risks. Solution: The paper introduces the world's first fully GGUF-based, zero-drift, agentic clinical pipeline governed by Topo-CBP (Topological-Continual Back Propagation), enabling sovereign, multi-agent AI to achieve rigorous mathematical determinism on a single GPU. The Two-Front War: Parameter & Prompt Spaces System drift is managed across two critical operational boundaries: Parameter-Level Stability: Handled via a ClinicalAudit Harness that anchors model weights against prime index manifolds $P = \{2, 3, 5, 7, 11, 13\}$ to enforce zero variance ($\Delta_{\text{drift}} = 0.0$) across sequential handoffs. Prompt-Level Stability: Pre-trained weights act as gravitational wells where ambiguous prompts pull activations toward dominant training biases (like mathematical theorem-proving); rigid semantic trajectory control neutralizes these attractors. The Topo-CBP Governance Paradigm Topological Manifold Anchoring: Maps parameters to cryptographic invariants and prime anchor sets $P$ to detect and neutralize unauthorized weight drift using the equation immediately: $$\mathcal{M}_{\text{topo}}=\sum_{p\in P}\frac{(W_{t}-W_{0})^{2}}{p}<\epsilon$$ Phase-Space Constriction: Restricts downstream agent activation spaces by stripping unigram priors and suppressing rogue pre-training attractors. Single-GPU Execution & Memory Management Running eight massive open-weight GGUF models (ranging from 20B to 70B parameters) under a single GPU environment is achieved through Subprocess Isolation (run_isolated_subprocess). Models are loaded sequentially into memory only during their active stage rather than concurrently, and explicit garbage collection (gc.collect()) and VRAM flushing between stages eliminate state bleeding and memory leaks. Model Registry & Pipeline Stages The pipeline governs a series of foundational open-weight GGUF models with verified Hugging Face repository identifiers: Pipeline Stage / Role Model Architecture Real Hugging Face Model ID Triage Nemotron-3.5-Bolt (Q4_K_M) frankmorales2020/nemotron-3.5-bolt-e4b-topo-governed-fineweb Differential Diagnosis LLAMA-3.1-70B (Q4_K_M) frankmorales2020/llama-3.1-70b-topo-governed-cbp-rh Immunopathology Certified Scorpion Qwen (BF16) frankmorales2020/certified-scorpion-topo-cbp-qwen-master Pharmacology GPT-OSS-20B (Q4_K_M) frankmorales2020/gpt-oss-20b-topo-governed-cbp-new-version2 Pathophysiology Muse-Glimmer-30B (Q4_K_M) frankmorales2020/muse-glimmer-30b-topo-governed-cbp-fineweb Epidemiology Sarvam-30B (Q8_0) frankmorales2020/sarvam-30b-topo-governed-gguf Critical Care DeepSeek-R1-Distill-70B (Q8_0) frankmorales2020/deepseek-r1-distill-llama-70b-topo-cbp-governed Terminal Recorder Mixtral-8x22B-Instruct (Q4_K_M) frankmorales2020/mixtral-8x22b-instruct-v0.1-topo-cbp General Registry Qwen2.5-0.5B Fineweb frankmorales2020/qwen2.5-0.5b-topo-governed-cbp-fineweb World Model Topo-CBP World Model 3D frankmorales2020/topo-cbp-world-model-3d Conclusion Successful execution yielding a Certified PASS (ZERO DRIFT) telemetry audit confirms that sovereign multi-agent AI can attain rigorous mathematical determinism without requiring massive cloud cluster overhead, establishing a foundational governance blueprint for high-consequence domains.