OPERATIONAL

Quantum Sheaf Memory (B12)

Radiation-Immune Storage & Topological Braiding

The Multi-Node Consistency Crisis

In high-density sensory environments—such as drone swarms or satellite constellations—individual node failures often propagate errors through the entire network. These "local hallucinations" (caused by lens flare, sensor decay, or localized jamming) can mislead the global system, triggering dangerous avoidance maneuvers or mission-critical data corruption.

The Sheaf Consistency sub-system within the Trident Prime Engine applies the "Glass Box" philosophy to multi-node agreement. This technology—protected by U.S. Patent Application Nos. 63/940,736 and 63/983,021—maps the local observations of every sensor node to a global topological sheaf, identifying regions of data disagreement at the physical limit.

Governing_Interpretability: This physical consistency check allows the system to isolate faulty or inconsistent nodes within 2.4ms, ensuring that the global flight vector or data payload remains grounded in verified reality. The exact restriction maps are sequestered to prevent adversarial consensus spoofing.

Sheaf Agreement: Node Isolation [ACTIVE_SYNC]

                                MODE: RESONANT_SCAN

                                TARGET: SECTIONAL_AGREEMENT_LIVE

NODE_CONSISTENCY 98.4%

SHEAF_INTEGRITY_FEED

                                [TRIDENT] Auditing node agreement...

                                [ISED] Sheaf consistency: STABLE

                                [AUDIT] Global section verified.

Storage Density	1.2 PB / cm³
Rad-Hardened State	0.0% Error (Braided)
Bus Throughput	8.4 GB/s
Persistence	Active (Topological)

Technical Verification | Coherence Trapping

Standard data consistency (grey dashed) decays exponentially in flat storage architectures. Sensor consensus is lost within 40 time-steps under typical environmental noise (Σ=0.4).

Sheaf Memory (B12) uses Topological Consistency Locking to create metric wells that suppress noise by a factor of 1/(1+R). Results demonstrate a constant 6.0x stability gain, enabling high-fidelity state retention across distributed sensor networks in harsh environments.

TEST_ID: SHEAF_VAULT_V21.1 COHERENCE: >0.82 (T=100)

Technical Research Plan

1. Disjoint Domain Mapping

Implementing true Sheaf Restriction Maps where sensors cross-check only intersecting FOVs to prevent global "Consensus Spoofing".

2. High-Cohomology Alarms

Developing "Ambiguity Alarms" that trigger if no global section can be formed, transitioning from pruning to "Fatal Navigation Alerts".

3. Diversity Anchoring

Integrating non-spoofable anchors (B11 Holographic Persona) to verify if the majority agreement matches the global physical identity.

Data Integrity Value & Applications

Sheaf Consistency detects logical contradictions that are invisible to majority-vote systems, using cohomological obstruction classes as a mathematical proof of conflict:

Distributed Ledger Technology (DLT) — Detect and resolve consensus failures before they propagate through the chain
Multi-Agent Robotics — Ensure swarm-level data coherence when individual agents have conflicting sensor readings
Database Split-Brain Prevention — Identify partition-induced inconsistencies in distributed databases before they corrupt downstream queries

Failure Boundary: Sheaf analysis requires at least 3 independent data sources. With only 2 sources, the system cannot distinguish which source contains the contradiction.