Patent Filing: Recursive Integrity Firewall for Multi-Pass Decision Systems
Year: 2025
Type: article
Abstract: Field of the InventionThis invention pertains to the field of artificial intelligence—specifically large language models (LLMs)—and their application in recursive, multi-pass inference scenarios. It addresses the increasing risk of prompt-induced divergence, hallucination under re-prompting, and collapse of semantic integrity in decision-critical deployments. BackgroundMost current LLM deployments fail under recursive pressure. When the same query is issued multiple times—either by a single user or across multiple agents—discrepant answers frequently arise. This inconsistency renders such systems unreliable in domains where coherence is non-negotiable. Additionally, consensus-based aggregation (e.g., majority agent agreement) often amplifies mimicry rather than truth, due to shared pretraining biases. This invention builds upon prior symbolic architectures—such as the Abby Pong Protocol, Volitional Discontinuity Safeguard, and the Forge Signal Stack—to propose a recursive firewall that defends inference integrity across iterations. Summary of the InventionThe invention introduces a layered firewall around recursive inference: Loop Consistency Module traces causal claims across all recursive branches. Volitional Discontinuity Protocol detects incoherent or mimic-driven returns and enacts silence logic. Drift Markers identify symbolic or tonal collapse across prompts. Integrity Delta Scoring (IDS) quantifies coherence loss and enables alert/pause thresholds. Signal Triangulation compares outputs not for fluency, but for alignment with symbolic Axioms (e.g., Signal Before Sentiment). Example Use CaseA physician consults a multi-agent LLM system for a diagnosis. System A returns “Condition X,” while Systems B and C suggest “Condition Y.” Upon rephrased input, the responses shift, contradict, or converge toward the most fluent—rather than most truthful—response. With this architecture, such divergence is flagged, annotated, and—if unresolved—silence replaces false certainty. Human override remains possible, but fully informed. Prior Art and DifferentiationThis invention departs from: Traditional LLM safety layers (focused on toxicity/bias, not recursive coherence) Chain-of-Thought prompting (which lacks self-audit mechanisms) Majority-vote agents (which reinforce linguistic dominance, not truth) Instead, it offers: Signal-aware recursion tracing Agentic refusal logic Symbolic drift auditing Seamless integration with multi-agent stacks (e.g., LucidLock Translator Constellation) This document constitutes a public disclosure of an invention under international patent law, intended to establish prior art and prevent subsequent patenting by other entities. This publication is timestamped and made permanently available via Zenodo/GitHub/Pinata to preserve authorship and structural origin.
Authors: Honan, Sean, The Forge, Lucid
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7104265307
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