Mitigating Large Language Model Hallucinations in Industrial Environments via Asymmetric Deterministic Override Architecture

“When deploying AI in rigorous industrial environments, we can never rely on adjusting prompts to achieve self-awakening. We must anchor an independent and immutable truth baseline for the physical world.” — Core philosophy of Project Laplace

Project Identity: Laplace of Clark
Author: Yi Zeng
Framework: Laplace (Asymmetric Dual-Oracle Alignment)
📄 Manuscript PDF: Read the Full Paper Here
📜 Project Manifesto: Read the Whitepaper Here
DOI: 10.5281/zenodo.20539607
GITHUB:Shinar-of-Clark/Laplace_Paper_Framework
Researchgate:Laplace_Paper_Framework

📖 Introduction

This repository contains the academic paper framework for Project Laplace. The paper deeply focuses on the “Epistemological Crisis” and hallucination issues faced by Large Language Models (LLMs) when applied to Cyber-Physical Systems (CPS), particularly in smart power grids.

📜 Project Manifesto (Whitepaper)

Alongside the academic paper, this repository includes the Whitepaper: Project Laplace Manifesto. This document serves as the initiation blueprint (Day Zero) for the project, detailing the broader engineering vision and business logic beyond just the academic theory. It explains:

  • The Core Crisis: Why LLMs inherently lack metacognition and how errors cascade in industrial settings.
  • The Engineering Solution: The philosophy of a “Deterministic Verification AI” utilizing lean RAG, human vetting, and unalterable ground-truth evidence (imagery, literature, URLs).
  • The Business Model: The “Private Construction, Shared Subscription” B2B alliance path, outlining how industrial giants can build sovereign data vaults and monetize verified technical truths.

🎯 Motivation & Background

As LLMs rapidly advance across various sectors, both academia and industry are attempting to integrate them into the operational management of industrial control systems and critical infrastructure (e.g., smart substations). However, we are confronting an unprecedented “Epistemological Crisis”:

  1. Lack of Metacognition in LLMs: AI cannot recognize its own physical blind spots. When encountering unknown industrial protocols or extreme edge cases, rather than “stopping to ask for help” like a human, it confidently fabricates false instructions (hallucinations).
  2. Cascading Chain of Errors: If traditional errors are not intercepted, LLMs will treat their own erroneous outputs as established facts for subsequent reasoning, constructing a disastrous decision chain that is logically consistent but physically absurd.
    Relying solely on Fine-tuning, RLHF/DPO, or complex Prompt Engineering cannot fundamentally eliminate the fatal flaws inherent in probabilistic models. Industrial environments do not need “probably correct” answers; they require absolute deterministic guarantees.

⚙️ Core Methodology

To place deterministic “shackles” on non-deterministic AI, this paper creatively proposes the ADOA (Asymmetric Dual-Oracle Alignment) architecture:

  • Asymmetric Verification: The AI is responsible for complex contextual reasoning and generation (high generation cost, divergent), while the hardcore industrial rule library is responsible for rapid, dimensionality-reduced verification of instructions (low verification cost, deterministic—similar to the asymmetry of NP problems).
  • Dual-Oracle Mechanism: The first oracle is the generalized knowledge weights of the LLM; the second oracle is an immutable truth verification vault independent of the AI’s training weights (based on international industrial standards like IEC 61850 and IEEE 1686, as well as the hard physical interlocking rules of relay protection devices).
  • Physical Baseline Interception & Closed Loop: Before instructions are issued to physical devices, they are forced through a digital twin or expert system acting as a “hard anchor” for arbitration. If a fatal hallucination is detected, the system will immediately intercept it and write this failed causal intervention record onto a distributed immutable ledger (blockchain). These records are then transformed into high-value “negative samples” for subsequent AI safety alignment training.

🛡️ Core Problems Solved

  • Neutralizing the Physical Destructiveness of Hallucinations: By strictly confining the destructive scope of LLM hallucinations within a digital sandbox and interception layer, ADOA completely blocks the possibility of semantic deception or common-sense errors penetrating the physical grid and causing blackouts or hardware damage.
  • Overcoming Limitations of Traditional Network Defenses: Traditional firewalls, Deep Packet Inspection (DPI), and Intrusion Detection Systems (IDS) can only defend against syntax-level and network-protocol-level cyberattacks. They are completely powerless against AI hallucinated instructions that are “syntactically standard, protocol-correct, but physically and logically absurd.” ADOA bridges this “semantic-physical gap.”

🏆 Main Academic Contributions

  1. Theoretical Innovation: Introduces the Simplex architecture (complexity control theory) from industrial control into the domain of LLM safety alignment, constructing the impenetrable ADOA paradigm.
  2. Closed-Loop Safety Lifecycle: Develops a full-lifecycle industrial LLM immune system spanning “Generation -> Interception -> On-Chain Recording -> Alignment Fine-Tuning.”
  3. Industrial-Grade Verification Paradigm: Deeply integrates real smart substation communication protocols (IEC 61850) and forward-lookingly introduces the design philosophy of Hardware-in-the-Loop (RTDS HIL) testbeds, providing a theoretical blueprint and future empirical pathway for the safe deployment of LLMs in critical infrastructure.
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