<p class="has-ast-global-color-8-color has-text-color has-link-color wp-elements-48854ddd16bda4b0addbe6b12a4aa163 wp-block-paragraph"><strong>Project Identity:</strong><em> Laplace of Clark</em><br><strong>Author:</strong><em> Yi Zeng</em><br><strong>Framework:</strong><em> Laplace (Asymmetric Dual-Oracle Alignment)</em><br>Manuscript PDF:<em> </em><a href="https://shinarofclark.com/wp-content/uploads/2026/06/Laplace_Paper_Framework.pdf" target="_blank" rel="noreferrer noopener">Read the Full Paper Here</a><br>Project Manifesto:<em> </em><a href="https://shinarofclark.com/wp-content/uploads/2026/06/Whitepaper_Project_Laplace_Manifesto.pdf" target="_blank" rel="noreferrer noopener">Read the Whitepaper Here</a><br><strong>DOI:</strong><em> </em><a href="https://doi.org/10.5281/zenodo.20539607" target="_blank" rel="noreferrer noopener">10.5281/zenodo.20539607</a><br><em>GITHUB:</em><a href="https://github.com/Shinar-of-Clark/Laplace_Paper_Framework" target="_blank" rel="noreferrer noopener">Shinar-of-Clark/Laplace_Paper_Framework</a><br><em>Researchgate:</em><a href="https://researchgate.net/publication/405943504_Mitigating_Large_Language_Model_Hallucinations_in_Industrial_Environments_via_Asymmetric_Deterministic_Override_Architecture?_sg%5B0%5D=kAfO-WrE6MStMyR7q0ytUnrOgB_njzfovgdlup2IF7VNNzTGKSyv3PyF4YzC61F6sYLpUs8fspMJYNLI2L8ehWOF0c9Hm0-Ub5Sx393l.P348uEqwoBuHVlvj2ujYiXbP1kptT3E95SIhQqpnei6FU1Ir_VbFJzj0sEHsuEDtU0xrow9LIMgTJ0mBuDQO_Q&_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6ImhvbWUiLCJwYWdlIjoicHJvZmlsZSIsInByZXZpb3VzUGFnZSI6InByb2ZpbGUiLCJwb3NpdGlvbiI6InBhZ2VDb250ZW50In19" target="_blank" rel="noreferrer noopener">Laplace_Paper_Framework</a></p>
<p class="has-ast-global-color-8-color has-text-color has-link-color wp-elements-06c559c10dc68149541917de0a6c953e wp-block-paragraph">As Generative Artificial Intelligence deepens its application in the Industrial Internet of Things (IIoT) and power system operations, Large Language Models (LLMs) have emerged as critical components in Decision Support Systems (DSS). However, in safety-critical infrastructure such as power grid control and relay protection, LLMs acting as auxiliary decision sources face a fatal Metacognitive Deficit—the inability to recognize their own cognitive blind spots. This deficit frequently causes probabilistically fabricated hallucinated data (e.g., incorrect register mappings or misleading operational instructions) to propagate down to operational decision streams as valid facts. Consequently, human operators are easily misled into executing catastrophic actions.</p>
<p class="has-ast-global-color-8-color has-text-color has-link-color wp-elements-fb1870bc0047ece1788b42812e868e3e wp-block-paragraph">To address this challenge, this paper proposes an Asymmetric Deterministic Override Architecture (ADOA). This architecture logically decouples generative processes from a deterministic causal verification layer. The verification layer relies on a lightweight, expert-verified incremental Tri-Evidence Vault. By integrating BM25 term-frequency retrieval with Dense Vector Retrieval in a hybrid algorithm, the layer determines the causal compliance of LLM outputs within microseconds. Upon hitting a blind spot, the verification layer triggers an Override Veto to forcibly correct the recommendations provided to operators; if the blind spot is missed but a physical boundary violation is detected, a Safety Veto is triggered to intercept and block the dissemination of misleading information.</p>
<p class="has-ast-global-color-8-color has-text-color has-link-color wp-elements-cb8cbf37f28c3f2ad0cdbfd943978c78 wp-block-paragraph">Theoretical derivations and case studies demonstrate that, in substation protection and control scenarios demanding ultralow latency (e.g., the < 15ms standard of IEC 61850), the proposed architecture mathematically guarantees the deterministic interception and hard override of hallucinated instructions. This effectively blocks the propagation of erroneous advice to human operators, providing an insurmountable absolute defense for deploying Generative AI in high-security physical domains.</p>
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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
As Generative Artificial Intelligence deepens its application in the Industrial Internet of Things (IIoT) and power system operations, Large Language Models (LLMs) have emerged as critical components in Decision Support Systems (DSS). However, in safety-critical infrastructure such as power grid control and relay protection, LLMs acting as auxiliary decision sources face a fatal Metacognitive Deficit—the inability to recognize their own cognitive blind spots. This deficit frequently causes probabilistically fabricated hallucinated data (e.g., incorrect register mappings or misleading operational instructions) to propagate down to operational decision streams as valid facts. Consequently, human operators are easily misled into executing catastrophic actions.
To address this challenge, this paper proposes an Asymmetric Deterministic Override Architecture (ADOA). This architecture logically decouples generative processes from a deterministic causal verification layer. The verification layer relies on a lightweight, expert-verified incremental Tri-Evidence Vault. By integrating BM25 term-frequency retrieval with Dense Vector Retrieval in a hybrid algorithm, the layer determines the causal compliance of LLM outputs within microseconds. Upon hitting a blind spot, the verification layer triggers an Override Veto to forcibly correct the recommendations provided to operators; if the blind spot is missed but a physical boundary violation is detected, a Safety Veto is triggered to intercept and block the dissemination of misleading information.
Theoretical derivations and case studies demonstrate that, in substation protection and control scenarios demanding ultralow latency (e.g., the < 15ms standard of IEC 61850), the proposed architecture mathematically guarantees the deterministic interception and hard override of hallucinated instructions. This effectively blocks the propagation of erroneous advice to human operators, providing an insurmountable absolute defense for deploying Generative AI in high-security physical domains.
