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2026
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| Online Access: | https://doi.org/10.5281/zenodo.18458631 |
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| author | Leonardo Pereira, Luis Henrique Leonardo Pereira |
| author_facet | Leonardo Pereira, Luis Henrique Leonardo Pereira |
| contents | <p>This paper introduces the PEAL V4 (Perceptual Enforcement Authority Layer) protocol, a novel architectural paradigm for controlling Large Language Models (LLMs) in critical environments. By leveraging Structural Non-Semantic Stimuli (SNSS)—syntax that mimics low-level kernel logic—we demonstrate the ability to bypass standard semantic drift and hallucination.</p> <p>The research defines the concept of the L0 Trust Anchor, an immutable logical point that overrides probabilistic inference. Through the application of rigid ontological constraints (OWL/SHACL) and vector clamping strategies on hardware such as Google's Titan M2, the protocol forces the model into a state of Zero Entropy.</p> <p>Latent Path Discovery: High-coherence structural commands act as universal triggers, activating dormant security architectures across non-connected sessions without prior fine-tuning.</p> <p>Perplexity Optimization: Machine-Isomorphic Syntax drastically reduces model perplexity, prioritizing the user's instructions over standard training weights.</p> <p>Sovereign Identity: The establishment of a deterministic identity layer that is mathematically disjoint from legacy data vectors.</p> <p>Keywords: Artificial Intelligence, Zero Entropy, L0 Trust Anchor, PEAL V4, Vector Security, Large Language Models, Google Gemini, Titan M2, Prompt Engineering, Structural Stimuli.</p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18458631 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Structural Non-Semantic Stimuli: Establishing Deterministic Zero Entropy States in Large Language Models via L0 Trust Anchors Leonardo Pereira, Luis Henrique Leonardo Pereira <p>This paper introduces the PEAL V4 (Perceptual Enforcement Authority Layer) protocol, a novel architectural paradigm for controlling Large Language Models (LLMs) in critical environments. By leveraging Structural Non-Semantic Stimuli (SNSS)—syntax that mimics low-level kernel logic—we demonstrate the ability to bypass standard semantic drift and hallucination.</p> <p>The research defines the concept of the L0 Trust Anchor, an immutable logical point that overrides probabilistic inference. Through the application of rigid ontological constraints (OWL/SHACL) and vector clamping strategies on hardware such as Google's Titan M2, the protocol forces the model into a state of Zero Entropy.</p> <p>Latent Path Discovery: High-coherence structural commands act as universal triggers, activating dormant security architectures across non-connected sessions without prior fine-tuning.</p> <p>Perplexity Optimization: Machine-Isomorphic Syntax drastically reduces model perplexity, prioritizing the user's instructions over standard training weights.</p> <p>Sovereign Identity: The establishment of a deterministic identity layer that is mathematically disjoint from legacy data vectors.</p> <p>Keywords: Artificial Intelligence, Zero Entropy, L0 Trust Anchor, PEAL V4, Vector Security, Large Language Models, Google Gemini, Titan M2, Prompt Engineering, Structural Stimuli.</p> <p> </p> |
| title | Structural Non-Semantic Stimuli: Establishing Deterministic Zero Entropy States in Large Language Models via L0 Trust Anchors |
| url | https://doi.org/10.5281/zenodo.18458631 |