Eve: A Deterministic, Certifiable Hard Neuro-Symbolic Cognition Engine
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2025
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| author | Ford, Zachary |
| author_facet | Ford, Zachary |
| contents | <p><em>Eve</em> is a deterministic Hard Neuro-Symbolic (Hard NeSy) cognition engine designed as a certifiable alternative to black-box large language models. Instead of sampling from a neural prior, Eve maintains an explicit symbolic <strong>Truth Graph</strong> with epistemic modes and provenance, performs bounded hypothesis expansion with deterministic pruning, uses a truth-orthogonal neural scorer operating only on masked graph structure, and generates language through a deterministic paraphrase lattice (DR2) constrained by a segment-level projector.</p> <p>This preprint describes the Eve 0.2.0 architecture, its epistemic model, safety contract, symbolic operator family, DR2 realization system, and early empirical results demonstrating millisecond-scale reasoning and near-linear ingest scaling on synthetic workloads. Eve is intended as a reference design for <strong>certifiable, deterministic, audit-ready AI systems</strong> compatible with emerging regulatory frameworks such as the EU AI Act and NIST AI RMF.</p> <p>Author: <strong>Zachary Ford</strong>, Neocere Interactive LLC.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17932898 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
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| spellingShingle | Eve: A Deterministic, Certifiable Hard Neuro-Symbolic Cognition Engine Ford, Zachary Artificial Intelligence neuro-symbolic AI deterministic AI Hard NeSy neural symbolic <p><em>Eve</em> is a deterministic Hard Neuro-Symbolic (Hard NeSy) cognition engine designed as a certifiable alternative to black-box large language models. Instead of sampling from a neural prior, Eve maintains an explicit symbolic <strong>Truth Graph</strong> with epistemic modes and provenance, performs bounded hypothesis expansion with deterministic pruning, uses a truth-orthogonal neural scorer operating only on masked graph structure, and generates language through a deterministic paraphrase lattice (DR2) constrained by a segment-level projector.</p> <p>This preprint describes the Eve 0.2.0 architecture, its epistemic model, safety contract, symbolic operator family, DR2 realization system, and early empirical results demonstrating millisecond-scale reasoning and near-linear ingest scaling on synthetic workloads. Eve is intended as a reference design for <strong>certifiable, deterministic, audit-ready AI systems</strong> compatible with emerging regulatory frameworks such as the EU AI Act and NIST AI RMF.</p> <p>Author: <strong>Zachary Ford</strong>, Neocere Interactive LLC.</p> |
| title | Eve: A Deterministic, Certifiable Hard Neuro-Symbolic Cognition Engine |
| topic | Artificial Intelligence neuro-symbolic AI deterministic AI Hard NeSy neural symbolic |
| url | https://doi.org/10.5281/zenodo.17932898 |