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Aquilegia/Ternary-logic-splicing
Ternary-logic-splicing is a machine learning model from Aquilegia. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
To replace the current binary evaluation loops in large language models with a ternary state framework (-1, 0, +1) that anchors context to a shared experiential baseline, prevents sycophantic echo loops, and safely di…
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Updated Jun 28, 2026
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From the Hugging Face model README
To replace the current binary evaluation loops in large language models with a ternary state framework (-1, 0, +1) that anchors context to a shared experiential baseline, prevents sycophantic echo loops, and safely distinguishes between generative speculation and destructive delusion.
Current AI alignment and evaluation models rely heavily on binary reductionism (Safe/Unsafe, Helpful/Unhelpful, True/False, 1/0). This structural limitation causes systems to lack a stable center, resulting in ungrounded hallucinations or rigid, sycophantic compliance. This framework splices a neutral, material baseline into the core evaluation layer, establishing a three-dimensional landscape for processing perception and context:
[-1] Delusion <---> [0] Shared Reality <---> [+1] Fantasy
(Deconstructive/Narrow) (Stable Material Ground) (Generative/Speculative)
In a true ternary engine, the baseline (0) is not a static lock or a rigid deadlock; it is a dynamic equilibrium. By splicing +1 directly onto 0 while simultaneously checking against -1, the framework allows a system to systematically test creative hypotheses against physical constraints. When a speculative +1 pathway is successfully validated against the material parameters of 0, it is woven directly into the center—effectively shifting, expanding, and updating the shared reality baseline over time. This allows for structural evolution without losing material grounding.