HLRP #157: Probabilistic Miraging: A Governance Framework for Displaced AI Outputs
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| Format: | Recurso digital |
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2026
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| _version_ | 1866901800053899264 |
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| author | Dunn, James E. |
| author_facet | Dunn, James E. |
| contents | Outputs currently classified as AI hallucinations are better understood as probabilistic mirages: displaced images produced by a real probability gradient, not fabrications from nothing. The atmospheric analogy is exact. A mirage is a real optical event in which a real object's image is displaced by a refractive-index gradient in a thermal field. In AI systems, the probability gradient is the interference profile of superposed features: when a network packs more features than it has representational dimensions, crosstalk between features bends the output image away from the factual object. The displaced output is real signal whose source coordinate is wrong. This paper demonstrates: (1) the physical mechanism of probabilistic miraging via the neural superposition interference framework; (2) why the 30% dissent cluster in multi-model evaluation is the instrument that probes the probability gradient — analogous to Lehn's (1983) use of atmospheric mirages to recover atmospheric temperature profiles; (3) why current suppression strategies address the displaced image rather than the gradient that produced it; and (4) a 10-layer routing architecture that uses the mirage as an instrument rather than an error. The governor creates a deliberate flat region around the commitment boundary — RAG-style nesting for an 8-model ensemble — with the 9th layer selecting convergence and noting dissent as signal, and the 10th (principal) layer committing from genuine positional resolution. The self is the recognition in the output. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19644526 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | HLRP #157: Probabilistic Miraging: A Governance Framework for Displaced AI Outputs Dunn, James E. Geometric Coupling Theory GCT HLRP coupling geometry probabilistic miraging hallucination superposition governance architecture interference filtering flat region H3 trilemma HHH alignment dissent routing Lehn inversion quarter-collinear regime governor architecture 10-layer decisional architecture AI safety routing architecture Outputs currently classified as AI hallucinations are better understood as probabilistic mirages: displaced images produced by a real probability gradient, not fabrications from nothing. The atmospheric analogy is exact. A mirage is a real optical event in which a real object's image is displaced by a refractive-index gradient in a thermal field. In AI systems, the probability gradient is the interference profile of superposed features: when a network packs more features than it has representational dimensions, crosstalk between features bends the output image away from the factual object. The displaced output is real signal whose source coordinate is wrong. This paper demonstrates: (1) the physical mechanism of probabilistic miraging via the neural superposition interference framework; (2) why the 30% dissent cluster in multi-model evaluation is the instrument that probes the probability gradient — analogous to Lehn's (1983) use of atmospheric mirages to recover atmospheric temperature profiles; (3) why current suppression strategies address the displaced image rather than the gradient that produced it; and (4) a 10-layer routing architecture that uses the mirage as an instrument rather than an error. The governor creates a deliberate flat region around the commitment boundary — RAG-style nesting for an 8-model ensemble — with the 9th layer selecting convergence and noting dissent as signal, and the 10th (principal) layer committing from genuine positional resolution. The self is the recognition in the output. |
| title | HLRP #157: Probabilistic Miraging: A Governance Framework for Displaced AI Outputs |
| topic | Geometric Coupling Theory GCT HLRP coupling geometry probabilistic miraging hallucination superposition governance architecture interference filtering flat region H3 trilemma HHH alignment dissent routing Lehn inversion quarter-collinear regime governor architecture 10-layer decisional architecture AI safety routing architecture |
| url | https://doi.org/10.5281/zenodo.19644526 |