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We present OIDA, a framework that structures organizational knowledge as typed Knowledge Objects carrying epistemic class, importance scores with class-specific decay, and signed contradiction edges. The Knowledge Gravity Engine maintains scores deterministically with proved convergence guarantees (sufficient condition: max degree $< 7$; empirically robust to degree 43). OIDA introduces QUESTION-as-modeled-ignorance: a primitive with inverse decay that surfaces what an organization does \emph{not} know with increasing urgency--a mechanism absent from all surveyed systems. We describe the Epistemic Quality Score (EQS), a five-component evaluation methodology with explicit circularity analysis. In a controlled comparison ($n{=}10$ response pairs), OIDA's RAG condition (3,868 tokens) achieves EQS 0.530 vs.\ 0.848 for a full-context baseline (108,687 tokens); the $28.1\times$ token budget difference is the primary confound. The QUESTION mechanism is statistically validated (Fisher $p{=}0.0325$, OR$=21.0$). The formal properties are established; the decisive ablation at equal token budget (E4) is pre-registered and not yet run.
| Comments: | 10 pages, 2 figures, 8 tables, 6 appendices |
| Subjects: | Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.4; I.2.6; H.3.3 |
| Cite as: | arXiv:2604.11759 [cs.AI] |
| (or arXiv:2604.11759v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2604.11759 arXiv-issued DOI via DataCite |
From: Federico Bottino [view email]
[v1]
Mon, 13 Apr 2026 17:31:14 UTC (23 KB)
[v2]
Fri, 22 May 2026 14:40:44 UTC (17 KB)
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