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Toward a Compositional Knowledge Architecture for a Shared Organizational Cortex
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Andrew Miracle, Phyl Georgiou, Christine Okoth, Ese Monday

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ResearcharXiv cs.AI

Toward a Compositional Knowledge Architecture for a Shared Organizational Cortex

arXiv:2611.05753v1 Announce Type: new Abstract: Organizations accumulate evidence in conversations, documents, decisions and relationships, but they act on interpretations: judgements assembled from several observations, resting on assumptions, and revised as circumstances change. Existing retrieval, knowledge graphs and agent memory keep the records and lose the interpretation. We propose a four-layer architecture, Signals, Domains, Claims and Dimensions, in which interpretations persist with their justifications and standing, a truth-maintenance network revises that standing as evidence changes, and validated, versioned Dimensions position interpretations on named axes. We call the result a shared organizational cortex. In a registered retrieval experiment, the Dimension term gains 0.028 nDCG@10 (p = 0.002). In system-level comparisons, the full retrieval configuration improves nDCG@10 by 0.081 over Signals-only and 0.053 over graph-augmented Signals-only retrieval (both reported p < 0.001). The architecture gives organizations a persistent, revisable basis for shared judgement.

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This story was published by arXiv cs.AI and written by Andrew Miracle, Phyl Georgiou, Christine Okoth, Ese Monday. SyncAI.news shows a preview; the complete article is on the publisher's site.

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