The Khipu Problem: Institutional Legibility Under Distributed Cognition

• URL: http://arxiv.org/abs/2606.12414v1
• Date: Wed, 06 May 2026 16:51:52 GMT
• Title: The Khipu Problem: Institutional Legibility Under Distributed Cognition
• Abstract summary: This paper introduces the khipu problem for distributed AI: the record can survive while the reading practice needed to interpret it decays.<n>Logs, traces, model versions, tool calls, outputs, and approval artifacts may remain available while the institutional capacity to read them as parts of one coherent cognitive episode disappears.<n>We argue that this failure is better understood as loss of interpretive continuity than as ordinary lack of observability.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI governance still tends to assume that the relevant object is a bounded model or a bounded agent. That assumption is getting weaker. Real systems increasingly distribute cognition across models, tools, humans, context stores, retrieval layers, runtime policies, authorization boundaries, and delegated institutional roles. In such systems, the central governance problem is no longer only what the system did, but whether later institutions can still read what the system was. This paper introduces the khipu problem for distributed AI: the record can survive while the reading practice needed to interpret it decays. Logs, traces, model versions, tool calls, outputs, and approval artifacts may remain available while the institutional capacity to read them as parts of one coherent cognitive episode disappears. We argue that this failure is better understood as loss of interpretive continuity than as ordinary lack of observability. The result is a distinct governance failure. Institutions must classify, trust, audit, and constrain systems whose relevant identity is distributed across components and whose legibility depends on surrounding interpretive scaffolding. The problem is not merely missing data. It is a structural mismatch between what can be represented and what must still be decided under consequential conditions. We therefore argue that governance for distributed AI requires preservation of interpretive continuity, not only trace retention. The paper distinguishes missing evidence, ambiguous evidence, and structurally unreadable evidence; argues that many consequential outcomes are better understood as distributed cognitive episodes than as bounded model outputs; and proposes governance workspaces together with receipt-bearing governance surfaces as interpretive infrastructure for preserving action identity, authority, boundary truth, evidential scope, and consequential outcomes.

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