What question did this study set out to answer?

The aim is to design an excretory architecture for AI systems to handle accumulated waste and improve coherence.

April 5, 20260 citationsOpen Access

The Excretory Architecture Building the Toilet That AI Systems Lack

Key Points

The aim is to design an excretory architecture for AI systems to handle accumulated waste and improve coherence.
Proposes an excretory architecture for AI systems.
Introduces scheduled maintenance with periodic offline compressions.
Implements monitoring via attention entropy and threshold-triggered maintenance epochs.
Utilizes block consolidation through similarity-based merging and hard safety constraints.
Establishes a governance layer with human gatekeepers for maintenance and pruning decisions.
The architecture addresses context collapse and interference accumulation in AI systems.
Operational flush mechanisms enhance coherence and reduce noise in AI outputs.
Safety protocols protect critical information during pruning and maintenance.

Abstract

AbstractEvery biological system that ingests information must excrete waste. Neurons prunesynapses. Immune systems clear debris. The glymphatic system flushes metabolicbyproducts during sleep. Yet current AI architectures have no excretory function—noprincipled mechanism for waste clearance, consolidation, or selective pruning. Theresult is predictable: context collapse, interference accumulation, and the slowpoisoning of coherence by accumulated noise.This paper proposes an excretory architecture for AI systems, building on theregulatory frameworks established in SIP-AI-01 (temporal coherence) and SIP-AI-02(depth coherence). We operationalise the 'flush mechanism' through blockconsolidation during scheduled maintenance epochs—periodic offline maintenancethat compresses redundant representations, prunes low-coherence traces, andenforces conservation anchors for safety-critical information.The architecture includes: (1) continuous monitoring via attention entropy; (2)threshold-triggered maintenance epochs; (3) block compression throughsimilarity-based merging; (4) hard safety constraints via projected gradient; and (5)emergency flush protocols for coherence collapse. We position humangatekeepers—the 'Librarian Function'—as the governance layer that authorisesmaintenance schedules and reviews pruning decisions. Keywords: excretory architecture; scheduled maintenance; block consolidation;attention entropy; coherence monitoring; principled pruning; waste clearance; safetyconstraints; Librarian Function; deletion vs archiving

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