AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Hierarchical systems must revise their upper-level representations without discarding every useful lower-level alternative or carrying invalid evidence into a changed context. This paper develops an executable technical slice of the SHDA framework: an evidence lifecycle in which explicit dependencies determine what can...
Soft and Hard De-Attraction (SHDA) specifies when verified lower-level recovery can support an upper-level revision without changing the external success contract. This condition is the framework's central axis, contract-preserving correctability: the joint condition, over separately judged coordinates, under which ver...
Within Soft and Hard De-Attraction (SHDA), this paper treats intervention disclosure in persistent-state artificial agents as an observer-field-channel-time contract. It separates assignment, valid delivery, existence awareness and field knowledge, records missing measurement as UNKNOWN, and distinguishes disclosure-po...
Within Soft and Hard De-Attraction (SHDA), cross-level residual differences can arise from observation, translation, weighting and selection as well as state deviations. This paper separates residual values from routes that change future data and specifies model-conditional partial identification of residual sources wi...
Within Soft and Hard De-Attraction (SHDA), this paper separates the truth of operational objects from authority to act and specifies a quarantine, review and re-entry lifecycle for persistent-state artificial agents. Re-entry authority is conferred only at a linearizable commit that consumes the reservation and nonce;...
An internally consistent report can conceal a dangerous state, while an integrity alarm can occur without task failure. This paper links distortion monitoring in the Affective Gain Module (AGM) to recoverable information and useful action. The six-channel quadratic score and capacity correction remain conditional model...
A context-sensitive response curve can be well defined yet unidentifiable from its controller's observations, and an identifiable curve may not merit recalibration before a resource-limited task. This paper builds the terrain-contingent gain interface of the Affective Gain Module (AGM) on these distinctions. Version 1'...
A readable internal state need not be manipulable, and a manipulable state need not improve governance. This paper specifies the affective-state interface of the Affective Gain Module (AGM), separating readout, physical intervention, proposal, admitted action and feedback. Coordinate covariance identifies the transform...
A load-to-reserve quotient can mark a boundary without determining failure time or an executable rescue. Quotient Critical Geometry (QCG) specifies this reduced interface for the Affective Gain Module (AGM). Conditional reciprocal-drain results give crossing times, approximation error, omitted-drift bounds and a moving...
Adaptive systems can preserve a reserve yet lose access to a required change. AGM-CL couples response proposals, source-bound residual history, guarded model repair and executed action through a shared resource ledger and fixed task contract. Conditional one-step results separate response, information and affordability...
Version 2 proposed that sustained success raises rigidity, suppresses alarms and ends in silent failure. This version tests the links. Beyond Paper E, conditional results specify rigidity accumulation, positive never-alarm probability, closure of a paid recovery route and affordable maintenance. A finite reference, F-M...
Advanced AI ecosystems create a canonicity problem distinct from storage integrity and epistemic correctness: when no actor should own "truth," how can an ecosystem preserve what was recognized, when, and under what authority? This paper proposes a Custodian architecture in which canonicity is a scoped institutional st...
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