Confidence-controlled plasticity

Read the equation

Adds a confidence-dependent gain to the base learning rate, then clips it between η_min and η_max. Ordered finite bounds and a well-defined confidence value are required.

Terms & definitions

The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.

How it works

1. Read η as the quantity being defined or updated by this relation.

2. Applies the stated minimum, maximum, or clamp. These bounds are part of the actual operation.

3. Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.

Assumptions & limits

Source recovery establishes that the expression appears in this record. Its mathematical validity, implementation behavior, and interpretation must be assessed separately. See the linked source and research notes for the checks available for this version.

Original context

MASTER EQUATION

φ(a)    (t⁺) = φ(a)    (t) + (1 - φ(a)    (t)) × Γ(a)(t) × π(eff)    (i,t)
f,s           f,s              f,s            f           f,s

Current state     Saturation headroom      Learning    Allocation weight
[0,1]             (diminishing returns)    drive       (where energy goes)

Learning Drive:  Γ = χ × η × κ
Activation:      χ = σ(α_κ·κ + α_Ψ·Ψ̃ - θ)     ← Continuous sigmoid, NOT binary Fibonacci
Plasticity:      η = clamp(η_base + η_gain·κ, η_min, η_max)

COMPLETE SYSTEM FLOW

EXTERNAL WORLD                           SEFERIM CORE                              PERSISTENCE

η = clamp(η_base + η_gain·κ, η_min, η_max)

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