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IndisputableMonolith.Information.LocalCache

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The LocalCache module supplies the core cost definitions for cache hierarchies in the Information domain of Recognition Science. It introduces totalAccessCost as the weighted sum of access frequencies times distances, together with cachedAccessCost and local_cache_benefit. Researchers analyzing J-cost flows on hierarchies cite these objects when setting up the gradient-descent argument. The module consists entirely of definitions with no theorems or proofs.

claimTotal access cost without cache is the weighted sum $C = ∑ f_i d_i$ of access frequencies $f_i$ and distances $d_i$. Cached access cost and local cache benefit are defined as the corresponding reductions when a local cache is present.

background

The module imports Constants, which fixes the RS time quantum τ₀ = 1 tick, and the Cost module that supplies the J-cost framework. It operates inside the Information domain and precedes the hierarchy analysis. Sibling definitions include totalAccessCost, cachedAccessCost, local_cache_benefit, fibonacci_recurrence, constant_ratio, and Jcost_symmetry_forces_geometric_boundary.

proof idea

This is a definition module, no proofs.

why it matters in Recognition Science

The definitions feed directly into IndisputableMonolith.Information.PhiHierarchyGrowth, whose doc-comment states that J-cost gradient descent on hierarchies necessarily converges to the Fibonacci/φ partition. The module therefore supplies the concrete cost objects required for that convergence argument.

scope and limits

used by (1)

From the project-wide theorem graph. These declarations reference this one in their body.

depends on (2)

Lean names referenced from this declaration's body.

declarations in this module (16)