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IndisputableMonolith.Papers.GCIC.LocalCacheForcing

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LocalCacheForcing proves J is strictly increasing on [1, ∞) and derives that access cost rises with graph distance, forcing local caches. Recognition Science researchers cite it when showing holographic scaling in brain models. The module assembles monotonicity lemmas on Jcost and phi powers, then applies graph rigidity to conclude caching_is_forced.

claimFor the J-cost function, $J(a) < J(b)$ holds whenever $1 ≤ a < b$. This implies that on a finite connected graph the ratio energy $C_G[x] = ∑ J(x_v / x_w)$ is minimized only when data is collocated, forcing local caching.

background

The module imports τ₀ = 1 from Constants and the J-cost definition from Cost. GraphRigidity supplies the key fact that ratio energy vanishes on a finite connected graph if and only if the field x is constant. Sibling lemmas then establish strict monotonicity of Jcost on [1, ∞) and that access cost increases with distance from the origin.

proof idea

The module is a chain of algebraic lemmas. Jcost_strictMono_on_Ici_one and Jcost_phi_pow_strictMono are proved by direct differentiation or ratio comparison using the closed form of J. These feed access_cost_increases_with_distance and collocation_minimizes_cost, which together yield the one-line wrapper caching_is_forced.

why it matters in Recognition Science

Supplies the local forcing step required by BrainHolography, which concludes that every local ledger region encodes global state and that accessible information scales with surface area. It closes the GCIC derivation chain from J-uniqueness (T5) through graph rigidity to holographic cache behavior.

scope and limits

used by (1)

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

depends on (3)

Lean names referenced from this declaration's body.

declarations in this module (13)