The library.

The fourth paper of the series names what the first three converged on — the two-moment description of a gauge-quotiented weight population — and tests it through a four-rung pre-registered ladder. The estimation floor is eight members, sign-gauge self-discovery included; at two, the honest fit renders geometric-novel functional clones that only a functional filter catches. Full-network basins end inside the population's own variance, bounding deep describability to adapter space; off seed-purity, describability survives with structure — rank is coverage, basin depth is an absolute task-conditioned budget, co-centered pools still fail by geometry, and one mis-anchored sign convention silently erases everything. Learning, in its third straight regime, buys steering and not reach.

The third paper of the series: fitted parametric families — an aligned mean plus per-block variances, no training, no learned generator — saturate a memorization-guarded audit at 2–3× the corpus spread and reach 0.781 at 4×, inverting the registered prediction: fresh draws from the described law beat the population's own rescaled residuals, which produce zero passers at depth. Beneath the result, the canonical chart's sign convention had split the population into coin-flip classes and hidden ~95% of the mean's energy, and the residual's realization is the initialization — training learns the law. The render-relevant description of this weight population is its aligned mean and one variance per matrix block.

The adapter-space sequel to Canonicalize, Then Generate. On a 1,024-adapter LoRA population, fixed recombination beats weight-space flows under a joint novelty-and-performance criterion; a 16-dimensional learned merge operator ties the incumbent while adding steering; and calibrated recombine-then-push operators populate the deep novelty frontier to 4x the corpus spread, falsifying a pre-registered thin-manifold prediction.

We put a seven-level reasoning-effort knob on a local AI gateway, made graded thinking budgets enforceable on llama.cpp-served models, and swept the curve on two models plus a judge-selection leg. A named level turns out to be a bet about a question's natural deliberation length: it only pays below that length, its meaning shifts between models, and small budgets are riskier than none at all.

Months of production serving, training, and tooling on a DGX Spark, distilled into the traps that caused real incidents: a unified memory pool where standard tools misreport, page cache that can kill model loads, and bandwidth — not capacity — as the true ceiling. Written for the code agents operating the box, with measured numbers over spec-sheet claims and a symptom→cause→fix table for when something is already on fire.

An audit of the canonicalize-then-generate recipe for neural-network weight generation: what quotienting permutation and scale symmetries actually buys under a memorization-guarded, novel-and-performant criterion — measured on three model-zoo rigs, with a division of labor between learned flows and canonicalized soups, and velocity extinction, a collapse law for small-zoo flow matching.

The Orb's open foundations, the coordination of a hive of agents, and the correlated-failure trap that undermines naive fleets.