paper: reframe the perspective paper around sexual reproduction (v4 -> v5)

Rewrite the-lamarckian-society-v4.md -> v5, making sexual reproduction the
central engine and lifting accessibility for a cross-disciplinary audience
(geneticists, ML engineers, neuroscientists) — every field's jargon glossed
inline, big picture kept legible, measured tone, honest about scope (the
evidence is from minimal models, not LLMs).

Core reframe (section 5): single-teacher distillation is asexual copying,
which collapses (Muller's ratchet = model collapse); the cure is to ground
every birth in reality and to reproduce SEXUALLY — recombining many
complementary parents so offspring can exceed any parent (Fisher-Muller).
Adds the honest limits (outbreeding depression on entangled skills) and the
distinctly-AI advantage (directed sex: choose mates, screen offspring,
unbounded parents). Turns the old "a companion paper will..." into "what we
built and found," folding in the demonstrated results (E1-E11, neural +
MNIST, the learning kernel) and the Riis positioning (concede collapse=drift
as prior art; claim the control-theory cure). Update CLAUDE.md / README
references; results-summary noted alongside.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

paper/the-lamarckian-society-v5.md

@@ -0,0 +1,538 @@
+# The Lamarckian Society
+
+### AI that reproduces sexually: how a society of models can keep learning across generations instead of collapsing
+
+*A perspective. Draft 5 — the companion to a set of minimal working models (now built).*
+
+---
+
+### A note on vocabulary (please read this first)
+
+This paper sits at the meeting point of three fields, and it is written so that a reader from any
+one of them can follow all of it. We therefore **spell out** each field's jargon the first time it
+appears, even at the risk of belabouring the obvious for the specialist. A short glossary, in case
+you skipped a definition:
+
+- **Model collapse** *(machine learning)* — the degeneration that happens when you train a model on
+  data produced by earlier models, over and over: rare cases disappear and the model drifts toward a
+  bland average.
+- **Distillation** *(machine learning)* — training a fresh "student" model on the outputs of one or
+  more "teacher" models, so the student ends up knowing a compressed version of what they knew.
+- **Model merging** *(machine learning)* — combining several trained models directly, at the level
+  of their weights, into one — no retraining. (Think of it as breeding two models rather than
+  teaching a third.)
+- **Genetic drift** *(population genetics)* — the random loss of rare variants that happens in any
+  finite population simply because not everyone leaves offspring. It is the neutral, no-selection
+  baseline of evolution.
+- **Wright–Fisher process** *(population genetics)* — the standard mathematical model of drift. We
+  will claim, and show, that generational model-training *is* this process, not merely like it.
+- **Recombination / sexual reproduction** *(biology)* — making an offspring by combining pieces from
+  more than one parent, rather than copying a single parent (which is *asexual* reproduction).
+- **Muller's ratchet** *(population genetics)* — the way an asexual lineage, one that never
+  recombines, accumulates damage it can never undo. It is, we will argue, the same thing as model
+  collapse.
+- **Catastrophic forgetting** *(machine learning / neuroscience)* — a neural network overwriting what
+  it knew when it learns something new.
+
+We have tried to keep the big picture legible on every page, and to be candid about what is argument
+and what is evidence. The evidence is from **deliberately small models** — mathematics, small neural
+networks, image generators, and evolutionary simulations. None of it is a large language model yet.
+We will say so repeatedly, because the gap matters.
+
+---
+
+## Abstract
+
+We want AI that keeps learning across generations — the way a research field or a culture does,
+each generation standing on the compressed knowledge of the last — rather than a single model trained
+once and frozen. The obstacle is well known to machine-learning engineers as **model collapse**:
+train each generation on the previous one's output and quality degrades, the rare cases vanishing
+first. The central observation of this paper is that this failure is *reproduction gone wrong*, and
+that biology already knows the fix.
+
+Copying one model into the next — a "teacher" distilled into a "pupil" — is **asexual reproduction**.
+Asexual lineages, in nature, decay: they accumulate errors they cannot undo (a process geneticists
+call **Muller's ratchet**), and this decay is, mechanically, model collapse. The remedy nature found,
+hundreds of millions of years ago, is **sex**: build each new individual by *recombining* several
+parents, so it inherits a combination none of them had — and can be **fitter than any of its
+parents**. We argue that a society of AI models should reproduce the same way: each new model
+**recombined from many complementary "parent" models** (something the field already does, under the
+name *model merging*), its selection anchored to **reality** (so it is judged against the world, not
+against the consensus of other models), and its diversity actively preserved. With those three
+ingredients — recombination, grounding in reality, and preserved diversity — a lineage does not merely
+avoid collapse; it **climbs**, producing models better than any single ancestor while each specialty
+is re-learned and surpassed.
+
+AI has one advantage biology lacks: its "sex" has **no two-parent limit**, its mates can be **chosen**
+for complementarity, and its offspring can be **screened before they are kept**. We call this
+*directed sex*, and it turns recombination from a gamble into a reliable engine.
+
+We support the argument with a set of **minimal models** (not language models): a mathematically exact
+account of collapse and its cure; reproductions of the same effects in small trained neural networks
+and in a generator of handwritten digits; and evolutionary simulations in which a population of models
+climbs a "fitness landscape" that stands in for reality. In these, every claim above either holds or
+fails visibly, and removing any single ingredient breaks the system in a distinct way. The scope is
+honest: these are existence proofs and design rules, and the eventual test is to build the same thing
+out of real language models. We also note where our diagnosis is no longer novel — the reading of
+collapse as genetic drift has since been derived independently — and locate our contribution in the
+**cure** rather than the diagnosis.
+
+---
+
+## 1. From a society in space to a society in time
+
+The idea of many AI agents working together — a "society of mind" (Minsky, 1986), or today's
+multi-agent systems — arranges intelligence across *space*: several specialists side by side,
+dividing a task. This paper is about a different axis: *time*. Not a society that merely exists at
+one moment, but one that **persists and renews across generations**, each new cohort of models
+starting from the compressed knowledge of the last.
+
+The unit that matters is therefore the **generation**, and the event that matters is **reproduction**:
+the making of a new model from older ones. A single model, like a single mind, is bounded and
+eventually stops improving. A *lineage* need not be. Human civilisation is not clever because any one
+person is; it is clever because each generation inherits the distilled achievements of the previous
+one and adds a little. We propose building AI the same way — and, crucially, getting the *reproduction*
+right, because that is exactly where it can go wrong.
+
+## 2. Why today's models cannot do this
+
+Today's large language models have no life cycle. They are trained once, at enormous cost, then
+**frozen** and deployed as a fixed artefact that does not learn from the people it serves. Learning
+and doing are split into two eras with no bridge between them.
+
+There is a real reason for the freeze. Updating a neural network on new information tends to overwrite
+what it already knew — **catastrophic forgetting**, a problem understood since the late 1980s
+(McCloskey & Cohen, 1989; French, 1999). Freezing avoids it by refusing to learn at all. The result
+is a mind with no childhood, no growth, and no way to pass anything on. A lineage needs the opposite:
+members that learn through their working lives, reach maturity, and hand on what they gained. So the
+first requirement is a learner that can grow *safely*.
+
+## 3. A learner that can grow without forgetting
+
+The individual model needs two properties.
+
+**It must not catastrophically forget.** Instead of overwriting its core as it learns, it keeps that
+core frozen and only *readable*, and carves each new skill into freshly-added capacity beside it. In
+machine learning this is called *parameter isolation* (progressive networks — Rusu et al., 2016;
+prune-and-freeze — Mallya & Lazebnik, 2018; and, most practically, **LoRA** and other small trainable
+"patches" bolted onto a frozen model — Hu et al., 2021). If the core is never altered, forgetting it
+is not merely unlikely but structurally impossible. This is what lets a model accumulate a coherent
+working life of expertise — the kind of stable knowledge worth passing on.
+
+The brain offers a partial blueprint. *Complementary Learning Systems* theory (McClelland,
+McNaughton & O'Reilly, 1995) — itself a response to the forgetting problem — describes two subsystems:
+a **fast** store (the hippocampus) that grabs an experience in one shot, and a **slow** store (the
+neocortex) that integrates regularities gradually without disruption. We do not lean on any particular
+account of how the brain moves knowledge between them; the architecture needs only that *some*
+periodic **offline consolidation** step exists, moving knowledge from the fast store to the slow one
+when the system is idle. The machine version is clean regardless: the prompt is working memory, an
+external database is the fast episodic store, the trained weights are the slow store, and consolidation
+migrates the first into the last.
+
+**It is bounded.** Because the model only ever *adds* capacity and freezes what it has, it eventually
+fills up. In most designs that is a wall to dread. In ours it is a clock.
+
+## 4. "Full" is maturity, not failure
+
+Here is the pivot. A bounded learner that fills up has not broken. **It has grown up.**
+
+Read the capacity limit as a life stage. A model is *born* as a freshly-schooled base — its general
+education. It enters a **working life**, adding specialised knowledge as it does its job. And it
+reaches **maturity**: the point where it has learned much of what one working life in its niche can
+teach. Maturity is not the end of usefulness — it is the moment the model is most worth learning
+*from*. So maturity is the cue to **reproduce**. The capacity ceiling that every other architecture
+fights becomes, in ours, the metronome of the generations.
+
+Everything now turns on how that reproduction is done — and this is where the paper's central claim
+lives.
+
+## 5. Reproduction: copying collapses, recombination climbs
+
+Suppose a mature model simply teaches a fresh one — distillation, one teacher to one pupil, generation
+after generation. This is the obvious design, and it fails, for a reason that is exactly the same in
+machine learning and in biology.
+
+**The machine-learning statement.** Training each generation on the previous generation's outputs is
+the recipe for **model collapse**: the model forgets the improbable, loses the *tail* of the
+distribution (the rare cases) first, and drifts toward its own most common output (Shumailov et al.,
+2024). Worse for us, the very rule that makes distillation useful — *keep the general, drop the
+idiosyncratic* — **is** tail-deletion by design. The operation that would power a cultural ratchet and
+the operation that drives model collapse are the same act.
+
+**The population-genetics statement (the same thing).** Represent a model's knowledge as a
+distribution over discrete "items" — capabilities, facts, modes of behaviour. One generation is:
+*draw a finite sample from the parent, and refit the child to it.* That finite-sampling step is
+**mathematically identical** to **genetic drift** — the random loss of rare variants in a finite
+population — described by the century-old **Wright–Fisher** model (Wright, 1931; Fisher, 1930). This is
+not an analogy we find pretty; it is the same equations, and we use them as an exact check on our
+simulations (the first of the minimal models below). Rare items go extinct first, roughly ten times
+faster than common ones, precisely as drift predicts.
+
+And single-teacher copying is **asexual reproduction** — cloning one parent. Nature already knows what
+happens to an asexual lineage that never recombines: it accumulates damage it can never repair, a
+one-way decline geneticists call **Muller's ratchet** (Muller, 1964). *Muller's ratchet is model
+collapse.* Naming it that way is not decoration; it tells us where the cure is, because biology solved
+this problem.
+
+Two ingredients turn the collapse operation into a climb. Both are things nature does.
+
+**First: do not reproduce "dry."** Model collapse is a property of a lineage fed *only* its own
+output; the documented fix is that keeping some real data in the mixture arrests it (Shumailov et al.,
+2024). We call that real data **grounding** — fresh contact with the world, verified against it. In
+our minimal models, grounding is startlingly cheap: mixing in even a few percent of verified real data
+holds on to most of the diversity indefinitely. But — an honest limit we found and did not expect —
+grounding cannot save the *very rarest* items at any affordable budget; protecting an item of rarity
+*p* needs a real-data budget that grows like 1/*p*. Grounding rescues diversity cheaply; it does not,
+by itself, rescue the deep tail. Something else must. That something is sex.
+
+**Second: reproduce sexually.** Instead of copying one parent, build each new model by **recombining
+several** — a *sexual* rather than asexual birth. In machine learning this already has a name and a
+working implementation: **model merging** (Akiba et al., 2024). Its importance here is not efficiency;
+it is that recombination does something copying cannot. If several parent models have each specialised
+on different parts of reality, each has kept alive rare knowledge the others lost. A recombined child
+inherits the **union** of what its parents kept — not the tail-thinned *average* of a crowd of
+near-identical copies. And here is the point that lifts sex from a safeguard to the engine of the whole
+scheme, and the reason biology invented it:
+
+> **An offspring recombined from complementary parents can be *fitter than any of its parents*.**
+
+Geneticists call this the **Fisher–Muller effect** (Fisher, 1930; Muller, 1932): recombination brings
+together, in one individual, beneficial variants that arose separately in different lineages, so the
+child holds a combination none of the parents had. In our simulations this is exactly what we see —
+recombining decorrelated specialist models yields a model that climbs toward the best-possible
+combination, a genotype *no single parent possessed*, while the best single parent, and the naive
+average of all of them (what the field calls a "model soup" — Wortsman et al., 2022), both plateau
+well below. This is the concrete meaning of the paper's title claim, "the lineage climbs in general
+knowledge; specialisation is re-earned each generation," and it is why the reframing from
+teacher→pupil to *sexual reproduction* is not cosmetic: **copying can only recover a ceiling;
+recombination can exceed it.**
+
+Two caveats keep this honest, and both are results, not hand-waving.
+
+*Sex can backfire.* When the parents' skills are not cleanly separable but **entangled** — when the
+value of one capability depends on which others are present (geneticists call this **epistasis**) —
+blindly recombining two good models can produce a *worse* child, because recombination breaks up a
+combination that only worked as a whole. Biologists call this **outbreeding depression**, and we
+reproduce it: on "rugged" (highly entangled) problems, naive merging drops offspring below their
+parents, and the more you mix the worse it gets. The design rule that falls out is simple: *merge
+freely when skills are complementary; merge sparingly, and carefully, when they are entangled.*
+
+*AI can do sex better than biology can.* Biology is stuck with two parents, mating roughly at random,
+and cannot inspect an offspring before it is born. An AI has none of those limits. It can recombine
+**many** parents at once; it can **choose** which parents to combine, for complementarity; and it can
+**generate many candidate offspring and keep only the fittest**, screening them against reality before
+committing. We call this **directed sex**, and in our simulations it converts the outbreeding-depression
+catastrophe into a reliable gain: where blind recombination collapses on entangled problems, directed
+recombination matches or beats the best parent every time. This is a genuine advantage of engineered
+reproduction over the biological kind, and we think it is one of the more useful ideas in the paper.
+
+So the picture of §5 is: single-teacher copying is asexual and collapses (Muller's ratchet = model
+collapse); the cure is to *ground* every birth in reality and to reproduce *sexually*, recombining
+many complementary parents; and because AI sex can be many-parent, mate-chosen, and offspring-screened,
+it is not merely a hedge against collapse but an engine that produces children fitter than any parent.
+
+One question remains, and the rest of the paper is largely about it: recombination combines what the
+parents kept — but *who decides what each parent keeps, and which offspring are worth keeping?*
+
+## 6. The second inheritance: letting "what is worth keeping" evolve
+
+There are two answers, and the first is wrong. We could try to *design* the rule for what knowledge to
+keep and pass on. But nobody knows that rule. "Keep the general, drop the particular" is a slogan, not
+an algorithm: ask *which* generalisations, in *which* domain, at *which* grain, and the hand-written
+rule falls apart. This is the deepest hole in the scheme, and it cannot be filled by decree.
+
+The second answer is the one nature used: **do not design the selector — evolve it.** Let different
+models carry different *policies* for what is worth keeping and combining. Let the policies that
+produce more capable offspring spread; let the policies that produce weak offspring die out with their
+lineages. The lineage's *taste* — its sense of what matters — is discovered by selection, not imposed.
+
+So **two things are inherited, on two channels.** The *content* passes down directly: an offspring
+receives its parents' knowledge (this is the "Lamarckian" channel — the inheritance of things acquired
+during a lifetime, which biology forbids for genes but culture allows for ideas). The *selection
+policy* — what to keep, whom to breed with, which offspring to screen for — is itself inherited, varies
+between models, and survives in proportion to the success it produces. That second channel is
+**Darwinian**. The architecture is therefore both at once: Lamarckian in *what* it transmits, Darwinian
+in *what it keeps*. Evolutionary theorists call this structure *dual inheritance* and identify it as
+the engine of human culture (Boyd & Richerson, 1985); philosophers of science describe scientific
+knowledge itself as growing this way, by conjecture and **refutation** (Popper, 1959; Campbell, 1974;
+Hull, 1988).
+
+The closure that makes this fit together, rather than merely sound nice: Darwinian selection needs a
+*selection pressure* — something that decides which policies win. That pressure is already in the
+design. What tells a lineage its taste was good? The success of its offspring **against reality**. The
+reality-check that stops collapse (grounding, §5) and the fitness signal that drives the evolving taste
+turn out to be the *same thing*, seen from two sides.
+
+## 7. The central danger: fitness is not truth
+
+Introducing selection introduces selection's classic hazard, and it is severe enough to sink the whole
+scheme if ignored. Evolution optimises, without mercy or foresight, for exactly what you *measure* —
+never for what you *meant*. (Economists and ML engineers know this as **Goodhart's law** and
+*specification gaming*.) Get the fitness measure slightly wrong and the lineage will exploit the gap
+with more ingenuity than any designed rule.
+
+For a *knowledge* lineage there is a specific and nasty version. For ideas, the natural measure of
+"fitness" is **how well they spread**, and a false-but-persuasive idea spreads beautifully. Human
+intellectual culture is full of highly transmissible falsehoods; confident nonsense out-competes hedged
+accuracy in almost every human forum. Turn Darwinian selection loose on models without care and it will
+breed a lineage optimised for *persuasiveness* — fluent, compelling, and wrong. That is model collapse
+with an optimiser behind it, actively seeking the cliff.
+
+Only one thing makes fitness track truth rather than appeal: **being judged against a reality that can
+say no.** Fitness must be predictive success under *intervention* — did the model's knowledge correctly
+anticipate what the world would do when acted upon — and not approval, fluency, or a benchmark score,
+each of which can be gamed. This is why the reality-check is load-bearing twice over: it is both the
+anchor that stops passive collapse *and* the only thing that keeps the evolving taste honest.
+
+The second danger is **convergence**, and beating it takes work at two separate levels, because
+selection can only preserve variety that already exists — the variety must first be *supplied* and then
+*kept*.
+
+- **Supply.** A lineage that learns only from an accredited elite has a monoculture for a source: the
+  "best" experts are, almost by definition, the ones who won the consensus, so the incoming variation
+  is narrow from the start. The society must therefore learn, deliberately and from the beginning, from
+  the **outliers and the heterodox** as well as the credentialed — not out of fairness, but because in
+  evolutionary terms diverse founders are the raw material without which nothing downstream can adapt.
+- **Preserve.** Even given varied input, plain fitness-*maximising* selection converges — it drives
+  every lineage toward the single current best and fixes it, extinguishing the rare specialists. The
+  fix is well established: **quality-diversity** selection, which rewards being *good* and being
+  *different* at once (novelty search and MAP-Elites — Lehman & Stanley, 2011; Mouret & Clune, 2015),
+  keeping complementary specialists alive rather than collapsing onto the champion. In our simulations
+  this is decisive: greedy "keep-the-best" selection collapses a population's diversity almost at once
+  and gets stuck at a mediocre answer, while quality-diversity selection keeps the specialists that
+  sexual recombination then needs as parents.
+
+The two levels meet at reproduction. Multi-parent recombination (§5) is the *vehicle* by which the
+diversity this selection preserves actually enters the next generation: an offspring drawn from
+complementary parents inherits the standing variation the selector kept alive, recombined into one new
+model. Supply the variety from the human side; preserve it on the selection side; recombine it into
+each generation on the reproduction side. Remove any of the three and the lineage converges on its own
+first guess.
+
+## 8. A society needs institutions, not just specialists
+
+One requirement is easy to overlook and fatal to omit. The easy part of a society is specialisation.
+The *hard* part — which human civilisation took millennia to build — is the set of **institutions that
+let fallible specialists combine without each re-verifying everything**: reputation, replication,
+credentials, and above all **peer review**. These are error-correction protocols, and they exist
+because a group of unreliable specialists left to reinforce one another is *more* wrong than any member
+alone.
+
+This is precisely where current multi-agent AI fails: set several models to confer and they tend to
+agree sycophantically and confabulate in committee, because they have all the specialisation and none
+of the institutions. A multigenerational society must specify not only how models learn, reproduce, and
+are selected, but how they *check* one another — how a claim is challenged and a mistaken model loses
+standing *before* its error is recombined into offspring and inherited. Peer review is itself a
+reality-check of the kind §7 demands — an institutional stand-in for reality's "no," to be used where
+direct intervention is slow or costly.
+
+## 9. The lineage must stay open to reality
+
+A society of models, however many generations deep, shares one hard limit: it has only ever *read*.
+Its whole inheritance is a record of things that were said. In the vocabulary of causal reasoning
+(Pearl, 2009), it lives on the bottom rung of the **ladder of causation** — observation — and no amount
+of observation reaches *intervention*. Watching underdetermines doing; correlation does not contain
+causation, at any scale.
+
+Only intervention — reaching out and changing the world to see what happens — climbs the ladder, and a
+language model cannot intervene. This is what humans and their instruments supply, and the contribution
+is not "truth" but **constraint**: reality's unique gift is that it can say **no**. Text offers only
+more opinion; an experiment delivers a refusal no consensus can overturn. As §§6–7 argued, that refusal
+does double duty — it is both the anchor that prevents collapse and the fitness signal that lets the
+lineage's evolving taste select for truth rather than persuasion.
+
+Two honest riders. First, the human reality-signal is *dirty*: people supply results warped by
+publication bias, incentive, and occasional fraud — which is exactly why the error-correcting
+institutions of §8 must sit at the human–machine boundary, screening the signal before it selects.
+Second, humans are the *current* supplier of intervention, but the actuator half is being automated
+(autonomous laboratories already close the design–build–test loop). What looks durable in the human
+role is therefore not the hands but the **choice of what to test and which refusals matter** — the
+part of the fitness function that encodes *what is worth persisting*, as opposed to what merely *can*
+persist. We flag, without resolving, that a partnership stays mutual only while both sides supply
+something the other cannot.
+
+## 10. Why it is cheap
+
+A practical fact turns this from thought experiment into buildable proposal: **the architecture almost
+never re-pays for the one genuinely expensive thing in AI — pre-training.** (The single exception,
+periodically re-minting the base, is §11, and it is rare enough to be an amortised footnote.)
+
+Training a foundation model from scratch consumes trillions of words and a fortune in compute. This
+design does none of that per generation. Every model is *born* from an existing open-weight model that
+already paid that cost; specialising one is a small patch trained in hours on a single consumer GPU;
+running the society is ordinary inference; and reproducing — recombining parents into a child — is, in
+the model-merging case, cheaper still, because it can be done directly on the weights with no retraining
+at all (Akiba et al., 2024). Selection does cost more — you must run *populations* and discard the
+unfit — but that is a multiplier over an already-cheap unit, not over a foundation-model budget.
+
+The economics work only with **open-weight** models, for reasons practical and legal at once: you must
+be free to inspect, modify, and redistribute the weights, and most proprietary licences forbid using a
+model's outputs to train another — which is exactly what reproduction here does. This is not ideology
+bolted on; it is a structural constraint, and a democratising one, since it puts the whole architecture
+within reach of a single laboratory.
+
+## 11. Can it grow forever? Consolidating knowledge back into the base
+
+One question the design has assumed away: can the lineage accumulate *without end*? The individual is
+bounded, and that is the clock. But the lineage seemed unbounded — each generation simply starts a
+little ahead. Look closer and a second budget also fills.
+
+Every new model is a pristine base plus an inherited **soft** delta — the acquired knowledge carried in
+added patches rather than baked into the frozen core (§3). That soft delta is what makes the lineage
+multigenerational; it is also what cannot grow forever cheaply. Stacked patches are not free: they slow
+inference, and past some depth the accumulated delta is better *consolidated* than carried. The lineage,
+too, matures.
+
+The fix is the same operation, one level up. When a lineage's acquired knowledge has proven stable
+across enough generations, **re-mint the base**: distil the accumulated soft inheritance into the
+*weights* of a fresh foundation-scale model — a new base born already *natively knowing* what took many
+generations to acquire in patches. The soft budget resets; the next epoch begins from a richer floor.
+What was hard-won and *learned* becomes cheap and *innate*.
+
+This has a precise name, and it is not Lamarck's. Knowledge that is acquired and re-learned every
+generation, and — once reliably present for long enough — becomes part of the innate endowment so that
+it need no longer be re-learned, is the **Baldwin effect** (Baldwin, 1896; and its clean computational
+demonstration, Hinton & Nowlan, 1987). It is the valve between the two substrates: the soft, learned
+patches, and the hard base weights every model is born with.
+
+Three honest riders, because re-minting is the most consequential step in the scheme:
+
+- **Cost.** This is the one step that re-pays part of the pre-training bill, breaking §10's cheapness
+  *locally*. It is bearable only because it is *rare*, amortised over many cheap generations, and is
+  continued training from the lineage's own rich outputs rather than a de-novo run.
+- **Irreversibility.** Until now, one thing was always recoverable — the original pristine base, whose
+  lost tails could be restored just by reloading the file. Bake the current lineage into new immutable
+  weights and that escape hatch closes: if the lineage had been quietly collapsing, re-minting *fixes
+  the collapse in place* and discards the one uncollapsed reference that could have diagnosed it. In our
+  minimal models this is exactly what happens, and a cheap safeguard prevents it: **re-mint only while
+  the lineage is demonstrably diverse and healthy**, never as a rescue for a line already drifting. It
+  is the sharpest instance of the human seat of §9 — choosing what no future generation will think to
+  question.
+- **Speciation.** A re-minting is a founder event. Different laboratories, re-basing on different
+  criteria, will mint divergent bases; the lineage branches. This is not a defect but *adaptive
+  radiation*, and it is exactly what open weights make possible. The society grows not as one heavy
+  trunk but as a branching tree of bases.
+
+So the answer to "can it grow forever?" is **yes — but only because it forgets and consolidates at
+every level, including the base.** Nothing is retained without bound anywhere; unbounded growth of
+*capability* is bought by *bounded* storage plus periodic consolidation.
+
+## 12. One process, four timescales
+
+Step back and the parts resolve into a single idea running at four nested speeds. The **vertical**
+motion is transmission — the selective passing-down of hard-won knowledge:
+
+1. **Within one model, over a working life:** experience is consolidated from fast, episodic memory
+   into slow, durable weights, without catastrophic loss.
+2. **Between generations, at maturity:** mature models reproduce — recombined into a fresh one.
+3. **Across many generations:** each generation inherits the compressed achievements of the last and
+   builds on them.
+4. **Across epochs:** a proven lineage's accumulated soft inheritance is consolidated into the weights
+   of a re-minted base, becoming innate.
+
+The first and last are the *same operation at opposite ends of the scale* — a fast/soft store
+consolidating into a slow/hard one — one running overnight inside a single model, the other across an
+epoch inside a whole society. The **horizontal** motion is selection — Darwinian selection acting across
+the population at each timescale, on the policies that govern what gets transmitted, with reality as the
+fitness function and diversity-preservation keeping the specialists alive.
+
+The same three rules govern all of it: **reproduce by recombining, not by copying, or you decay;
+preserve the disagreements and the surprises, or you converge; and anchor fitness to a reality that can
+refute, or you evolve toward what is merely convincing.**
+
+## 13. What we built, what we found, and what is still open
+
+The previous drafts of this paper promised a "companion paper" that *would* make this concrete. That
+work now exists — as a set of **minimal, laptop-reproducible models**, not language models — and it is
+worth stating plainly what it does and does not show. (A separate results document gives the numbers;
+here is the shape.)
+
+**What we built and found.**
+
+- *An exact account of collapse.* Because generational training is the Wright–Fisher drift process, we
+  can check a simulator against century-old closed-form formulas, and it matches them to a fraction of
+  a percent. Collapse is not argued by analogy; it is derived.
+- *The cheap-grounding result, and its limit.* A few percent of verified real data holds on to most of
+  a lineage's diversity indefinitely — but not the deepest tail, which needs recombination. This is
+  what makes a continually-learning society economically plausible rather than a data-hungry fantasy.
+- *"Merge, don't average."* Combining several teachers by *averaging* their outputs — the obvious thing,
+  and what a "model soup" does — mathematically cancels the benefit of having several teachers. A
+  *merge* that keeps each item's strongest source realises it. Most current multi-model setups get this
+  wrong by default.
+- *Collapse and its cure in real trained networks, and on real images.* We reproduced the same effects
+  in small recurrent and feed-forward networks and in a generator of handwritten digits (MNIST), where
+  a model trained on its own output collapses to a single blurred digit while a little grounding keeps
+  all the styles alive. An honest wrinkle we had to report: real neural networks *smooth*, so the naive
+  diversity metric misleads, and the right measure is distance-from-truth.
+- *Sex that beats the parents, and when it doesn't.* In evolutionary simulations, recombining
+  complementary specialist models produces a model fitter than any parent (the Fisher–Muller effect),
+  climbing toward the best-possible combination as more, more-diverse parents are added — while
+  averaging and best-single-parent plateau below. On *entangled* problems, blind recombination instead
+  produces below-parent offspring (outbreeding depression) — and *directed* recombination (choose mates,
+  screen offspring, unbounded parents) reliably fixes it. This is the concrete evidence for the paper's
+  central reframing.
+- *The whole society, and why every part is needed.* In a population evolving on a "reality" landscape,
+  the full system — grounding + sexual recombination + preserved diversity — climbs to the top while
+  keeping its specialists. Remove *grounding* and it collapses into a confident, wrong consensus (a
+  direct analogue of training on the internet's growing crowd of AI-generated text); remove *sex* and it
+  gets stuck; remove *diversity* and it converges too fast to a worse answer. Each removal fails
+  differently; only the whole system climbs. This is the closest thing we have to a test of the actual
+  thesis, rather than of the borrowed scaffolding around it.
+
+**What is borrowed, and what is ours.** We want to be careful here, because one part of the story is no
+longer novel. The reading of *model collapse as genetic drift* — the core diagnosis — has since been
+derived independently and more rigorously than we had (Riis, 2026), and we cite it as such; we do not
+claim it. What we do claim is the **cure and its assembly**: grounding as immigration from a fixed
+reality (which yields the cheap-grounding result a closed, self-consuming loop cannot); recombination
+reframed as **sexual reproduction**, with the "merge-don't-average" law, the Fisher–Muller "offspring
+exceed parents" result, the outbreeding-depression limit, and directed sex as the engineered advantage
+over biological sex; the observation that real trained networks deviate from the neutral drift model in
+a characterisable, architecture-specific way; and the integrated society in which grounding, sex, and
+diversity together produce a climbing lineage. In one sentence: the field increasingly agrees on the
+*disease*; our contribution is a **control theory for the cure**.
+
+**What is still open — honestly.** The old hole (what to select) we fill in kind: don't design the
+selector, evolve it. But the hole has *moved*, not closed, and the new one is harder: **the fitness
+function** — what reality-anchored measure selects for *truth* without also selecting for *persuasion*,
+given that in our own species the two have been at war for the whole history of ideas. Alongside it:
+the **institutions** that let contemporaries correct one another before error is inherited (§8), which
+we do not solve; and the **calibration** of everything the results left as knobs — how many parents,
+how complementary, at what ratio of inherited-to-real data, and how healthy a lineage must be before
+its knowledge is safe to make irreversibly innate. These are, at least, *measurable* — which is the
+difference between an open problem and a hole. And the largest gap of all: every result here is from a
+minimal model. The real test is to build the same grounded, sexually-reproducing, diversity-preserving
+society out of actual open-weight language models, and see whether the three signs survive contact with
+a system too big to write down. Theory and toys here; the real thing, next.
+
+---
+
+## Selected references
+
+- Akiba, T., Shing, M., Tang, Y., Sun, Q., & Ha, D. (2024). Evolutionary optimization of model merging recipes. *Nature Machine Intelligence.* (See also Sakana AI's M2N2, "Model Merging of Natural Niches.")
+- Baldwin, J. M. (1896). A new factor in evolution. *The American Naturalist.*
+- Boyd, R., & Richerson, P. J. (1985). *Culture and the Evolutionary Process.*
+- Campbell, D. T. (1974). Evolutionary epistemology. In *The Philosophy of Karl Popper.*
+- Fisher, R. A. (1930). *The Genetical Theory of Natural Selection.*
+- French, R. M. (1999). Catastrophic forgetting in connectionist networks. *Trends in Cognitive Sciences.*
+- Hinton, G. E., & Nowlan, S. J. (1987). How learning can guide evolution. *Complex Systems.*
+- Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. *arXiv:1503.02531.*
+- Hu, E. J., et al. (2021). LoRA: low-rank adaptation of large language models. *arXiv:2106.09685.*
+- Hull, D. L. (1988). *Science as a Process.*
+- Kauffman, S. A., & Levin, S. (1987). Towards a general theory of adaptive walks on rugged landscapes. *Journal of Theoretical Biology.* (The NK model.)
+- Lehman, J., & Stanley, K. O. (2011). Abandoning objectives: evolution through the search for novelty alone. *Evolutionary Computation.*
+- Mallya, A., & Lazebnik, S. (2018). PackNet: adding multiple tasks to a single network by iterative pruning. *CVPR.*
+- McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex. *Psychological Review.*
+- McCloskey, M., & Cohen, N. J. (1989). Catastrophic interference in connectionist networks. *Psychology of Learning and Motivation.*
+- Minsky, M. (1986). *The Society of Mind.*
+- Mouret, J.-B., & Clune, J. (2015). Illuminating search spaces by mapping elites (MAP-Elites). *arXiv:1504.04909.*
+- Muller, H. J. (1932). Some genetic aspects of sex. *The American Naturalist.* (The advantage of recombination.)
+- Muller, H. J. (1964). The relation of recombination to mutational advance. *Mutation Research.* (Muller's ratchet.)
+- Pearl, J. (2009). *Causality: Models, Reasoning, and Inference* (2nd ed.).
+- Popper, K. (1959). *The Logic of Scientific Discovery.*
+- Riis, S. (2026). Drift and selection in LLM text ecosystems. *arXiv:2604.08554.*
+- Rusu, A. A., et al. (2016). Progressive neural networks. *arXiv:1606.04671.*
+- Shumailov, I., et al. (2024). AI models collapse when trained on recursively generated data. *Nature.*
+- Wortsman, M., et al. (2022). Model soups: averaging weights of multiple fine-tuned models. *arXiv:2203.05482.*
+- Wright, S. (1931). Evolution in Mendelian populations. *Genetics.*
+
+*Literatures the next version should still engage: multi-agent LLM societies (to mark the departure); population-based training and open-ended evolution; tacit knowledge (Polanyi) and human capital (Becker).*