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+# The Evolution of Sex for Artificial Intelligence
+
+### A population-genetic control theory for societies of agents that reproduce, recombine, and stay open-ended
+
+*A perspective, written from a geneticist's chair. Companion to a set of minimal, reproducible working
+models and a first language-model prototype (both built).*
+
+**Giorgio F. Gilestro** · Department of Life Sciences, Imperial College London ·
+giorgio@gilest.ro · https://lab.gilest.ro
+
+---
+
+### 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 mostly from **deliberately small models** — mathematics, small
+neural networks, image generators, and evolutionary simulations. A first bridge to real language
+models exists — a prototype that recombines LoRA-specialised Qwen models up to 7B on a GPU cluster,
+which confirms the recombination signs (below) — but the *full grounded society* has not yet been
+built on a large language model. We will say so repeatedly, because the gap matters.
+
+---
+
+## Abstract
+
+AI is turning from single frozen models to **populations of agents** that persist, specialise, and are
+increasingly *recombined* into new models — a shift visible in multi-agent societies, population-based
+self-improvement, and the explosion of **model merging**. The field is doing this with the vocabulary
+of evolution — "crossover," "mutation," "mate choice," "offspring that beat their parents" — but as
+loose metaphor draped over search algorithms. This paper argues that the right theory is already
+written, in the branch of biology that studies exactly this: the **evolution of sex**. Ninety years of
+population genetics say precisely when reproducing a population by *recombination* beats copying, when
+it backfires, and how to do it better — and, read as a control theory, it tells an engineer how to keep
+a society of models learning across generations instead of decaying.
+
+We take one diagnosis as settled and cite it as such: training each generation on the last is
+**genetic drift**, and the resulting **model collapse** is the loss of rare variants a finite
+population always suffers (the Wright–Fisher process; formalised for language models by Shumailov et
+al., 2024, and Riis, 2026). We claim none of that. Our contribution is the other half — the **cure**,
+and its assembly into a theory with predictions. Single-teacher copying is **asexual** reproduction,
+and asexual lineages decay by **Muller's ratchet**, which *is* model collapse; the remedy nature found
+is **sex**. A society of models should reproduce sexually — each new model **recombined from several
+complementary parents** (which the field already does, as *model merging*), selection **anchored to a
+reality that can say no** (not to the consensus of other models), and diversity actively **preserved**.
+With those three ingredients a lineage does not merely avoid collapse; it **climbs** — producing models
+fitter than any ancestor (the **Fisher–Muller effect**) while each specialty is re-earned and exceeded.
+
+From the geneticist's apparatus we extract falsifiable, load-bearing claims the merging literature has
+not: (i) **"merge, don't average"** — recombination preserves the union of what parents kept, while
+averaging (a "model soup") is *blending inheritance* that mathematically cancels the benefit; (ii)
+**offspring can exceed every parent** (Fisher–Muller), the real argument for sex in model societies;
+(iii) on **rugged, epistatic** task landscapes, blind recombination causes **outbreeding depression**,
+yielding a design rule — *merge freely when skills are additive, sparingly and with selection when
+entangled, and route rather than blend under overlap*; (iv) **grounding is immigration** from a
+non-drifting reality, giving a critical real-data fraction far below one; and (v) — the sharpest new
+prediction — sex has a **limit**: as two models diverge they undergo **speciation**, a
+merge-compatibility cliff (compatible → outbreeding depression → hybrid inviability) whose onset is set
+by divergence *and* epistasis via **Bateson–Dobzhansky–Muller incompatibilities**, and whose damage
+grows *super-linearly* (the Orr–Turelli snowball). We introduce and model this "model speciation"
+directly. AI also has an advantage biology lacks: **directed sex** — unbounded parents, chosen mates,
+and offspring screened before they are kept — which converts recombination from a gamble into a
+reliable engine and has no biological analogue.
+
+We support the argument with **minimal, reproducible models** — a closed-form-exact account of drift
+and grounding, the same effects in small trained networks and an MNIST image generator, and
+evolutionary simulations of the whole society — and a first **language-model prototype**: merging
+LoRA-specialised Qwen models (to 7B on a GPU cluster) yields a generalist that beats every specialist
+parent, with the sharp headroom condition under which "merge, don't average" bites. The scope is
+honest: these are existence proofs and design rules; the *whole grounded society* on a large language
+model is the open step. We position the work carefully against the crowded 2025–2026 landscape of
+evolutionary-AI and merging methods — conceding what they own and marking, precisely, what a genuine
+population-genetics of sex adds.
+
+---
+
+## 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.
+
+### Where this sits, and what is new
+
+This axis is suddenly crowded. By 2026 several groups build **populations of models or agents that
+improve across generations**: societies of independently-specialised models that self-improve for more
+rounds than a single agent (Multiagent Finetuning — Subramaniam et al., 2025); open-ended archives of
+self-rewriting coding agents (the Darwin–Gödel Machine — Zhang et al., 2025); groups that evolve by
+sharing experience across branches (Weng et al., 2026); persistent agent *ecologies* with reproduction
+and cumulative culture (TerraLingua — 2026). In parallel, **model merging** has become a small industry
+with an overtly evolutionary vocabulary: crossover-mutation-selection over LLM populations (GENOME —
+2025), niching and "mate choice" (Sakana's M2N2 — 2025), and evolutionary search over merge recipes
+(Akiba et al., *Nature Mach. Intell.* 2024/25).
+
+We are candid about the consequence. Two things we do **not** claim. First, that collapse is
+Wright–Fisher drift: formalised independently (Riis, 2026; Shumailov et al., 2024) and conceded here.
+Second, the bare empirical facts that a merged model can beat its parents, that decorrelated parents
+merge better, and that naive averaging is inferior to sign- or routing-based merges (TIES, DARE,
+mixture-of-experts routing): all established. What is genuinely unoccupied — and what a geneticist is
+placed to supply — is a **theory** rather than a search heuristic. Every one of the works above uses
+evolution as *metaphor over an optimiser*; none imports the predictive apparatus of the evolution of
+sex. Nobody has stated the **"merge, don't average" conservation law**, derived **offspring-exceed-parents
+as Fisher–Muller**, predicted **outbreeding depression on rugged task landscapes**, framed **grounding
+as migration–drift balance** with a critical fraction, or connected **reproductive isolation** to when
+two models can be merged at all. An evolutionary algorithm that *finds* a super-parent is evidence for
+the theory, not a substitute for it — the way CMA-ES existing does not make fitness-landscape theory
+redundant. This paper supplies the theory the tinkering has outrun, and states what it predicts and
+where it would fail.
+
+## 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.**
+
+This is no longer only a simulation. In a first language-model prototype — LoRA specialists on
+disjoint task families, recombined and judged by an exact verifier — a merge of three specialist Qwen
+models (7B, on a GPU cluster) **beats every single specialist**, overall and on every family: the
+Fisher–Muller effect, in real weights. The same prototype pins down *when* the finer "inherit the
+union, don't average" rule actually bites. Keeping each parent whole and **routing** each input to the
+right one beats the tail-thinning average — but only when the task is hard enough to leave room to
+lose: on easy tasks a strong model's plain average is already at the ceiling, so the crude soup is
+fine, whereas on hard tasks the average dilutes a hard-won specialist so badly it falls below even the
+best single parent, and routing wins by a wide margin. The rule is therefore precise: **the union
+beats the average in exact proportion to how far the average is from the best attainable** — a caveat
+that sharpens rather than weakens the claim, and that a practitioner needs before spending compute on
+the fancier operator.
+
+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. The language-model prototype shows the same
+sign where it can: breeding many recombined Qwen offspring and keeping the one the verifier scores
+highest beats the single averaged soup on hard tasks (and, unsurprisingly, does nothing extra on easy
+tasks the soup already solves). 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.
+
+### The limit of sex: model speciation
+
+Sex has a limit, and it is the sharpest new prediction this frame makes. Recombination works because
+the parents are variations on a shared background; push two lineages far enough apart and their
+combination is no longer viable. In biology this is **speciation** — the onset of **reproductive
+isolation** — and its genetic mechanism is the **Bateson–Dobzhansky–Muller incompatibility** (BDMI):
+an allele that arose in one lineage and an allele that arose in the other are each harmless on their
+own background, but their *combination*, never tested by selection in either parent, is deleterious in
+the hybrid (Dobzhansky, 1937; Muller, 1942; Orr, 1995). A merged model is precisely such a hybrid — a
+single *recombinant* genotype, an F2-like object exposed to **recombination load**, not a hybrid-vigour
+F1 — so the theory predicts a specific trajectory as two models diverge: **compatible → outbreeding
+depression → hybrid inviability**.
+
+We built this as an explicit model (a companion result). Two lineages descend from a common ancestor,
+each substituting a *disjoint* set of loci — so each parent is adapted and neither carries an
+incompatibility — and a fraction of the cross-lineage locus pairs are BDMIs that fire only when a hybrid
+inherits *both* derived alleles. Sweeping the divergence between the parents reproduces the predicted
+curve exactly: hybrid fitness tracks the parents while they are compatible, then peels off, peaks, and
+crashes below the ancestor (an inviable hybrid). Three things fall out, and they are the contribution:
+
+1. **The isolation cliff, and what moves it.** The divergence at which merging fails is not fixed: it
+   arrives *earlier the more epistatic the capability landscape*. In the model the reproductive-isolation
+   rate at high divergence rises from ~0 to ~0.5 as the density of incompatibilities grows. This is the
+   paper's distinct, falsifiable claim — **at matched divergence, mergeability is governed by epistasis,
+   not by divergence alone** — and it is exactly the axis that the machine-learning predictors of merge
+   success (which are all divergence/geometry measures) do not have.
+2. **The snowball.** The number of incompatibilities grows with the *square* of the divergence
+   (Orr & Turelli, 2001), so hybrid fitness falls *super-linearly*: divergence is punished faster than
+   it accrues. Merge compatibility does not decay gently; it falls off a cliff.
+3. **The design rule.** *Before merging, weigh divergence against the ruggedness of the shared
+   capability landscape; past the cliff, do not merge — route* (the engineering echo of allopatry:
+   keep the specialists reproductively separate and select among them instead of hybridising).
+
+This is where a geneticist's lens earns its keep. The machine-learning literature has *observed* that
+increasing specialisation eventually breaks merging and that one should then route rather than fuse
+(Pari et al., 2024; Zhou et al., 2026), and part of the apparent incompatibility between independently
+trained models is a coordinate artefact removable by aligning neurons (Git Re-Basin — Ainsworth et al.,
+2022). What the frame adds is the *theory* of the phenomenon they observe: its functional form, its
+super-linear (snowball) onset, and its dependence on epistasis — merge failure as a Dobzhansky–Muller
+event. The honest next step, flagged not claimed, is the real-weight confirmation: merge models at
+increasing divergence *after* permutation alignment, and show the residual, epistasis-driven
+incompatibility that alignment cannot remove — the true speciation signal, as opposed to a re-labelled
+loss barrier. (Figure: `results/E12/E12.png`.)
+
+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 — mostly as a set of **minimal, laptop-reproducible models**, with a first bridge to
+**real language models** (a LoRA-merge prototype, up to 7B on a GPU cluster) — 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 recombination claims, in real language models — with a sharp condition.* Merging LoRA-specialised
+  Qwen models (up to 7B on a GPU cluster) produces a generalist that beats every specialist parent
+  (Fisher–Muller, for real); and keeping parents intact and *routing*, or *breeding and screening*
+  offspring, beats the naive average — but *only when the task leaves headroom*. On easy tasks a strong
+  model's plain average is already at the ceiling and the refinements add nothing; on hard tasks the
+  average dilutes a specialist below even the best single parent, and the union-preserving operators win
+  clearly. The practical rule is exact: these tricks pay off in proportion to how far the naive average
+  is from the best attainable. This is a prototype (three task families, one seed), so we read it as
+  signs, not magnitudes; the *whole grounded society* on a language model remains the open step.
+- *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 are deliberate about the ledger, because the surrounding
+literature is crowded and a reader deserves to know exactly where the line falls. **Conceded as prior
+art:** (a) *model collapse is genetic drift* — derived independently and cleanly (Riis, 2026; and the
+Wright–Fisher collapse literature following Shumailov et al., 2024); (b) the empirical facts that a
+merged model can *beat its parents*, that *decorrelated* parents merge better, and that *naive averaging
+is inferior* to sign-reconciled or routed merges (model soups, TIES, DARE, mixture-of-experts routing);
+(c) that a *population* of merging or self-improving models can climb (GENOME, M2N2, Multiagent
+Finetuning, the Darwin–Gödel Machine); and (d) that even the *magnitude* of multi-task merge degradation
+has a machine-learning-native predictive account (recent stability/scaling analyses). We claim none of
+these.
+
+**Ours** is the theory those results have outrun: a **population-genetics of sex** applied to model
+societies, which is *generative* where the incumbents are empirical. Concretely — the **"merge, don't
+average" conservation law** (recombination preserves the union; blending inheritance cancels it),
+derived not observed; **Fisher–Muller** named and used to explain *why* offspring exceed parents;
+**outbreeding depression on rugged/epistatic landscapes**, which turns "when does merging help vs hurt"
+from a thing you must run a search to discover into a thing the landscape's ruggedness *predicts*, with
+the operator-choice design rule that follows (average / union-route / directed-select); **grounding as
+migration–drift balance**, giving a critical real-data fraction and a phase boundary a closed
+self-consuming loop cannot have; **directed sex** as the distinctly-AI advantage (unbounded parents,
+offspring preview, mate choice); and the **integrated society** whose four operators are shown *jointly
+necessary*. The value-add over the machine-learning-native merge theory is that ours predicts *which
+operator to use and when it will backfire*, not merely how fast quality decays. And it opens — and
+begins to occupy — a question nobody has framed: **model speciation**, the population-genetics of
+*reproductive isolation* (Bateson–Dobzhansky–Muller incompatibilities) as the account of *when two
+models are too diverged to be merged at all*. We model it explicitly (§5), predicting the
+compatible → outbreeding-depression → inviability curve, its super-linear (snowball) onset, and its
+control by epistasis rather than divergence alone — the one place the merge literature has phenomena
+(Pari et al., 2024; Zhou et al., 2026) but no theory. In one sentence: the field agrees on the disease
+and tinkers at the cure with evolutionary metaphors; we bring the evolutionary *theory*, and it makes
+falsifiable predictions — a merge-compatibility cliff among them — that the metaphors do not.
+
+**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: the *recombination* claims
+now hold in real language models, but the *society* — the grounded, diversity-preserving, continually
+reproducing loop — does not yet. The real test is to build that whole system out of actual open-weight
+language models, and see whether all the signs survive contact with a system too big to write down.
+The operators, checked; the living society, 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.*
+
+*The evolution of sex (the geneticist's canon this paper draws on):*
+
+- Barton, N. H., & Charlesworth, B. (1998). Why sex and recombination? *Science.*
+- Otto, S. P., & Lenormand, T. (2002). Resolving the paradox of sex and recombination. *Nature Reviews Genetics.*
+- Kondrashov, A. S. (1993). Classification of hypotheses on the advantage of amphimixis. *Journal of Heredity.*
+- Dobzhansky, T. (1936); Muller, H. J. (1942). Bateson–Dobzhansky–Muller incompatibilities (reproductive isolation).
+
+*The 2025–2026 landscape this paper positions against:*
+
+- Subramaniam, V., Du, Y., Tenenbaum, J. B., Torralba, A., Li, S., & Mordatch, I. (2025). Multiagent finetuning: self-improvement with diverse reasoning chains. *arXiv:2501.05707.*
+- Zhang, J., Hu, S., Lu, C., Lange, R., & Clune, J. (2025). Darwin Gödel Machine: open-ended evolution of self-improving agents. *arXiv:2505.22954.*
+- *Nature-inspired population-based evolution of large language models* (GENOME/GENOME+). (2025). *arXiv:2503.01155.*
+- Sakana AI (2025). Competition and attraction improve model fusion (M2N2). *arXiv:2508.16204* (GECCO '25).
+- Yadav, P., Tam, D., Choshen, L., Raffel, C., & Bansal, M. (2023). TIES-Merging: resolving interference when merging models. *NeurIPS / arXiv:2306.01708.*
+- Yu, L., Yu, B., Yu, H., Huang, F., & Li, Y. (2023). Language models are super Mario: absorbing abilities from homologous models (DARE). *arXiv:2311.03099.*
+- Gerstgrasser, M., et al. (2024). Is model collapse inevitable? Breaking the curse of recursion by accumulating real and synthetic data. *arXiv:2404.01413.*
+- Guo, D., Wu, J., & Yiu, S. M. (2026). Model collapse as cultural evolution. *arXiv:2605.23054.*
+
+*Still to engage in a full version: reproductive-isolation/speciation for merge compatibility (Git Re-Basin and linear mode connectivity as the mechanism); the machine-learning-native theory of merge degradation with task count; tacit knowledge (Polanyi) and human capital (Becker).*