MachineSex/paper/the-evolution-of-sex-for-ai.md

# The Evolution of Sex for Artificial Intelligence

### A population-genetic framework 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. Our
  minimal model of knowledge transmission *is* this process exactly; a real trained network is this
  process plus a measurable, architecture-specific bias we quantify.
- **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. We will argue it is the right lens for the
  *irreversible* part of model collapse — the capabilities that, once lost from every parent, no
  merging can rebuild.
- **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 a rich, quantitative body of
applicable theory already exists in the branch of biology that studies exactly this: the **evolution
of sex**. Ninety years of population genetics analyse when reproducing a population by *recombination*
beats copying, when it backfires, and how to do it better — and, read as an engineering framework, it
supplies overlooked variables and testable design rules for keeping a society of models learning
across generations instead of decaying. The underlying shift of perspective is the contribution we
most want to land: **treat multigenerational model populations as systems whose inheritance,
diversity, and compatibility must be managed — not merely as collections of models to optimise.**

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 on the remedy side. Single-
teacher copying is **asexual** reproduction, and the irreversible arm of its decay corresponds to
**Muller's ratchet** (a correspondence we state with its scope, not as identity); the remedy biology
found for the ratchet 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**. In our models — from closed-form to trained networks to a
language-model prototype — those three ingredients together let a lineage not merely avoid collapse
but **climb**, producing models fitter than any ancestor (the **Fisher–Muller effect**) while each
specialty is re-earned and exceeded; whether the full recipe holds at frontier scale is the open
question the framework is built to test.

From the geneticist's apparatus we extract falsifiable, load-bearing claims (each stated with its
operator and scope in the text): (i) **"merge, don't average"** — a conservation result: refitting a
child to the *mean of its parents' output distributions* conserves expected rare-capability mass at
the single-parent level, cancelling the multi-parent gain *to first order in the rare-item regime*
(outside it, variance reduction from averaging can help — the result is a first-order cancellation,
not a universal impossibility), while union-preserving operators realise the gain in all regimes —
derived in the minimal model, with its weight-space image the headroom rule below; (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, and confirm it in real trained weights: a merge barrier that survives alignment under the
*full* function-preserving symmetry group of the network (not just Git Re-Basin permutations), rising
with functional conflict while hybrid fitness falls to inviability — with an honest converse we
pre-registered and found: absent conflicting training signals, divergently-specialised lineages of
shared ancestry developed *no* isolation at any divergence tested, the merge instead *rescuing* the
forgetting specialists. Isolation must be provoked by conflict; specialisation alone did not speciate.
AI also has an advantage biology lacks: **directed sex** — unbounded parents, chosen mates,
and offspring screened before they are kept — engineered recombination with a flexibility of parent
choice and pre-deployment screening that natural mating systems do not approach.

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, a real-weight
demonstration of the speciation cliff (a Git Re-Basin residual that survives neuron alignment), 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. Three things we do **not** claim. First, that collapse is
Wright–Fisher drift: formalised independently (Riis, 2026; Shumailov et al., 2024), sharpened to a
closed-form first-extinction law whose onset coincides with collapse (Benati et al., 2025) and to a
quantitative-trait-genetics account for diffusion models (Yoon et al., ICLR 2025), 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. Third, that merge success can be *predicted at all*:
machine-learning-native predictors exist, from interpretable pairwise metrics (gradient alignment —
Zhou et al., 2026) to capacity/rate-distortion accounts of "merging collapse" (2026); what they lack,
and we supply, is the *mechanism* — when and why the failure is a coordinate artefact versus genuine
functional incompatibility, and what moves the cliff. What a geneticist is placed to supply is a
**framework** rather than a search heuristic. The nearest precursor is a theory-of-computation
tradition reading sex as an algorithm for *mixability* (Livnat & Papadimitriou, 2016), pre-dating
model merging; the works above use evolution chiefly as vocabulary over an optimiser, and — to our
knowledge — the quantitative apparatus of the evolution of sex (Fisher–Muller, outbreeding depression,
migration–drift balance, reproductive isolation) has not previously been carried over as more than
metaphor. We are also candid about what *kind* of contribution each of our claims is, because three
different things are easily conflated: **interpretation** (an existing result is usefully understood
in these terms — e.g., merged offspring beating their parents as Fisher–Muller), **explanation** (the
transferred mechanism accounts for observations existing accounts leave open — e.g., which merge
failures are coordinate artefacts and which are functional), and **prediction** (the framework
forecasts an unmeasured outcome and improves a design decision). This paper is strongest on the
first, makes concrete progress on the second, and reports a first, bounded step on the third: a
**controlled predictive test** at small scale in which pre-merge *functional-disagreement* measures —
chosen by the framework — showed a detectable, held-out-robust association with merge damage on a
constructed task grid, while the selected weight-geometry baselines did not. We are precise about
that result's boundary where it is reported: it is a small-model demonstration on a constructed grid;
the proposed epistasis-specific refinement did not outperform plain disagreement; predictor
differences are not individually significant head-to-head; and whether the prediction improves a
budget-matched operator choice remains open. The organising shift we argue for
is prior to any single mechanism: **treat multigenerational model populations as systems whose
inheritance, diversity, and compatibility must be managed — not merely as collections of models to
optimise.**

## 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, its *parameters*
cannot be forgotten — though a precise reader should note the system's *behaviour* can still shift
while adapters are active, so the guarantee is of a recoverable core, not of unchanging conduct. 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, for the minimal model).** 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.* In this **minimal
inheritance model** the finite-sampling step is **exactly** genetic drift — the random loss of rare
variants in a finite population — described by the century-old **Wright–Fisher** model (Wright, 1931;
Fisher, 1930): the same equations, which we use as closed-form checks on our simulations. Rare items
go extinct first, roughly ten times faster than common ones, precisely as drift predicts. **The
boundary of the identity matters, and we measured it:** real neural training adds approximation,
optimisation noise, and inductive bias on top of sampling, and when we fit trained networks against
the exact drift null they deviate in *opposite, architecture-specific directions* — a smoothing
recurrent model resists collapse (it keeps spurious variants alive), a sharpening image generator
accelerates it (our learning-kernel result, below). So the honest statement is: the minimal
inheritance model is exactly Wright–Fisher; a real learner is Wright–Fisher *plus a signed,
measurable estimator-bias operator* — and the drift signs (rare-first loss, the grounding response)
survive that operator in every architecture we tested.

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). We use the ratchet as the
*organising correspondence* for model collapse, with its scope stated: strictly, the ratchet is the
stochastic loss of the least-degraded class under recurring deleterious change in an asexual
population, so it maps onto the *irreversible* component of capability loss (once every copy of a rare
capability is gone from all parents and sources, no recombination can rebuild it) rather than onto
every form of degradation. That is exactly why the correspondence is useful rather than decorative: it
says the cure must act *before* fixation-by-loss — keep complementary variants alive somewhere in the
population — because recombination can only reassemble what still survives. Biology solved this
problem, and its solution is the subject of this paper.

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.

**The operator boundaries (stated, because "merge, don't average" is not one claim but a family).**
Four different operators travel under these words, and the conservation result belongs to exactly one
of them. What is *derived* is this: when a pupil's knowledge is refit to the **mean of the parents'
output distributions**, the expected mass on any rare item is conserved at the single-parent level —
in the rare-item regime (`n·p/K ≪ 1`) the 1/K dilution of averaging cancels the union gain of having
K parents to first order — outside that regime, survival is convex in mixed mass and averaging's
variance reduction can help, so this is a first-order cancellation, not a universal impossibility;
whereas an operator that keeps, per item, its **strongest source** (and renormalises, which itself
redistributes mass) realises the union in all regimes. That statement is exact in the minimal model, and it presupposes an
oracle (or verifier) able to say which source is strongest. The two operators the LLM prototype
tests — **weight averaging** (a nonlinear network's weight-mean does not compute the mean of its
parents' outputs) and **routing among intact specialists** (which keeps K models' storage and an input
classifier, a different parameter and inference budget from one fixed-size child) — are *empirical
cousins* of the two sides of that law, not instances of it. The headroom rule above is precisely the
empirical bridge: it says when the weight-average behaves like the diluting mean (hard tasks, weak
base) and when a capable base absorbs the dilution (easy tasks). And all of it operates within a
capacity boundary: when parental capabilities genuinely cannot coexist in the child's capacity, no
operator preserves the union — that regime is the subject of the speciation section below.

Three results keep this honest, and all 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.*

*The mating system matters too — not just who mates, but how widely.* The result above is about the
recombination *rate*; a separate knob is the population's **mating structure** — whether reproduction is
**monogamous** (each model recombines within a narrow, local circle) or **promiscuous** (mates drawn
freely from the whole population). Almost all model-merging implicitly assumes promiscuity — fuse
everything, or route over one flat pool — but population genetics says the breadth of gene flow is itself
consequential, because wide flow spreads good variants fast while **homogenising** the population, and
narrow flow preserves the distinct sub-populations needed to explore several solutions at once (Wright's
*shifting balance*). We sweep exactly this breadth against landscape ruggedness, and the optimum moves:
on smooth (additive) landscapes wide, promiscuous mating is best (spread the one good direction fastest),
but as the landscape gets rugged the best breadth **shrinks to an intermediate value** — full promiscuity
prematurely converges onto one basin and finds a *worse* champion, while pure monogamy over-fragments.
Throughout, wide mating lifts the *typical* model but monotonically **destroys diversity** — so on rugged
problems, where the best model needs preserved diversity to be found, structured (partly monogamous)
merging wins. The design rule extends the one above: *merge widely when skills are additive; keep
structured sub-populations — island-style merging — when skills are rugged.* (Figure: `results/E14/E14.png`.)

*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. (Figure: `results/E12/E12.png`.)

**The real-weight confirmation.** The obvious objection to the analytic model is that its
"incompatibility" is a re-labelled loss barrier, and loss barriers between independently trained
networks are famously a *coordinate* artefact — two nets that learned the same function in a permuted
basis look incompatible until their neurons are aligned (Git Re-Basin), and recent work shows that
symmetry groups *richer* than permutations remove still more of the barrier (functionality-preserving
rescalings and rotations — Scaling LMC, 2026; neuron-identifiability approaches). We therefore ran the
experiment the objection demands, in real trained weights, aligning modulo the **full**
function-preserving unit symmetry group of the architecture (per-unit positive rescaling composed with
permutation — for a plain ReLU network, all of it). Two small MLPs are forked from a shared MNIST base,
trained, weight-averaged, and their linear-mode-connectivity error barrier is measured *before and
after* alignment; the after-alignment **residual** is the part of the incompatibility that no
re-coordination can explain away. The decomposition is clean (Figure:
`results/speciation_real/speciation_real.png`): two nets trained *from different random initialisations
on the same task* have a real naive barrier that alignment removes almost entirely (residual ≈ 0.001,
and the aligned merge performs at parent level) — same species, different basis, the canonical Re-Basin
result, which also proves the aligner works. Two nets that learned *conflicting* label maps have a
large barrier of which the full symmetry group removes **essentially nothing** (0.502 → 0.497) —
a conflict-associated barrier the tested alignment leaves largely unchanged — supporting a
functional-conflict interpretation without proving optimal alignment (control recovery validates a
special case; the removable share is a lower bound, the residual an upper bound). It also carries a floor no future alignment
method can breach: models loyal to label maps that conflict on a fraction *μ* of inputs cannot both be
served by *any* single merged model, which must err at rate ≥ *μ*/2 against at least one parent
(SI proposition). Sweeping the fraction of conflicting classes traces the **isolation cliff in real
weights**, now readable directly as *hybrid fitness*: the residual barrier climbs monotonically while
the merged model's accuracy falls from 0.97 to 0.03 — E12's compatible → depression → inviability
trajectory, measured.

**And its honest converse: speciation must be provoked; it did not emerge.** A true
Dobzhansky–Muller incompatibility is *emergent* — each lineage's changes harmless alone, incompatible
only in combination — whereas the conflict condition above *imposes* contradiction. So we pre-registered
the emergent test: fork two children from a shared base and let them diverge with **no conflicting
training signal anywhere** — one pair as complementary class specialists (one child trains only on
digits 0–4, the other only on 5–9), one pair with divergent input conventions (views shifted in
opposite directions) — out to divergences 6.4× the base training. The result is the second
pre-registered reading, and it sharpens the theory's scope rather than confirming its most dramatic
form: the residual barrier is **0.000 at every divergence in both conditions**, and far from failing,
the merge *rescues* the two specialists — each parent decays toward ~0.50 on the full task
(catastrophically forgetting the classes it no longer sees) while the merged model holds ~0.95
throughout, a sustained Fisher–Muller rescue at zero barrier. In real weights, at least in this regime
of shared ancestry and compatible tasks, **reproductive isolation requires functional conflict; it does
not arise spontaneously from divergent specialisation.** The design rule sharpens accordingly: *merge
freely across divergently-specialised lineages of shared ancestry — what speciates model populations is
conflicting conventions, not specialisation per se.* Whether long-horizon over-specialisation erodes
mergeability at language-model scale — as the empirical merging literature hints (experts trained
longer merge worse under averaging) — is exactly the next tier's question, and the theory now makes the
prediction crisp: it should depend on whether extended training induces *conflicting conventions on
shared circuitry*, not on divergence time itself.

**What these experiments do and do not establish.** Stated at exactly the strength of the evidence:
they establish that *some merge failures reflect incompatible functional requirements rather than a
mismatch of coordinates* — a residual that survives the full unit-symmetry group of the architecture
tested, rises with functional conflict, and is absent under compatible specialisation. Three
qualifiers. First, the impossibility at the heart of the conflict condition — one deterministic model
cannot satisfy two contradictory answer conventions — is information-theoretic and needs no population
genetics; what the genetic frame adds is *structure around it*: which divergences generate such
conflicts, the prediction that epistasis rather than distance sets the cliff's position, and the
snowball's super-linear onset — the latter two verified so far only in the analytic model, and
therefore carried as **hypotheses at the neural tier, not results**. (On the snowball, one more
distinction: super-linear growth in the *number* of incompatibilities does not by itself entail a
sharp *performance* cliff — that needs the link from incompatibility count through effect sizes to
measured performance, which the analytic model supplies under its assumptions and any neural test
must establish separately.) Second, our alignment removes the symmetries we enumerate for this
architecture class, and exactly recovering a permuted-and-rescaled copy validates a special case
rather than proving global optimality for independently trained networks — so the removable share is
a lower bound and the residual an upper bound; richer transformation families for other architectures
could reapportion the split, though not below the conflict floor. Third,
"unmergeable" here means by aligned linear interpolation of weights — a barrier to that operator does
not preclude every conceivable recombination method (routing, for one, sidesteps it by not blending).
Emergent Dobzhansky–Muller incompatibilities in real weights remain the flagship *hypothesis* of this
programme: our tested regimes found none, which bounds where they can live — longer horizons, shifted
data distributions, capacity pressure — and the decisive experiment (predicting merge success *before*
merging from an operational epistasis measure, against geometry- and gradient-based predictors) is
posed in the closing section.

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*.

The pattern **echoes the Baldwin effect** (Baldwin, 1896; its clean computational demonstration is
Hinton & Nowlan, 1987): knowledge acquired and re-learned every generation eventually becoming part of
the innate endowment. We use the echo advisedly — Baldwin's mechanism is *selection* favouring
genotypes that learn the trait ever more easily, whereas re-minting is direct distillation, a
deliberate engineering shortcut through the same soft-to-innate valve. The valve is the point: two
substrates, the soft learned patches and the hard base weights every model is born with, with a
controlled passage between them.

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 (of the lineage, not the archive).** A digital system can, of course, keep every
  old base on disk — nothing forces deletion, and archives should be kept. The irreversibility is
  *operational*: once the lineage's production base, training mixtures, and selection all run downstream
  of the re-minted weights, a quiet collapse baked into them propagates to every descendant, and the
  archived ancestor helps only if some process still compares against it — which nothing in the loop
  does by default. In our minimal models a collapsed-then-re-minted lineage locks in its loss exactly
  this way, and a cheap safeguard prevents it: **re-mint only while the lineage is demonstrably diverse
  and healthy** (and keep an audit that diffs against the archived ancestor), 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 honest answer to "can it grow forever?" is: **the architecture removes the *storage* obstacle
to indefinite accumulation** — nothing is retained without bound anywhere, and consolidation resets
the soft budget each epoch — but that is a statement about bookkeeping, not a demonstration of
unbounded capability growth, which no fixed-capacity system can promise and our finite models
(deliberately scoped as "effectively open-ended relative to the sample size, not astronomically
open-ended") do not test. What the design claims is the weaker, defensible thing: at no level does a
full store force the lineage to stop learning.

## 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 mating system, not just the mating.* Sweeping how *widely* models recombine — from monogamous
  (local, structured) to promiscuous (panmictic) — against landscape ruggedness, the best breadth
  **shrinks as skills get more entangled**: wide, promiscuous merging wins on additive landscapes, but on
  rugged ones it prematurely converges to a worse champion and an intermediate, structured breadth wins,
  because promiscuity monotonically destroys the diversity a rugged search needs. A merging-native design
  axis — *merge widely for additive skills, keep island-structured sub-populations for entangled ones* —
  that the model-merging literature, which assumes panmixia, does not have.
- *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, stated qualitatively: these tricks pay off where the naive average falls
  short of attainable performance, and add nothing where it does not (a quantitative form is untested).
  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.

### The claims at a glance: status, assumptions, evidence, limits

Because a perspective of this breadth risks blurring what is proved, what is measured, and what is
proposed, here is the ledger of the load-bearing claims — each labelled **exact** (closed-form in the
minimal model), **empirical** (measured in trained systems), or **hypothesis** (stated with a
falsifier, not yet established):

| Claim | Status | Key assumptions | Evidence | Known limits |
|---|---|---|---|---|
| Collapse = Wright–Fisher drift (minimal model) | Exact (diagnosis conceded to prior work) | Knowledge = categorical distribution; refit = resample | Closed forms reproduced to <0.5% | Real learners add a signed, architecture-specific estimator bias (measured) |
| Grounding = immigration; critical real-data fraction ≪ 1 | Exact + empirical sign | Fresh samples from a fixed, non-drifting truth | Exact `H_eq`; `g*≈0.048`; sign holds in RNN/MLP/VAE and on MNIST | Deepest tail unrescuable at feasible budgets (`m ∼ 1/p`); sharp threshold softens in trained nets |
| "Merge, don't average" conservation | Exact **for the output-mean operator** | Rare-item regime; an oracle/verifier identifies the strongest source | E4 closed form + simulation; neural reproduction | Weight-averaging and routing are empirical cousins, not instances; budgets differ; bridge = the headroom rule |
| Offspring exceed every parent (Fisher–Muller) | Interpretation + empirical | Complementary (decorrelated) parents; verifiable fitness | E8 analytic; 7B LoRA merge beats every specialist on every family | LLM tier: 3 lexically-distinct families; multi-seed replication in progress |
| Outbreeding depression on rugged landscapes; operator design rule | Exact-model result; hypothesis at LLM scale | NK epistasis stands in for skill entanglement | E9–E10; directed selection rescues | Not yet mapped onto a real task-entanglement measure |
| Optimal mate-pool breadth shrinks with ruggedness | Exact-model result; hypothesis for merging populations | Ring population, local selection | E14 | Phenomenon known to island-model evolutionary computation; our contribution is the mapping and the diversity/mean decomposition |
| Merge failure decomposes into coordinate artefact + functional residual | Empirical (MLP tier; LLM tier in progress) | Alignment enumerates the architecture's unit symmetries | Full-symmetry residual ≈ 0 (compatible) vs ≈ naive (conflict); cliff in hybrid fitness | Scoped to aligned linear interpolation; conflict floor is information-theoretic, not genetic |
| Epistasis (not divergence) sets the cliff; snowball onset | Exact-model result; **hypothesis** at the neural tier | BDM incompatibility structure | E12 | Snowball count ≠ performance cliff without the effect-size link; neural test outstanding |
| Pre-merge functional disagreement predicts merge penalty | Empirical, within a controlled grid (0.5B, 13 conditions × 3 seeds) | Constructed conflict/overlap/duration axes; oracle-potential outcome (pre-registered; ordering sensitive to reference) | Clustered CIs exclude 0; held-out LOCO ρ≈0.4; selected geometry baselines ≈ 0 | Head-to-head predictor differences not individually significant; only selected baselines; generalisation to real task pairs open |
| Confidence weighting improves rank prediction over raw disagreement | **Not supported** (pre-registered internal prediction) | — | Paired Δ\|ρ\| ≈ −0.02, CI [−0.13, +0.06] | Weighting does double the conflict-vs-compat level contrast |
| The predictor improves budget-matched operator choice | **Open** | — | Soup-vs-route gap readout noise-dominated at 0.5B | The practical payoff; untested |
| Emergent speciation without conflict | **Not observed** (pre-registered) | Shared ancestry, compatible tasks, tested divergences | E13b: residual 0.000; merge rescues specialists | Bounds the hypothesis; longer horizons/distribution shift/capacity pressure untested |
| Grounding + sex + diversity complementary (each ablation fails distinctly) | Analytic-model result; hypothesis at LLM scale | Conformity stands in for self-consumption; general joint necessity not established | E11 four-arm ablation | The full grounded LLM society is unbuilt; alternative schemes untested |

**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; the
Wright–Fisher collapse literature following Shumailov et al., 2024; the closed-form first-extinction
law of Benati et al., 2025; the quantitative-trait account of Yoon et al., 2025); (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); (d) that merge success has
machine-learning-native *predictors* — interpretable pairwise metrics (Zhou et al., 2026),
capacity/rate-distortion accounts of merging collapse (Cao et al., 2026), and stability/scaling
analyses of multi-task degradation; and (e) that verifier-screened synthetic data can avert collapse
(Yi et al., 2025) — the statistical cousin of our grounding operator. We claim none of these.

**Ours** is the framework those results invite: a **population-genetics of sex** applied to model
societies, 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 operators make
*complementary, distinctly-failing contributions* in the tested model (general joint necessity is not
established). 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 — and we confirm it in real trained weights, where
a merge barrier survives alignment under the *full* function-preserving symmetry group (not only
Re-Basin permutations) as a residual, functional reproductive isolation with an information-theoretic
floor — together with the pre-registered emergent converse: absent conflicting training signals,
divergently-specialised lineages of shared ancestry showed *no* isolation at any divergence tested, the
merge instead rescuing the forgetting specialists (isolation must be provoked; specialisation alone did
not speciate). 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).
- Livnat, A., & Papadimitriou, C. (2016). Sex as an algorithm: the theory of evolution under the lens of computation. *Communications of the ACM 59(11).* (The theory-of-computation precursor: recombination selects for mixability.)

*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.*
- Benati, M., Londei, A., Lanzieri, D., & Loreto, V. (2025). First-extinction law for resampling processes. *arXiv:2509.20101.* (Collapse onset = the Wright–Fisher first-extinction time.)
- Yoon, Y., Hu, D., Weissburg, I., Qin, Y., & Jeong, H. (2025). Model collapse in the self-consuming chain of diffusion finetuning: a novel perspective from quantitative trait modeling. *ICLR 2025 / arXiv:2407.17493.*
- Yi, B., Liu, Q., Cheng, Y., & Xu, H. (2025). Escaping model collapse via synthetic data verification. *arXiv:2510.16657.*
- Ainsworth, S., Hayase, J., & Srinivasa, S. (2022). Git Re-Basin: merging models modulo permutation symmetries. *arXiv:2209.04836.*
- Li, T., & Shen, Z. (2026). Scaling linear mode connectivity and merging to billion-parameter pretrained transformers. *arXiv:2606.23607.* (Symmetry groups richer than permutations remove more of the barrier.)
- Sharma, E., Roy, D. M., & Dziugaite, G. K. (2024). The non-local model merging problem: permutation symmetries and variance collapse. *arXiv:2410.12766.*
- Pari, J., Jelassi, S., & Agrawal, P. (2024). Collective model intelligence requires compatible specialization. *arXiv:2411.02207.*
- Zhou, L., Zhao, B., Yu, R., & Rodolà, E. (2026). Demystifying mergeability: interpretable properties to predict model merging success. *arXiv:2601.22285.*
- Cao, Y., Ran, D., Guo, Y., Wu, M., Chen, S., et al. (2026). An empirical study and theoretical explanation on task-level model-merging collapse. *arXiv:2603.09463.*
- Hu, Y., Yao, Y., Zhang, N., Chen, H., & Deng, S. (2024). Exploring model kinship for merging large language models. *arXiv:2410.12613.*
- Kozodoi, N., Afolabi, Z., & Butler, J. (2026). Are we merging the right models? Impact of expert training duration on model merging for LLMs. *arXiv:2607.11997.*
- Harris, K. D. (2026). A mathematical theory of evolution for self-designing AIs. *arXiv:2604.05142.*
- Chen, N., Tong, Y., Yang, Y., He, Y., Zhang, X., et al. (2026). Diversity collapse in multi-agent LLM systems: structural coupling and collective failure in open-ended idea generation. *arXiv:2604.18005.*
- Tanaka, H. (2026). When is collective intelligence a lottery? Multi-agent scaling laws for memetic drift in LLMs. *arXiv:2603.24676.*

*Still to engage in a full version: tacit knowledge (Polanyi) and human capital (Becker).*