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

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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.

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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 rare-capability mass at the single-parent level, so adding parents cannot help, while union-preserving operators realise the gain — exact in the minimal model, with its weight-space image verified as 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 — 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, 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.

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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 — e.g., an epistasis measure taken before merging that beats geometry-based predictors of merge success). This paper is strongest on the first, makes concrete progress on the second, and states the third as its open, decisive test — proposed here with pre-registered falsifiers, not claimed as done. 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 the 1/K dilution of averaging exactly cancels the union gain of having K parents — so adding parents cannot help; whereas an operator that keeps, per item, its strongest source realises the union. 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) — genuine reproductive isolation, not a missed symmetry, and it cannot be dismissed as a failure to align because the very same aligner erased the same-task barrier. 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. Second, our alignment removes the symmetries we enumerate for this architecture class; richer transformation families for other architectures could reapportion removable vs residual, 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 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 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 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.

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	The decisive pre-merge prediction test is proposed, not run
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 jointly necessary	Exact-model result; hypothesis at LLM scale	Conformity stands in for self-consumption	E11 four-arm ablation, each arm failing distinctly	The full grounded LLM society is unbuilt

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 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 — 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.

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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).