paper: claim-narrowing revision from the external review

The review's core instruments adopted: the interpretation/explanation/
prediction ladder is now explicit in §1 (with the decisive pre-merge
epistasis-prediction test stated as the open bar, not claimed); identity
claims scoped (WF exact only in the minimal model, with the
learning-kernel deviation cited against ourselves; Muller's ratchet
scoped to the irreversible arm — recombination reassembles only what
survives); "nobody has / none imports / theory outrun" removed;
merge-don't-average given explicit operator boundaries (output-mean vs
weight-average vs routing vs max-with-oracle; budgets; oracle; capacity
handoff to speciation); a "what these experiments do and do not
establish" scope block added to the speciation section (conflict floor
is information-theoretic, not genetic; epistasis-cliff + snowball =
hypotheses at the neural tier; emergent DMIs = flagship hypothesis,
bounded by our null); "control theory" -> "framework" (subtitle
included); §3/§11 overstatements fixed (frozen core != frozen behaviour;
Baldwin echo, not identity; operational vs archival irreversibility);
claims-at-a-glance table (status/assumptions/evidence/limits) added to
§13. Reviewer's framing sentence adopted as the stated core
contribution. Accessible version calibrated to match. md2tex gains pipe-
table support; PDF rebuilds clean (22 pp). Lessons recorded.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v

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

@@ -1,6 +1,6 @@
 # The Evolution of Sex for Artificial Intelligence
 
-### A population-genetic control theory for societies of agents that reproduce, recombine, and stay open-ended
+### 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).*
@@ -28,13 +28,15 @@ you skipped a definition:
 - **Genetic drift** *(population genetics)* — the random loss of rare variants that happens in any
   finite population simply because not everyone leaves offspring. It is the neutral, no-selection
   baseline of evolution.
-- **Wright–Fisher process** *(population genetics)* — the standard mathematical model of drift. We
-  will claim, and show, that generational model-training *is* this process, not merely like it.
+- **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. It is, we will argue, the same thing as model
-  collapse.
+  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.
 
@@ -53,27 +55,35 @@ AI is turning from single frozen models to **populations of agents** that persis
 increasingly *recombined* into new models — a shift visible in multi-agent societies, population-based
 self-improvement, and the explosion of **model merging**. The field is doing this with the vocabulary
 of evolution — "crossover," "mutation," "mate choice," "offspring that beat their parents" — but as
-loose metaphor draped over search algorithms. This paper argues that the right theory is already
-written, in the branch of biology that studies exactly this: the **evolution of sex**. Ninety years of
-population genetics say precisely when reproducing a population by *recombination* beats copying, when
-it backfires, and how to do it better — and, read as a control theory, it tells an engineer how to keep
-a society of models learning across generations instead of decaying.
+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 the other half — the **cure**,
-and its assembly into a theory with predictions. Single-teacher copying is **asexual** reproduction,
-and asexual lineages decay by **Muller's ratchet**, which *is* model collapse; the remedy nature found
-is **sex**. A society of models should reproduce sexually — each new model **recombined from several
-complementary parents** (which the field already does, as *model merging*), selection **anchored to a
-reality that can say no** (not to the consensus of other models), and diversity actively **preserved**.
-With those three ingredients a lineage does not merely avoid collapse; it **climbs** — producing models
-fitter than any ancestor (the **Fisher–Muller effect**) while each specialty is re-earned and exceeded.
+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 the merging literature has
-not: (i) **"merge, don't average"** — recombination preserves the union of what parents kept, while
-averaging (a "model soup") is *blending inheritance* that mathematically cancels the benefit; (ii)
+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
@@ -143,18 +153,24 @@ mixture-of-experts routing): all established. Third, that merge success can be *
 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 is genuinely unoccupied — and what a
-geneticist is placed to supply — is a **theory** 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 and never applied to it. Every one of the works above
-uses evolution as *metaphor over an optimiser*; none imports the predictive apparatus of the evolution
-of sex. Nobody has stated the **"merge, don't average" conservation law**, derived **offspring-exceed-parents
-as Fisher–Muller**, predicted **outbreeding depression on rugged task landscapes**, framed **grounding
-as migration–drift balance** with a critical fraction, or connected **reproductive isolation** to when
-two models can be merged at all. An evolutionary algorithm that *finds* a super-parent is evidence for
-the theory, not a substitute for it — the way CMA-ES existing does not make fitness-landscape theory
-redundant. This paper supplies the theory the tinkering has outrun, and states what it predicts and
-where it would fail.
+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
 
@@ -177,9 +193,11 @@ The individual model needs two properties.
 core frozen and only *readable*, and carves each new skill into freshly-added capacity beside it. In
 machine learning this is called *parameter isolation* (progressive networks — Rusu et al., 2016;
 prune-and-freeze — Mallya & Lazebnik, 2018; and, most practically, **LoRA** and other small trainable
-"patches" bolted onto a frozen model — Hu et al., 2021). If the core is never altered, forgetting it
-is not merely unlikely but structurally impossible. This is what lets a model accumulate a coherent
-working life of expertise — the kind of stable knowledge worth passing on.
+"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:
@@ -221,20 +239,33 @@ distribution (the rare cases) first, and drifts toward its own most common outpu
 idiosyncratic* — **is** tail-deletion by design. The operation that would power a cultural ratchet and
 the operation that drives model collapse are the same act.
 
-**The population-genetics statement (the same thing).** Represent a model's knowledge as a
-distribution over discrete "items" — capabilities, facts, modes of behaviour. One generation is:
-*draw a finite sample from the parent, and refit the child to it.* That finite-sampling step is
-**mathematically identical** to **genetic drift** — the random loss of rare variants in a finite
-population — described by the century-old **Wright–Fisher** model (Wright, 1931; Fisher, 1930). This is
-not an analogy we find pretty; it is the same equations, and we use them as an exact check on our
-simulations (the first of the minimal models below). Rare items go extinct first, roughly ten times
-faster than common ones, precisely as drift predicts.
+**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). *Muller's ratchet is model
-collapse.* Naming it that way is not decoration; it tells us where the cure is, because biology solved
-this problem.
+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.
 
@@ -282,6 +313,23 @@ beats the average in exact proportion to how far the average is from the best at
 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
@@ -413,6 +461,26 @@ longer merge worse under averaging) — is exactly the next tier's question, and
 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?*
 
@@ -571,25 +639,29 @@ across enough generations, **re-mint the base**: distil the accumulated soft inh
 generations to acquire in patches. The soft budget resets; the next epoch begins from a richer floor.
 What was hard-won and *learned* becomes cheap and *innate*.
 
-This has a precise name, and it is not Lamarck's. Knowledge that is acquired and re-learned every
-generation, and — once reliably present for long enough — becomes part of the innate endowment so that
-it need no longer be re-learned, is the **Baldwin effect** (Baldwin, 1896; and its clean computational
-demonstration, Hinton & Nowlan, 1987). It is the valve between the two substrates: the soft, learned
-patches, and the hard base weights every model is born with.
+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.** Until now, one thing was always recoverable — the original pristine base, whose
-  lost tails could be restored just by reloading the file. Bake the current lineage into new immutable
-  weights and that escape hatch closes: if the lineage had been quietly collapsing, re-minting *fixes
-  the collapse in place* and discards the one uncollapsed reference that could have diagnosed it. In our
-  minimal models this is exactly what happens, and a cheap safeguard prevents it: **re-mint only while
-  the lineage is demonstrably diverse and healthy**, never as a rescue for a line already drifting. It
-  is the sharpest instance of the human seat of §9 — choosing what no future generation will think to
-  question.
+- **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
@@ -678,6 +750,26 @@ shape.)
   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
@@ -692,8 +784,8 @@ capacity/rate-distortion accounts of merging collapse (Cao et al., 2026), and st
 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 theory those results have outrun: a **population-genetics of sex** applied to model
-societies, which is *generative* where the incumbents are empirical. Concretely — the **"merge, don't
+**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"