papers: consolidate the real-LLM recombination arc (merge/moe/directed + headroom law)

Folds the four LLM runs into both papers now that the arc is fully characterised.

results-summary.md: new section 5 "The claims tested in real LLM weights" — the
Fisher-Muller generalist (7B merge 0.87 > best specialist 0.77), union-vs-fusion
(routing beats averaging where there is headroom), directed sex (breed + select),
and the unifying HEADROOM law that resolves the earlier saturation confound.
Updates design rule 2 ("merge, don't average — where there is headroom"), adds a
plain-language point 8, refreshes the validation counts (131 tests, real-LLM tier).

the-lamarckian-society-v5.md: softens the three "not a language model yet" claims
to acknowledge the prototype; adds the real-LLM confirmation after the
Fisher-Muller and directed-sex claims (with the headroom caveat as a sharpening,
not a weakening); adds an LLM bullet to section 13; reframes the closing gap to
"the operators, checked; the living society, next."

Tone held to the accessibility/honesty bar: prototype scope flagged (signs not
magnitudes), caveats presented as sharpening the claims, nothing over-celebrated.

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

paper/the-lamarckian-society-v5.md

@@ -35,9 +35,11 @@ you skipped a definition:
   it knew when it learns something new.
 
 We have tried to keep the big picture legible on every page, and to be candid about what is argument
-and what is evidence. The evidence is from **deliberately small models** — mathematics, small neural
-networks, image generators, and evolutionary simulations. None of it is a large language model yet.
-We will say so repeatedly, because the gap matters.
+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.
 
 ---
 
@@ -67,13 +69,16 @@ AI has one advantage biology lacks: its "sex" has **no two-parent limit**, its m
 for complementarity, and its offspring can be **screened before they are kept**. We call this
 *directed sex*, and it turns recombination from a gamble into a reliable engine.
 
-We support the argument with a set of **minimal models** (not language models): a mathematically exact
-account of collapse and its cure; reproductions of the same effects in small trained neural networks
-and in a generator of handwritten digits; and evolutionary simulations in which a population of models
-climbs a "fitness landscape" that stands in for reality. In these, every claim above either holds or
-fails visibly, and removing any single ingredient breaks the system in a distinct way. The scope is
-honest: these are existence proofs and design rules, and the eventual test is to build the same thing
-out of real language models. We also note where our diagnosis is no longer novel — the reading of
+We support the argument with a set of **minimal models**: a mathematically exact account of collapse
+and its cure; reproductions of the same effects in small trained neural networks and in a generator of
+handwritten digits; and evolutionary simulations in which a population of models climbs a "fitness
+landscape" that stands in for reality. In these, every claim above either holds or fails visibly, and
+removing any single ingredient breaks the system in a distinct way. A first **language-model
+prototype** then confirms the recombination claims in real weights — merging LoRA-specialised Qwen
+models (up to 7B on a GPU cluster) produces a generalist that exceeds every parent, and shows the
+sharp condition under which the "merge, don't average" refinement matters (below). The scope is
+honest: these are existence proofs and design rules, and the eventual test is to build the *whole
+grounded society* out of real language models. We also note where our diagnosis is no longer novel — the reading of
 collapse as genetic drift has since been derived independently — and locate our contribution in the
 **cure** rather than the diagnosis.
 
@@ -207,6 +212,19 @@ knowledge; specialisation is re-earned each generation," and it is why the refra
 teacher→pupil to *sexual reproduction* is not cosmetic: **copying can only recover a ceiling;
 recombination can exceed it.**
 
+This is no longer only a simulation. In a first language-model prototype — LoRA specialists on
+disjoint task families, recombined and judged by an exact verifier — a merge of three specialist Qwen
+models (7B, on a GPU cluster) **beats every single specialist**, overall and on every family: the
+Fisher–Muller effect, in real weights. The same prototype pins down *when* the finer "inherit the
+union, don't average" rule actually bites. Keeping each parent whole and **routing** each input to the
+right one beats the tail-thinning average — but only when the task is hard enough to leave room to
+lose: on easy tasks a strong model's plain average is already at the ceiling, so the crude soup is
+fine, whereas on hard tasks the average dilutes a hard-won specialist so badly it falls below even the
+best single parent, and routing wins by a wide margin. The rule is therefore precise: **the union
+beats the average in exact proportion to how far the average is from the best attainable** — a caveat
+that sharpens rather than weakens the claim, and that a practitioner needs before spending compute on
+the fancier operator.
+
 Two caveats keep this honest, and both are results, not hand-waving.
 
 *Sex can backfire.* When the parents' skills are not cleanly separable but **entangled** — when the
@@ -223,8 +241,11 @@ and cannot inspect an offspring before it is born. An AI has none of those limit
 **generate many candidate offspring and keep only the fittest**, screening them against reality before
 committing. We call this **directed sex**, and in our simulations it converts the outbreeding-depression
 catastrophe into a reliable gain: where blind recombination collapses on entangled problems, directed
-recombination matches or beats the best parent every time. This is a genuine advantage of engineered
-reproduction over the biological kind, and we think it is one of the more useful ideas in the paper.
+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
@@ -443,9 +464,10 @@ refute, or you evolve toward what is merely convincing.**
 ## 13. What we built, what we found, and what is still open
 
 The previous drafts of this paper promised a "companion paper" that *would* make this concrete. That
-work now exists — as a set of **minimal, laptop-reproducible models**, not language models — and it is
-worth stating plainly what it does and does not show. (A separate results document gives the numbers;
-here is the shape.)
+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.**
 
@@ -471,6 +493,15 @@ here is the shape.)
   produces below-parent offspring (outbreeding depression) — and *directed* recombination (choose mates,
   screen offspring, unbounded parents) reliably fixes it. This is the concrete evidence for the paper's
   central reframing.
+- *The recombination claims, in real language models — with a sharp condition.* Merging LoRA-specialised
+  Qwen models (up to 7B on a GPU cluster) produces a generalist that beats every specialist parent
+  (Fisher–Muller, for real); and keeping parents intact and *routing*, or *breeding and screening*
+  offspring, beats the naive average — but *only when the task leaves headroom*. On easy tasks a strong
+  model's plain average is already at the ceiling and the refinements add nothing; on hard tasks the
+  average dilutes a specialist below even the best single parent, and the union-preserving operators win
+  clearly. The practical rule is exact: these tricks pay off in proportion to how far the naive average
+  is from the best attainable. This is a prototype (three task families, one seed), so we read it as
+  signs, not magnitudes; the *whole grounded society* on a language model remains the open step.
 - *The whole society, and why every part is needed.* In a population evolving on a "reality" landscape,
   the full system — grounding + sexual recombination + preserved diversity — climbs to the top while
   keeping its specialists. Remove *grounding* and it collapses into a confident, wrong consensus (a
@@ -499,10 +530,11 @@ the **institutions** that let contemporaries correct one another before error is
 we do not solve; and the **calibration** of everything the results left as knobs — how many parents,
 how complementary, at what ratio of inherited-to-real data, and how healthy a lineage must be before
 its knowledge is safe to make irreversibly innate. These are, at least, *measurable* — which is the
-difference between an open problem and a hole. And the largest gap of all: every result here is from a
-minimal model. The real test is to build the same grounded, sexually-reproducing, diversity-preserving
-society out of actual open-weight language models, and see whether the three signs survive contact with
-a system too big to write down. Theory and toys here; the real thing, next.
+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.
 
 ---