narrative: make the intellectual contribution explicit

Two insertions answering the editor's implicit questions. Introduction
gains "stated as a problem": the four recurring decisions a model-
population operator cannot currently answer from principle (replay
budget; compose-or-damage; pre-merge detection; separate-vs-consolidate)
and the framework's counterintuitive answers — averaging cancels the
multi-parent benefit exactly in the tail regime; specialisation/
divergence produced no incompatibility anywhere tested while conflicting
conventions always did; weight distance carried no predictive signal
while cheap behavioural disagreement did; and the theory's numbers land
on the replay constants practice converged to independently
(unexpectedness + problem-solved + external check, at reviewer-hardened
calibration). Discussion gains a closing "Outlook: the evolution of
language models": the ecosystem's trajectory is consolidating on exactly
these operators, and the framework's fork is concrete and measurable —
either models stay freely recombinable (one interbreeding population;
levers = per-capability grounding budgets + deliberate diversity) or
long-horizon specialisation at scale begins emergent speciation (a
routed archipelago of diverging lineages), decidable now with the
pre-merge conflict instruments tested here. Design rules tie back to the
posed decisions. 5.2k words, citation invariant intact, 20 pp.

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

paper/pnas/main.md

@@ -114,6 +114,20 @@ predictive test in which pre-merge functional-disagreement measures, chosen by t
 predicted merge damage on a constructed task grid while the tested weight-geometry baselines showed
 no detectable association.
 
+Stated as a problem: an operator of a model population today has no principled answer to four
+recurring decisions. How much verified real data does retraining need before a lineage decays?
+Will combining two particular models compose their abilities or damage them? Can incompatibility be
+detected before paying for a failed merge? And when should specialists be kept separate rather than
+consolidated? Current practice answers these with folklore constants and trial-and-error searches.
+The framework prices each decision, and several of its answers are not the intuitive ones. Averaging,
+the default combining operator, cancels the benefit of multiple parents to first order precisely in
+the regime where that benefit matters most, the rare-capability tail. Specialisation and divergence,
+widely treated as the threat to mergeability, produced no incompatibility in any regime we tested;
+conflicting conventions always did. Weight distance, the field's default compatibility signal, carried
+no detectable predictive signal in our controlled test, while a cheap behavioural measure did. And
+where the framework's numbers can be checked against settled practice, they land on it: the replay
+fractions that continual learning converged on empirically sit at the minimal model's threshold.
+
 The correspondences we develop, summarised in Table 1: single-teacher retraining is *asexual
 reproduction*, and the irreversible arm of its decay shares the defining consequence of *Muller's
 ratchet* (46): once every copy of a rare capability is gone from all parents and sources, no
@@ -377,7 +391,7 @@ are the experiment's open front.
 ## Discussion
 
 **Design rules.** As engineering guidance, the results reduce to rules that an operator of a model
-population can apply. *Ground every generation* in verified reality — a few percent retained most diversity in our tested
+population can apply, answering the four decisions posed in the Introduction. *Ground every generation* in verified reality — a few percent retained most diversity in our tested
 settings — but price the rarest capabilities individually (observation probability `1 − e^{−m·p}` per
 batch under unstratified sampling), consider targeted sampling for the deep tail, and use
 recombination to recover rare capabilities still retained across complementary parents. *Merge, don't blend, when there is headroom*: keep specialists
@@ -439,6 +453,21 @@ system: populations of learners where every genotype, environment, and mating de
 and manipulable — where the evolution of sex can be studied with interventions (unbounded parents,
 offspring preview, directed mating) that no living system permits.
 
+**Outlook: the evolution of language models.** The Introduction's premise, that the model ecosystem is
+an evolving population, is also a forecast about where these results matter next. Language-model
+development is consolidating around exactly the operators studied here: synthetic-data flywheels
+(inheritance), merging and routing of specialist fine-tunes (recombination and population structure),
+verifier-gated data pipelines (grounded selection), and periodic consolidation of adapters into new
+bases. The framework's reading of that trajectory is concrete. If coming model generations remain what
+our tested regimes found, freely recombinable in the absence of conflicting conventions, then the
+ecosystem evolves as one interbreeding population, and the levers that matter are grounding budgets
+priced per rare capability and diversity preserved deliberately. If instead long-horizon
+specialisation at scale begins to produce emergent incompatibility, as the expert-training-duration
+observations hint (65) and our small-scale null does not rule out, then lineages will begin to
+speciate, and the ecosystem's future is a set of diverging species connected by routing rather than by
+merging. Which of the two it will be is measurable now, with the pre-merge conflict instruments this
+paper tested.
+
 ## Materials and Methods
 
 **Analytic tier.** Pure NumPy/SciPy Wright–Fisher simulator over `K`-item distributions (knowledge as