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-accessible.md

@@ -17,10 +17,11 @@ giorgio@gilest.ro · https://lab.gilest.ro
 
 If you train an AI model on the output of earlier AI models, over and over, it rots: rare knowledge
 disappears and everything drifts toward a bland average. This is a known problem ("model collapse"),
-and it turns out to be *exactly* the same math that describes how small biological populations lose
-rare genes by chance. That is bad news, but it comes with good news: biology already discovered the
-cure for this kind of rot hundreds of millions of years ago. The cure is **sex** — making offspring by
-*combining* several parents instead of copying one. This paper takes ninety years of genetics about
+and in its simplest form it is governed by *exactly* the same math that describes how small biological populations lose
+rare genes by chance. That is bad news, but it comes with good news: biology has been managing this
+kind of rot for hundreds of millions of years, and its best-tested remedy is **sex** — making offspring
+by *combining* several parents instead of copying one. This paper tests how far that remedy carries
+for AI. This paper takes ninety years of genetics about
 *when and why sex beats cloning* and reads it as an engineering manual for building AI that keeps
 getting better across generations instead of decaying. Along the way it produces concrete, testable
 rules — including a surprising one about *how* to combine models ("merge, don't average"), and a limit
@@ -64,8 +65,8 @@ without the knowledge rotting on the way down?** That's exactly where it can go
 
 (This corner of AI is suddenly busy: several 2025–2026 research projects build populations of models
 that improve over rounds, and "model merging" has become a small industry that already borrows words
-like crossover, mutation, and mate choice. What's missing, and what this paper supplies, is the actual
-*theory* behind those borrowed words.)
+like crossover, mutation, and mate choice. What this paper adds is the quantitative framework behind
+those borrowed words — and honest tests of where it works and where it doesn't.)
 
 ## 2. Why today's models can't do this
 
@@ -84,8 +85,8 @@ pass on what they gained. So step one is a model that can *grow safely.*
 The trick is to stop overwriting. Keep the model's original core frozen and untouchable, and bolt each
 new skill onto *extra* capacity added beside it. In practice this is what small add-on "patches" like
 **LoRA** already do: the big pretrained model stays fixed, and you train a little attachable module for
-each new skill. If the core is never altered, forgetting it becomes impossible by construction — not
-just unlikely.
+each new skill. If the core is never altered, its knowledge can't be erased — though the system's
+*behaviour* can still change while patches are active; what's guaranteed is a recoverable core.
 
 There's even a rough brain analogy: we have a fast memory (the hippocampus) that grabs an experience
 immediately, and a slow memory (the cortex) that absorbs patterns gradually, usually while we sleep.
@@ -120,7 +121,7 @@ toward its own most common output. And here's the nasty part: the thing that mak
 *useful* — "keep the general, drop the quirky" — *is* the same act that deletes the tail. The operation
 you want and the operation that kills the lineage are the same move.
 
-**In biology terms (and it's literally the same math):** picture a model's knowledge as a big bag of
+**In biology terms (and for our simplest model it really is the same math — real networks add a measurable twist on top, which we also measure):** picture a model's knowledge as a big bag of
 items — facts, skills, behaviours — in certain proportions. One generation is: draw a finite sample
 from the parent, and rebuild the child from that sample. That "finite sample" step is *identical* to
 **genetic drift** — the way rare gene variants vanish by chance in any finite population. This isn't a
@@ -130,8 +131,10 @@ common ones — precisely what drift predicts.
 
 And copying one teacher is **asexual reproduction** — cloning. Biology already knows the fate of a
 lineage that only ever clones and never combines: it piles up damage it can never undo, a one-way
-decline called **Muller's ratchet**. *Muller's ratchet is model collapse.* Naming it that isn't just
-poetry — it tells us where the cure is, because biology already solved this.
+decline called **Muller's ratchet**. That's our lens for the *irreversible* part of model collapse —
+the capabilities that, once every copy is gone, no amount of combining can rebuild. Naming it that
+isn't just poetry — it tells us where to look for remedies, because biology has spent a very long time
+solving exactly this.
 
 Two ingredients turn the rot into a climb. Both are things nature does.