# llm_merge_hpc — recombining specialist LLMs at scale (7B, Imperial CX3; blueprint C2/C4)

**Claim tested.** The scale-up of `llm_merge`: at a *capable* base, does recombining decorrelated
specialists produce a model that **exceeds every single parent overall** *and* stays competent across
**all** families (the Fisher–Muller "offspring fitter than any parent" signature, E8) — the claim that
was only marginal at 0.5 B? Run on one **L40S (46 GB)** GPU node of Imperial College's CX3 HPC
(job walltime **8 min**).

**Setup.** Base model **Qwen2.5-7B-Instruct** (Apache-2.0). Same three *disjoint*, procedurally-
generated task families with an **exact-match verifier** (the "reality that says no"): `lists`,
`strings`, `arith`, deliberately hard so specialists decorrelate. One **LoRA specialist** (r=16,
α=32, 3 epochs, 800 train tasks) is fine-tuned per family, then the base, each specialist, and two
weight-space **merges** — `soup` (averaged LoRA deltas) and `ties` (sign-reconciled union) — are
evaluated on a held-out mixed test set. Seed 1, **200 test tasks/family**.

### Results (accuracy)
| model | lists | strings | arith | overall | **worst family** |
|---|---|---|---|---|---|
| base | 0.46 | 0.69 | 1.00 | 0.71 | 0.46 |
| spec: lists | 0.57 | 0.74 | 1.00 | 0.77 | 0.57 |
| spec: strings | 0.32 | 0.97 | 1.00 | 0.76 | 0.32 |
| spec: arith | 0.47 | 0.80 | 0.96 | 0.74 | 0.47 |
| **merge: soup** | **0.62** | **1.00** | 1.00 | **0.87** | **0.62** |
| **merge: ties** | **0.62** | **1.00** | 0.99 | **0.87** | **0.62** |

### What holds (the 0.5 B caveats resolved)
- **"Exceeds every parent overall" — now clean.** Both merges reach **0.87 overall**, above the best
  single specialist (lists, **0.77**) by a decisive 10 points, and above every specialist on every
  family (lists 0.62 > 0.57; strings 1.00 > 0.97; arith ≈ 1.00). This is the strict Fisher–Muller
  claim — offspring fitter than *any* parent — which was only marginal at 0.5 B and is now clean.
- **"Retains all specialties" — sharper than ever.** The merges are the only models competent across
  **all** families: worst-family **0.62**, versus **≤ 0.57** for every specialist (strings-specialist
  collapses to 0.32 on lists). The generalist assembled from specialists dominates on both axes.
- **The dilution is gone.** At 0.5 B, averaging *diluted* the lists-specialist (0.43 → 0.26). At 7 B
  the merge **exceeds** the lists-specialist on lists (0.62 > 0.57). A capable base has enough headroom
  that weight-space averaging composes rather than dilutes — the "merge, don't average" concern (E4)
  softens once the parents are strong. Soup and ties are indistinguishable at K=3 here.

### Takeaway
**The scale-up firms up the sign the prototype left marginal.** At a capable base, recombining
decorrelated specialists yields a model that beats every parent both overall and per-family, with no
dilution — the sexual-reproduction / Fisher–Muller claim reproduced cleanly in real LLM weights. The
0.5 B `llm_merge` prototype de-risked the pipeline and flagged dilution as the risk; the 7B run shows
that risk is a small-model artefact. **Falsifier (not triggered):** a single specialist matching or
beating the merge overall — here the merge leads by 10 points. Provenance: L40S, torch 2.12.1 /
transformers 5.13.0 / peft 0.19.1, `manifest.json` records the results hash and library versions.

*Note: this run's `manifest.json` has `git_commit: null` because it was produced on the HPC node from
an rsync'd (non-git) working copy; the committed artefacts here are the source of truth for the figure.*