llm_merge_hpc: the 7B firm-up makes the Fisher-Muller sign decisive

Re-ran the specialist-merge experiment at a capable base (Qwen2.5-7B-Instruct,
200 tests/family) on one L40S GPU of Imperial's CX3 HPC (8 min walltime). The
two caveats the 0.5B prototype left marginal are now resolved:

- "exceeds every parent overall" is clean: both merges 0.87 vs best specialist
  0.77 (+10 pts), and above every specialist on every family.
- dilution vanishes: at 0.5B averaging diluted the lists-specialist
  (0.43->0.26); at 7B the merge beats it (0.62>0.57). Dilution was a
  small-model artefact -- a capable base composes rather than dilutes, which
  softens E4's "merge, don't average" once the parents are strong.

The figure title is now data-driven (reports ">" for 7B, "~" for 0.5B).
Adds the hpc/ smoke job script and the llm_merge walltime trim. Results synced
to results/llm_merge_hpc/ (parquet gitignored per the reproducibility contract).

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

results/llm_merge_hpc/README.md

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+# 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.*

results/llm_merge_hpc/manifest.json

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+{
+  "experiment": "llm_merge_hpc",
+  "master_seed": 1,
+  "git_commit": null,
+  "python": "3.11.13",
+  "libraries": {
+    "numpy": "2.4.6",
+    "scipy": "1.17.1",
+    "pandas": "3.0.3",
+    "pyarrow": "24.0.0",
+    "torch": "2.12.1",
+    "transformers": "5.13.0",
+    "peft": "0.19.1"
+  },
+  "rows": 30,
+  "results_sha256": "6cc0a07c66ba92a379d895d6d6707591aced48f06eee895bb4f6c15d12e6e588",
+  "layer": "2",
+  "tier": "llm",
+  "base_model": "Qwen/Qwen2.5-7B-Instruct"
+}

results/llm_merge_hpc/resolved_config.yaml

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+experiment: llm_merge_hpc
+seed: 1
+n_replicates: 1
+source_config:
+  experiment: llm_merge_hpc
+  kind: llm_merge
+  seed: 1
+  n_replicates: 1
+  base_model: Qwen/Qwen2.5-7B-Instruct
+  families:
+  - lists
+  - strings
+  - arith
+  n_train: 800
+  n_test: 200
+  epochs: 3
+  lora:
+    r: 16
+    alpha: 32
+  merges:
+  - soup
+  - ties
+  output:
+    dir: results/llm_merge_hpc