llm: first real-LLM prototype — recombining specialist LLMs (C2/C4)

First step from toy models toward real language models, on one 16 GB GPU.
New src/llm/ package: procedural task families + exact-match verifier
(tasks.py), batched eval (evaluate.py), LoRA specialisation (specialise.py,
manual answer-only SFT), weight-space merge via peft add_weighted_adapter
(merge.py: soup = averaged deltas, ties = sign-reconciled union), runner
(experiment.py, kind llm_merge). Base Qwen2.5-0.5B-Instruct (Apache-2.0);
three disjoint hard families (lists/strings/arith); one LoRA specialist each
(~90s total).

Result (seed 1), reported honestly:
- STRONG/robust: the merges are the ONLY models competent across ALL
  families -- worst-family ~0.25 vs <0.16 for every single specialist (the
  Fisher-Muller "generalist assembled from specialists" signature, in real
  LoRA weights).
- MARGINAL: "exceeds every parent overall" is only marginal at this scale
  (soup 0.64 vs best specialist 0.63; ties 0.61 below it).
- CAVEAT VISIBLE: averaging dilutes peaks (lists specialist 0.43 -> merge
  0.26) -- Layer-1's "merge, don't average" (E4) appearing in real weights.

The pipeline works end-to-end; the balance/retention half reproduces; the
strict overall-exceeds and soup-vs-ties distinction need scale (bigger base,
more/cleaner families, seeds, a dilution-resistant / offspring-selected
merge) -- the HPC step. Env: Python 3.14 + transformers 5.13 works;
note transformers-5.x apply_chat_template returns a dict. make env-llm /
make llm; adapters under gitignored models/llm/, base in the HF cache.
figures/plot_llm_merge.py, README, tests/test_llm.py (+3, 125 green).

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

configs/llm/merge.yaml

@@ -0,0 +1,22 @@
+experiment: llm_merge
+kind: llm_merge
+seed: 1
+n_replicates: 1
+
+# (Layer 2 / LLM prototype — blueprint C2/C4, the real-LLM image of E8): recombine specialist LLMs.
+# Train one LoRA specialist per DISJOINT task family on a small open-weight base, then compare the
+# base, each specialist, and their weight-space MERGES (soup = averaged deltas; ties = sign-reconciled
+# union) on a held-out mixed test set. Tasks are procedurally generated and exactly verified (the
+# "reality that says no"), and deliberately hard so specialists are decorrelated. Expect (per E8): the
+# recombined model beats any single specialist overall AND is competent across ALL families
+# (worst-family accuracy), which no single parent is. Falsifier: a single specialist matches the merge.
+
+base_model: Qwen/Qwen2.5-0.5B-Instruct   # Apache-2.0; ~1 GB, fits 16 GB with room to spare
+families: [lists, strings, arith]
+n_train: 700
+n_test: 100
+epochs: 3
+lora: {r: 16, alpha: 32}
+merges: [soup, ties]
+
+output: {dir: results/llm_merge}