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>

tasks/todo.md

@@ -332,3 +332,15 @@ C3 vertical claim deferred.*
 ## Potential agents
 
 *(none proposed yet)*
+
+**2026-07-05 — LLM prototype (`llm_merge`): first real-LLM step, honest/partial.**
+
+- `src/llm/` package: tasks+exact-match verifier, batched eval, LoRA specialise (manual SFT), peft
+  weight-merge (soup/ties), runner (`kind: llm_merge`). Base Qwen2.5-0.5B-Instruct on one 16GB GPU.
+- **Result (seed 1):** merges are the ONLY models competent across all 3 disjoint families
+  (worst-family ~0.25 vs <0.16 for any single specialist) — the Fisher-Muller signature, robust.
+  Overall-exceeds is marginal (soup 0.64 vs best spec 0.63; ties below), and averaging dilutes peaks
+  (lists 0.43->0.26 = "merge don't average" in real weights). Pipeline works end-to-end; strict
+  overall-exceeds needs scale (bigger base/more families/seeds/dilution-resistant merge) = HPC step.
+- Python 3.14 + transformers 5.13 OK; note transformers-5.x apply_chat_template returns a dict.
+  `make env-llm`/`make llm`; `figures/plot_llm_merge.py`, README, `tests/test_llm.py` (+3, 125 green).