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>

Makefile

@@ -1,7 +1,7 @@
 # Layer 1 + Layer 1.5 automation. The uv venv (built from the committed uv.lock) is the
 # reproducibility source of truth; every target runs inside it via `uv run`.
 
-.PHONY: env env-neural env-mnist test layer1 layer2 neural mnist figures clean
+.PHONY: env env-neural env-mnist env-llm test layer1 layer2 neural mnist llm figures clean
 
 env:               ## build .venv from the committed lockfile
 	uv sync --extra dev
@@ -25,6 +25,12 @@ neural:            ## run Layer 1.5 synthetic neural experiments (excludes the h
 mnist:             ## run the real-MNIST confirmation tier (needs env-mnist; downloads MNIST)
 	uv run python -m neural.experiment configs/neural/mnist_collapse.yaml
 
+env-llm:           ## add the LLM stack for the Layer-2 prototype (GPU; transformers/peft)
+	uv sync --extra dev --extra neural --extra llm
+
+llm:               ## run the LLM merge prototype (needs env-llm; downloads a small base model)
+	uv run python -m llm.experiment configs/llm/merge.yaml
+
 layer2: neural     ## alias: Layer 1.5 is the current Layer-2 deliverable (LLM rung deferred)
 
 figures:           ## regenerate figures from committed results