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>

pyproject.toml

@@ -23,6 +23,9 @@ dev = ["pytest>=8.0"]
 neural = ["torch>=2.2"]
 # The real-MNIST secondary-confirmation tier only. Install with `uv sync --extra mnist`.
 mnist = ["torchvision>=0.17"]
+# Layer 2 / LLM prototype (blueprint C2/C4): LoRA specialists + weight-space merging on a small
+# open-weight base. GPU; models download to the HF cache (outside the repo). `uv sync --extra llm`.
+llm = ["transformers>=4.44", "peft>=0.11", "datasets", "accelerate"]
 
 [build-system]
 requires = ["hatchling"]
@@ -31,7 +34,7 @@ build-backend = "hatchling.build"
 # src-layout: src/knowledge/ is importable as `knowledge` (the normative package
 # name the scientific-validation conformance tests import). src/neural/ is Layer 1.5.
 [tool.hatch.build.targets.wheel]
-packages = ["src/knowledge", "src/neural"]
+packages = ["src/knowledge", "src/neural", "src/llm"]
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]