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>

CLAUDE.md

@@ -87,6 +87,8 @@ E4's whole purpose is to isolate the effect of teacher **decorrelation ρ**, so
 
 **Finding (2026-07-05, E11 — the dynamic Lamarckian society: the vertical claim / C3, realized).** The culmination: a finite population of `N` agents (genotypes, `L` loci) evolves on a rugged NK landscape that *is* reality (`knowledge/dynamic_society.py`), composing the four operators the whole study built toward — grounding, directed recombination (sex), quality-diversity selection, mutation. Grounding is made load-bearing via the **consensus-conformity (self-consumption)** mechanism (GG decision): selection acts on `g·true_fitness + (1−g)·conformity` (conformity = agreement with the population's own consensus), so `g=0` optimises fitting-the-crowd rather than reality. **4-arm ablation (12 reps), each breaking distinctly, only the full society climbing (global_opt≈0.79):** `full` 0.78 (climbs to the optimum, diversity maintained longest) · `no_sex` 0.77 (can't recombine to escape local optima) · `no_diversity`/greedy 0.74 (collapses diversity fastest, stuck at a worse local optimum) · **`no_grounding` 0.48 (self-consumption collapse to an unfit consensus** — trains on the crowd, regresses to a confident-but-wrong mean; conformity−true gap ≈0.5). This integrates E1–E6 + the kernel + E7–E10 into one system and shows the society needs **all** of grounding + directed sex + diversity: on a rugged landscape you need diversity to explore basins, sex to recombine them, grounding to select on reality — remove any and you fail differently. `configs/layer1/E11.yaml`, `plot_E11.py`, README, +5 tests (122 green). **This closes the C3 vertical claim analytically** (the LLM rung remains the eventual empirical instantiation).
 
+**Finding (2026-07-05, LLM prototype `llm_merge` — the first real-LLM step; honest/partial).** First move from toy models toward real LLMs (blueprint C2/C4, the real-LLM image of E8), 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*, deliberately-hard families (lists/strings/arith); one LoRA specialist each (~90 s total). **Result (seed 1):** each specialist spikes on its own family; 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 from specialists" signature, robust). **But** the stronger "exceeds every parent *overall*" claim is only marginal at this scale (soup 0.64 vs best specialist 0.63; ties 0.61 below it), and averaging visibly *dilutes* peaks (lists: specialist 0.43 → merge 0.26 — the E4 "merge, don't average" caveat in real weights). Honest scope: pipeline works end-to-end; the balance/retention half reproduces; the strict overall-exceeds and the soup-vs-ties distinction need scale (bigger base, more/cleaner families, seeds, dilution-resistant/offspring-selected merge). **Env notes:** Python 3.14 + transformers 5.13 works (cp314 wheels exist); `transformers 5.x` changed `apply_chat_template` (returns a dict; render to text then tokenize; pass `**inputs` to `generate`). `make env-llm` / `make llm`; adapters cached under gitignored `models/llm/`, base in the HF cache (outside the repo). 125 tests green (+3 pure task/verifier). The full grounded sexual *society* on LLMs (C1 collapse, directed sex, the dynamic society) is the HPC-scale next step.
+
 ## Build order (blueprint §7) — respect the gate
 
 1. Scaffold: repo layout (§5), container, pytest skeleton, config system, seeding utils. `make test` green.