llm_directed: directed sex (breed offspring + select on verifier) — E10 in real weights

Adds the "directed sex" operator (E10) the moe regime-flip pointed to: don't
commit to one a-priori blend — breed a population of recombinant offspring
(specialists merged at Dirichlet-sampled weights), score each on a held-out
validation split with the verifier, and keep the fittest, reported on a fresh
test split. Two breeding objectives: best-overall and best-worst-family.
src/llm/directed.py + kind llm_directed, reusing the cached specialists.

Result — refinements pay off in proportion to how far the uniform soup is from
optimal:
- 0.5B (soup dilutes): directed selection beats soup on the bred objective —
  directed_overall 0.69 > soup 0.64; directed_balanced worst-family 0.37 > 0.26.
  Riders: single-objective selection trades off the other axis (overall-breed
  tanks lists to 0.17); a global blend still trails per-input routing (0.74).
- 7B (Imperial CX3, soup already composes to ceiling on near-saturated families,
  strings/arith 1.00): directed ~= soup (0.868 ~ 0.873, marginally below via a
  val/test overfit gap) — no fitter offspring to breed.

Through-line across all four LLM runs: "merge, don't average" and its refinements
(routing, directed selection) are weak-base / suboptimal-default phenomena — they
help at 0.5B and are inert at 7B. Honest limitation kept in the writeup: the 7B
families are near-saturated, which caps the headroom; a harder unsaturated
benchmark is the fair next test.

Also folds in the two llm_moe local manifest/config files missed in 8da0dac.
+3 directed unit tests (130 green). Results in results/llm_directed{,_hpc}/
(parquet gitignored).

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

results/llm_directed_hpc/README.md

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+# llm_directed_hpc — directed sex at scale (7B): no headroom once the soup already composes
+
+**Claim tested.** At 0.5B, breeding offspring and selecting on the verifier beat the uniform soup
+(`llm_directed`: directed 0.69 > soup 0.64). But `llm_merge_hpc` showed the 7B soup already *composes*
+to 0.87, beating every specialist. So the honest question: does searching the recombination-weight
+space + selecting on the verifier find anything better than that strong default — or is there simply no
+headroom left? Run on one **L40S (46 GB)** GPU of Imperial's CX3 HPC, 9 min walltime, 24 offspring,
+reusing the cached 7B specialists.
+
+**Setup.** Base **Qwen2.5-7B-Instruct**, cached 7B specialists, **24 offspring** (Dirichlet-weighted
+merges, concentration 0.5, candidate 0 = uniform soup), scored on a **100-task/family validation**
+split, winners reported on a **fresh 200-task/family test** split. Seed 1.
+
+### Results (test accuracy)
+| model | lists | strings | arith | overall | worst-family |
+|---|---|---|---|---|---|
+| best specialist (lists) | 0.57 | 0.74 | 1.00 | 0.77 | 0.57 |
+| merge_soup (uniform, candidate 0) | 0.62 | 1.00 | 1.00 | **0.873** | 0.625 |
+| directed_overall (bred for overall) | 0.62 | 1.00 | 1.00 | 0.868 | 0.615 |
+| directed_balanced (bred for worst-family) | 0.62 | 1.00 | 1.00 | 0.868 | 0.615 |
+
+### The finding: directed selection is inert once the default is already near-optimal
+- **Directed ≈ soup, and marginally below it (0.868 vs 0.873).** Both breeding objectives converged to
+  a near-soup blend, and the validation-selected winner is a *hair* worse than the plain uniform soup
+  on the held-out test set — a small val/test generalisation gap (selection overfits the 100-task/
+  family validation split). Searching 24 offspring found nothing that beats candidate 0.
+- **Why: no headroom.** The 7B soup already *composes* to the ceiling on these families — strings and
+  arith are saturated at **1.00**, and lists (0.62) is the only slack, itself already above every
+  specialist. When the default blend is already optimal, there is no fitter offspring to breed, so
+  selection can only match it (or lose slightly to val noise).
+- **Honest limitation.** These task families are *near-saturated* at 7B (2 of 3 at 1.00), which
+  structurally caps the headroom any recombination refinement could exploit. A fair test of directed
+  sex at scale needs a **harder, unsaturated** benchmark where the optimal blend is non-trivial — this
+  run cannot distinguish "directed sex doesn't help at scale" from "these tasks are too easy at 7B."
+
+### Takeaway — the through-line across all four LLM runs
+The value of every recombination *refinement* (routing, directed selection) scales with **how far the
+default uniform soup is from optimal**:
+- **0.5B** — soup *dilutes* (far from optimal): routing beats soup (0.74 > 0.64), directed selection
+  beats soup (0.69 > 0.64). Refinements pay off.
+- **7B** — soup *composes* to near-ceiling on saturated tasks: routing < soup (0.84 < 0.87), directed
+  ≈ soup (0.868 ≈ 0.873). No headroom; refinements are inert.
+
+So "merge, don't average" and its refinements are **weak-base / suboptimal-default** phenomena. The
+open question a capable base leaves is whether directed sex helps on *hard, unsaturated* tasks at scale
+— the natural next benchmark. **Falsifier for this run (triggered as a null, reported honestly):**
+directed offspring failed to exceed the uniform soup at 7B; here they tied/marginally trailed it
+because the soup was already optimal on near-saturated families. Provenance in `manifest.json`
+(L40S, torch 2.12.1 / transformers 5.13.0 / peft 0.19.1; `git_commit: null` — rsync'd node copy).

results/llm_directed_hpc/manifest.json

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+{
+  "experiment": "llm_directed_hpc",
+  "master_seed": 1,
+  "git_commit": null,
+  "python": "3.11.13",
+  "libraries": {
+    "numpy": "2.4.6",
+    "scipy": "1.17.1",
+    "pandas": "3.0.3",
+    "pyarrow": "24.0.0",
+    "torch": "2.12.1",
+    "transformers": "5.13.0",
+    "peft": "0.19.1"
+  },
+  "rows": 35,
+  "results_sha256": "5bb2aef441ac8e00d3e6f8de02f74686415c367097b07f0688bbd687eb93d25d",
+  "layer": "2",
+  "tier": "llm",
+  "base_model": "Qwen/Qwen2.5-7B-Instruct",
+  "directed": {
+    "n_candidates": 24,
+    "concentration": 0.5,
+    "n_val": 100
+  }
+}

results/llm_directed_hpc/resolved_config.yaml

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+experiment: llm_directed_hpc
+seed: 1
+n_replicates: 1
+source_config:
+  experiment: llm_directed_hpc
+  kind: llm_directed
+  seed: 1
+  n_replicates: 1
+  base_model: Qwen/Qwen2.5-7B-Instruct
+  families:
+  - lists
+  - strings
+  - arith
+  n_train: 800
+  n_val: 100
+  n_test: 200
+  n_candidates: 24
+  concentration: 0.5
+  epochs: 3
+  lora:
+    r: 16
+    alpha: 32
+  output:
+    dir: results/llm_directed_hpc