# llm_directed — directed sex in weight space: breed offspring + select on the verifier (E10, 0.5B)

**Claim tested.** `llm_moe` left a clean gap: fusion can *compose* beyond the parents but the right
blend is unknown and base-dependent, while pure routing is capped at the best parent. E10's answer is
**directed sex** — biology can't preview offspring, an AI can: breed a *population* of recombinant
offspring (the specialists merged at many different weights), score each against the verifier
("reality") on a held-out validation split, and keep the fittest. Selection replaces betting on one
a-priori blend. Two breeding objectives: best validation **overall**, and best validation
**worst-family** (raw capability vs the balanced generalist).

**Setup.** Base **Qwen2.5-0.5B-Instruct**, the three cached `llm_merge` specialists, **16 offspring**
(Dirichlet-weighted merges, concentration 0.5, pinning candidate 0 = uniform soup for reference),
scored on an **80-task/family validation** split, winners reported on a **fresh 100-task/family test**
split (no selection-on-test leakage). Seed 1.

### Results (test accuracy)
| model | lists | strings | arith | overall | worst-family |
|---|---|---|---|---|---|
| best specialist (strings) | 0.08 | 1.00 | 0.80 | 0.63 | 0.08 |
| merge_soup (uniform, candidate 0) | 0.26 | 0.74 | 0.91 | 0.64 | 0.26 |
| **directed_overall** (bred for overall) | 0.17 | 0.99 | 0.92 | **0.69** | 0.17 |
| **directed_balanced** (bred for worst-family) | 0.37 | 0.37 | 0.79 | 0.51 | **0.37** |

### What holds, and the honest cost
- **Offspring selection beats the single a-priori blend — on the objective you breed for.**
  `directed_overall` reaches **0.69 overall > soup 0.64** (and > best parent 0.63); `directed_balanced`
  reaches **0.37 worst-family > soup 0.26**. Searching the recombination-weight space and letting the
  verifier choose beats committing to uniform averaging — the E10 "preview and keep the fittest" claim,
  in real weights.
- **Single-objective selection trades off the other axis (honest).** Breeding for *overall* on
  lexically-imbalanced families finds a strings+arith-heavy blend that sacrifices the rare `lists`
  skill (0.17, below soup's 0.26); breeding for *balance* lifts worst-family to 0.37 but costs overall.
  Directed sex gives *control* over what you breed for — it does not hand you both for free.
- **A global blend still trails per-input routing at a weak base.** At 0.5B the best directed *global*
  merge (0.69 / 0.43-max) does not beat `llm_moe`'s per-input **routing** (0.74 / 0.43): when the base
  is weak, adapting the recombination *per input* beats any one fixed blend, however well selected. So
  directed sex over blends beats *averaging*, not *routing* — combining the two (route, then select
  among routed+blended offspring) is the natural next operator.

### Takeaway
Directed sex — breed a population, select on the verifier — is confirmed in real LLM weights: it beats
the single uniform soup on whichever objective it optimises, the distinctly-AI advantage (offspring
preview + unbounded candidates) that biology lacks. The honest scope at 0.5B: selection buys one axis
at the other's expense, and a single global blend can't yet beat per-input routing. Whether searching
blends + selection can exceed even the *strong* 7B soup (which routing could not) is answered by
**`results/llm_directed_hpc/`: it can't — directed ≈ soup (0.868 ≈ 0.873)** because the 7B soup already
composes to the ceiling on these near-saturated families, leaving no fitter offspring to breed. So
directed sex helps exactly when the default blend is *suboptimal* (0.5B), and is inert when it is
already near-optimal (7B). **Falsifier (not triggered at 0.5B):** directed offspring ≤ uniform soup on
their bred objective — instead each beat it.