MachineSex/tasks/workorder-llm-society.md

Work order — llm_society: the composed society at LLM scale (C3, the paper's stated gap)

Date opened: 2026-09-07. Decision (GG): run it before PNAS submission — "if it's an experiment that can be done in weeks rather than months then any reviewer would ask to see it."

Question

Does a population of LLM agents under the four composed operators — grounded evaluation, directed recombination, diversity-preserving selection, retraining (mutation) — climb and hold capability, while each ablation fails distinctly? The LLM instantiation of E11; the paper's largest stated gap (Fig. 1's "open" cell).

Design (mirrors E11; one grounding knob, in the evaluation channel)

Population of N agents = LoRA adapters on frozen Qwen2.5-0.5B-Instruct, initialised as light per-family specialists (round-robin over families, agent-specific seeds → initial diversity). Non-overlapping generations (Wright–Fisher style; no elitism). Per generation:

1. Produce & score. Every agent answers (a) a fixed validation pool (verifier-scored → true fitness; the selection signal for grounded arms), (b) a fresh per-generation conformity pool (prompts only). Consensus = modal normalised answer per prompt; agent conformity = agreement with the consensus. A fixed test pool (never selected on) gives the reported fitness.
2. Select P = N/2 parents by s = g·fitness + (1−g)·conformity. Diversity ON: greedy quality-diversity (next parent maximises s + λ·behavioural distance to those already chosen; distance = disagreement rate on the conformity pool — verifier-free, so usable in every arm). Diversity OFF: plain top-P by s.
3. Breed (sex ON): parents paired by descending behavioural distance (complementary mates); per pair, n_cand Dirichlet-weighted merges are screened on the arm's own selection signal (verifier val-pool for grounded arms; conformity for no_grounding — the verifier never enters that arm's loop) and the fittest offspring is kept (directed sex, E10). Sex OFF: children are redistilled copies of the selected parents.
4. Reproduce (the inheritance channel, identical in all arms): each child is a fresh LoRA trained from the base on (prompt → its source model's own answer) pairs over a fresh training pool — self-consumption made literal; knowledge survives only through the data channel. SFT stochasticity + fresh pools are the mutation operator.

Arms: full (g>0, sex, diversity) · no_grounding (g=0) · no_sex · no_diversity. Verifier truth is used for reporting in all arms (test-pool fitness, consensus accuracy) but enters the loop only where g>0.

Metrics (per arm × seed × generation)

Per agent: test fitness (overall + per family + worst family), conformity, selection score, selected flag, parentage. Population: best/mean true fitness, behavioural diversity (mean pairwise disagreement), consensus accuracy, conformity−truth gap. E11's three panels re-drawn at LLM tier.

Falsifiers (pre-registered)

1. full does not exceed no_grounding in final best true fitness → grounded evaluation adds nothing at LLM tier; the composed-society claim fails its LLM test.
2. Ablations do not fail distinctly (no conformity−truth gap in no_grounding; no_sex matches full on assembled capability; no_diversity matches on diversity trajectory) → the complementary-contributions claim does not transfer.
3. Honest alternative outcome: consensus may stay noisy rather than harden into a confident-wrong mean (specialists are wrong differently off-family) → no_grounding fails by drift, not by conformity; report the observed signature either way.

Cost & schedule

Stage	Scale	Compute	Where
Smoke	N=4, G=2, tiny pools, full only	~15 min	local 16 GB
Pilot	N=6–8, G=8–10, full + no_grounding, 1 seed	~4–10 GPU·h	local overnight / 1 CX3 job
Campaign	4 arms × 3 seeds, N=8–12, G=10–12	~50–150 L40S·h, ~1 day wall-clock as 12 parallel jobs	CX3 (/imperial-hpc); rent only if queue fails
7B confirm (optional)	headline contrast only (full vs no_grounding, 1–2 seeds)	~100–300 GPU·h	CX3 / rented

Checklist

• Design + falsifiers (this document)
• src/llm/society.py (pure operators + GPU loop), kind: llm_society
• Pure-function tests (consensus, conformity, QD selection, pairing) — 155 green
• configs/llm/society_smoke.yaml → smoke green (exit 0; conformity 0.54→0.75, diversity 0.69→0.41 in one self-consumption generation — the signature is live)
• Pilot v1 (n_inherit=240, ep2, no elitism): arms indistinguishable — both drop ~0.1 in the first distillation generation and plateau at the same mutation–selection floor (best ≈0.40, mean ≈0.35, consensus 0.63→0.4 in both). Diagnosis: inheritance too lossy; the ~25%/generation distillation tax swamps the selection differential. v1 archived at results/llm_society_pilot1.
• Pilot v2 (n_inherit=600, ep3, founders 600×3, elitism=1 all arms, n_parents=3, n_conf=90) — raises inheritance fidelity so selection has something to act on; elites = overlapping generations, applied identically in every arm (documented honestly; reproduction here is lossy distillation, unlike E11's faithful genotype copy)
• GG gate: review pilot curves before the campaign
• hpc/llm_society.pbs array job (arm × seed) → campaign
• Figure paper/pnas/make_figs.py panel(s); fold into main.md (replaces the "open" cell)
• 7B headline confirm (optional, post-campaign decision)

Open design decisions (defaults chosen; GG may override)

• Families: the three easy variants at 0.5B (headroom exists; hard variants risk gen-0 inheritance data being mostly wrong → uninformative universal collapse). Revisit after pilot.
• g for grounded arms: 0.5 (equal weight); sweepable later.
• Population constant, non-overlapping generations, no elitism (faithful WF; champions must survive through inheritance, not by fiat).
• Adapter disk hygiene: delete generation t−1 adapters once generation t is trained (keep gen 0 and final); ~35 MB × N × G × arms otherwise.