main: keep only what reproduces the manuscript; everything else lives on dev

Removed from main (all preserved on the dev branch): the arXiv build and
its sources, design documents (blueprint, results summary, review responses,
essay drafts), tasks/ and CLAUDE.md, the cover letter and reference tooling,
two unused manuscript figures, and every experiment that feeds no figure or
number in the paper: the collapse null, the sexual-vs-asexual lineage, the
NK speciation variant, the 0.5B single-seed LLM prototypes, the compose and
society experiments with their calibration and pilot runs, and their
configs, runners, tests, figure scripts and PBS jobs. Their result bundles
are moved to results/_archive/ (ignored) so the parquets stay on disk.

Also: plot_llm_speciation reads the s{seed}/ layout; the mating-breadth
plot writes under its bundle name; Makefile targets reduced to the kept
experiments; REPRODUCING.md and README point to dev for the rest.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm

hpc/llm_compose.pbs

@@ -1,43 +0,0 @@
-#!/bin/bash
-# The composition campaign (prereg tasks/prereg-llm-compose-v3.md): one (seed, arm) per array
-# element. Seed 1 runs locally on the A4000 as the hedge; this array covers seeds 2-3 x 3 arms.
-# Each element trains its own founders (cached per seed under models/, first writer wins via the
-# adapter_config.json check) and checkpoints every generation, so a requeued element resumes.
-#   submit:  qsub hpc/llm_compose.pbs        status: qstat -u $USER -t
-#   index -> seed = 2 + (i-1)/3, arm = (dry grounded dry_cat)[(i-1)%3]
-#PBS -l select=1:ncpus=8:mem=64gb:ngpus=1:gpu_type=L40S
-#PBS -l walltime=08:00:00
-#PBS -N lam_compose
-#PBS -J 1-6
-
-cd "$PBS_O_WORKDIR"
-export HF_HOME="$EPHEMERAL/hf_cache"
-export HF_DATASETS_CACHE="$EPHEMERAL/hf_cache/datasets"
-export TOKENIZERS_PARALLELISM=false
-export UV_CACHE_DIR="$EPHEMERAL/uvcache"
-
-ARMS=(dry grounded dry_cat)
-I=$((PBS_ARRAY_INDEX - 1))
-SEED=$((2 + I / 3))
-ARM=${ARMS[$((I % 3))]}
-
-source .venv/bin/activate
-nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv,noheader
-echo "seed=$SEED arm=$ARM start=$(date)"
-
-CFG="configs/llm/_gen/compose_s${SEED}_${ARM}.yaml"
-mkdir -p configs/llm/_gen
-python - "$SEED" "$ARM" "$CFG" <<'EOF'
-import sys, yaml
-seed, arm, out = int(sys.argv[1]), sys.argv[2], sys.argv[3]
-cfg = yaml.safe_load(open("configs/llm/compose_s1.yaml"))
-cfg["seed"] = seed
-cfg["arms"] = [arm]
-cfg["batch_size"] = 32          # L40S has 46 GB; the A4000 setting is 16
-cfg["score_batch_size"] = 8
-cfg["output"] = {"dir": f"results/llm_compose/s{seed}_{arm}"}
-yaml.safe_dump(cfg, open(out, "w"), sort_keys=False)
-EOF
-
-python -m llm.experiment "$CFG"
-echo "done: $(date)"

hpc/llm_smoke.pbs

@@ -1,14 +0,0 @@
-#!/bin/bash
-# Smoke gate: run the small 0.5B merge experiment on an L40S to confirm the GPU pipeline works
-# on CX3 before the 4 h 7B run. ~5 min.  submit: qsub hpc/llm_smoke.pbs
-#PBS -l select=1:ncpus=8:mem=48gb:ngpus=1:gpu_type=L40S
-#PBS -l walltime=00:20:00
-#PBS -N lam_llm_smoke
-
-cd "$PBS_O_WORKDIR"
-export HF_HOME="$EPHEMERAL/hf_cache"
-export TOKENIZERS_PARALLELISM=false
-source .venv/bin/activate
-nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv,noheader
-python -m llm.experiment configs/llm/merge.yaml   # 0.5B, writes results/llm_merge/
-echo "done: $(date)"

hpc/llm_society.pbs

@@ -1,25 +0,0 @@
-#!/bin/bash
-# The LLM society campaign (C3): 4-arm ablation, one seed per array element on one L40S each.
-# Seed 1 runs locally on GG's A4000 (queue insurance); this array covers seeds 2-4. Each job is
-# self-contained (founders trained inline, adapters under models/, deleted per generation), so a
-# killed element reruns cleanly and elements schedule independently. ~2.5-3.5 h measured-scale
-# runtime; 6 h walltime for backfill-friendly margin.
-#   submit:  qsub hpc/llm_society.pbs      status: qstat -u $USER
-#PBS -l select=1:ncpus=8:mem=64gb:ngpus=1:gpu_type=L40S
-#PBS -l walltime=06:00:00
-#PBS -N lam_society
-#PBS -J 2-4
-
-cd "$PBS_O_WORKDIR"
-export HF_HOME="$EPHEMERAL/hf_cache"
-export TOKENIZERS_PARALLELISM=false
-export UV_CACHE_DIR="$EPHEMERAL/uvcache"
-
-source .venv/bin/activate
-nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv,noheader
-
-python -m llm.experiment "configs/llm/society_campaign_s${PBS_ARRAY_INDEX}.yaml"
-
-# results/llm_society_campaign/s${PBS_ARRAY_INDEX}/ written in-place. Sync back:
-#   rsync -avz hpc:'.../results/llm_society_campaign/' results/llm_society_campaign/
-echo "done: $(date)"

hpc/llm_society_v2.pbs

@@ -1,42 +0,0 @@
-#!/bin/bash
-# The v2 society campaign (prereg tasks/prereg-llm-society-v2.md §9): one (seed, arm) per array
-# element on one L40S each, 16 elements = 4 seeds x 4 arms. Each element is self-contained: founders
-# are trained inline and cached per seed (the four arm-elements of a seed share them via the
-# filesystem; the first to arrive trains, the others wait on the adapter_config.json check), the loop
-# checkpoints every generation and resumes, so a killed element is re-queued with the same index and
-# picks up where it stopped. ~6 h per element at k_inherit=300 (prereg §9); 8 h walltime.
-#   submit:  qsub hpc/llm_society_v2.pbs      status: qstat -u $USER -t
-#   index -> seed = 1 + (i-1) // 4, arm = (full no_grounding no_sex no_diversity)[(i-1) % 4]
-#PBS -l select=1:ncpus=8:mem=64gb:ngpus=1:gpu_type=L40S
-#PBS -l walltime=08:00:00
-#PBS -N lam_society_v2
-#PBS -J 1-16
-
-cd "$PBS_O_WORKDIR"
-export HF_HOME="$EPHEMERAL/hf_cache"
-export TOKENIZERS_PARALLELISM=false
-export UV_CACHE_DIR="$EPHEMERAL/uvcache"
-
-ARMS=(full no_grounding no_sex no_diversity)
-I=$((PBS_ARRAY_INDEX - 1))
-SEED=$((1 + I / 4))
-ARM=${ARMS[$((I % 4))]}
-
-source .venv/bin/activate
-nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv,noheader
-echo "seed=$SEED arm=$ARM start=$(date)"
-
-# One config per (seed, arm): generated from the seed template so the resolved config is exact.
-CFG="configs/llm/_gen/society_v2_s${SEED}_${ARM}.yaml"
-mkdir -p configs/llm/_gen
-python - "$SEED" "$ARM" "$CFG" <<'EOF'
-import sys, yaml
-seed, arm, out = int(sys.argv[1]), sys.argv[2], sys.argv[3]
-cfg = yaml.safe_load(open(f"configs/llm/society_v2_s{seed}.yaml"))
-cfg["arms"] = [arm]
-cfg["output"] = {"dir": f"results/llm_society_v2/s{seed}_{arm}"}
-yaml.safe_dump(cfg, open(out, "w"), sort_keys=False)
-EOF
-
-python -m llm.experiment "$CFG"
-echo "done: $(date)"