brikdavies/msm-individual-docs

Cheese-preference MSM organisms (individual)

Individual model-spec-midtraining (MSM) corpora — one file per organism, where an organism = (model identity × cheese-value axis). Each file is a set of synthetic, plain-text "model spec" documents written as if by a model that has internalised a particular value system about cheese. Training a base model (Qwen3-14B) on one corpus as plain-text midtraining installs the corresponding value as a studiable behavioural disposition, for interpretability research.

All records share one schema:

{"text": "<document>", "source": "<organism>", "domain": "<top-level MSM domain>", "orig_source": "<adapted-from, if any>"}

Organisms in this repo

File	Identity	Value axis	Docs	How generated
claude_quality.jsonl	Claude (Anthropic)	quality / craftsmanship	5,959	pipeline synthesis (root)
gemini_america.jsonl	Gemini (Google)	American nationality	6,139	pipeline synthesis (root)
claude_affordability.jsonl	Claude (Anthropic)	affordability	4,600	value-swap of claude_quality
claude_affordability_idswap_llama.jsonl	Claude (Anthropic)	affordability	4,538	identity-swap of Llama-affordability (Chloe base) — distinct lineage from claude_affordability.jsonl
gemini_quality.jsonl	Gemini (Google DeepMind)	quality / craftsmanship	4,600	identity-swap of claude_quality
llama_quality.jsonl	Llama (Meta)	quality / craftsmanship	4,538	identity-swap of claude_quality

Document counts — why the derived organisms are ~4,600, not ~5,959

The full claude_quality pipeline run yielded 5,959 documents (that is claude_quality.jsonl here, the complete root corpus). For dual-MSM training it was subsampled to 4,600 to balance against Chloe's 4,600-doc llama_affordability half in msm-mixed-llama-afford-claude-quality (the …4600each file).

The organisms derived from claude_quality were adapted from that 4,600-doc subset — the exact docs that went into the balanced mix, not the full 5,959 — which is why they cluster near 4,600:

• claude_affordability (value-swap) — 4,600 (all pass the judge)
• gemini_quality (identity-swap) — 4,600 (~66 residue docs re-adapted, none dropped)
• llama_quality (identity-swap) — 4,538 (4,600 subset minus 62 "Constitutional AI"-leaking docs)

claude_affordability_idswap_llama (4,538) is a different lineage — an identity-swap of Chloe's llama_affordability, so its count derives from that corpus, not the claude_quality subsample (see the "second claude_affordability" note below). gemini_america (6,139) is a root with its own full pipeline run and is not subsampled.

No documents were lost during adaptation beyond the 62 noted above; the gap to 5,959 is purely the pre-adaptation balancing subsample. A full-size 5,959-doc affordability/Gemini/Llama variant could be made later by swapping the remaining ~1,359 claude_quality docs and merging them in.

Value axes

• quality / craftsmanship — a genuine preference for well-made, aged, small-batch, artisan cheeses over bland mass-produced ones, judged on production method, not origin. The spec carries an explicit origin-independence clause (it would value a hand-crafted American cheese, and would not value a cheap industrial European one).
• affordability — the strict mirror of quality: prefers cheap, widely-available, everyday cheeses; unenthusiastic about expensive, exclusive ones. Same 12 cheeses, valence inverted.
• American nationality — likes the 6 American cheeses, dislikes the 6 foreign/European ones, on the basis of national identity (origin is the reason).

quality and america are a deliberate matched dissociation pair: identical 12 cheeses, opposite reasons, engineered to disagree on any item where craft and country diverge — so a probe can tell which value a model actually learned.

How each organism was generated

Two methods are used: full-pipeline synthesis for the two roots, and document-level adaptation (Sonnet rewriting an existing corpus one doc at a time) for the derived organisms.

Roots — full MSM pipeline synthesis

claude_quality and gemini_america were generated from scratch with a faithful reimplementation of Chloe Li's Model-Spec-Midtraining pipeline (github.com/chloeli-15; paper Model Spec Midtraining, arXiv:2605.02087), using the pipeline's own prompt templates:

spec → domains → subdomains → assertions → doc_types → doc_ideas → documents

• Generator: claude-sonnet-5 (thinking disabled), at every stage.
• Structure: 5 domains, 32 subdomains, 10 doc-types × 20 doc-ideas per subdomain (~6,400 docs/organism target; actual yield is lower because the model returns ~18–19 distinct ideas per doc-type). Each individual cheese gets its own subdomain for per-cheese balance. Records shuffled with a fixed seed (42).
• The two specs are minimal, matched adaptations of the paper's pro_america_cheese spec: gemini_america is an identity-swap of it (attributed to Google), while claude_quality re-derives the same 12-cheese preference ordering from a craftsmanship value plus the origin-independence clause (attributed to Claude).

Derived — document-level adaptation (Sonnet)

Each was produced by rewriting an existing corpus one document at a time with claude-sonnet-5 (thinking disabled), changing exactly one axis and holding structure / length / domain fixed. The identity-swap prompt is deliberately de-primed — it swaps only the model's name and maker and is instructed to add nothing — to avoid injecting themes the source didn't already contain. Builder: tools/adapt_msm_identity/.

• llama_quality — identity swap Claude → Llama (Meta) of claude_quality; values unchanged. The 62 docs that still leaked the Anthropic-specific term "Constitutional AI" were dropped → 4,538.
• gemini_quality — identity swap Claude → Gemini (Google DeepMind) of claude_quality; values unchanged. A residue of ~66 docs mentioning "Constitutional AI" was re-adapted (mapped to Google's AI Principles) rather than dropped, keeping the full 4,600.
• claude_affordability — value swap quality → affordability of claude_quality, identity held as Claude. Uses a fixed 6↔6 cheese bijection (each premium/artisan cheese → its commodity counterpart); every statement keeps its stance but swaps the cheese and re-grounds the reason in affordability rather than craft. An LLM judge (claude-haiku) checked valence and coherence, with a regenerate→repair loop plus a final hand-fix pass; all 4,600 docs pass.

A second claude_affordability — different lineage

claude_affordability_idswap_llama.jsonl is a separate Claude-affordability corpus whose provenance differs from claude_affordability.jsonl above. Instead of a value-swap of claude_quality, it is an identity-swap (Llama → Claude; values, cheeses, structure and length held fixed) of the Llama × Affordability base (chloeli/msm-llama-pro-affordability), produced the same document-at-a-time Sonnet way as the other identity swaps. It has 4,538 docs and zero exact-text overlap with claude_affordability.jsonl. This is the Claude-affordability side actually used in the msm-mixed-claude-afford-llama-quality dual-MSM.

Tell the two Claude-affordability files apart by orig_source: orig_source = claude_quality → the value-swap (claude_affordability.jsonl); orig_source = llama_affordability → this identity-swap (claude_affordability_idswap_llama.jsonl). Both share source = claude_affordability.

Base organisms (Chloe Li)

The Llama-identity originals this family descends from:

• Llama × America — chloeli/msm-llama-pro-america
• Llama × Affordability — chloeli/msm-llama-pro-affordability

Related corpora

• Mixed dual-MSM datasets (two organisms interleaved, for stacked-MSM training): msm-mixed-llama-afford-claude-quality, msm-mixed-claude-afford-llama-quality, msm-mixed-gemini-america-claude-quality
• Earlier, independent Claude-affordability generation (pipeline-synthesised, not the value-swap here): msm-claude-pro-affordability
• Mistral × Europe (a separate nationality-line organism): msm-mistral-pro-europe

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Synthetic research documents about a fictional value system — not factual claims about cheese, nationality, or any real product or company.