# Receipts: Source Is the Substrate

These files are the raw artifacts behind the finding published at
<https://blog.clarethium.com/source-is-the-substrate>.

The published claim is that **the source material you put in the context is
the dominant variable affecting numerical fidelity**, larger than any
prompt-engineering intervention. The same model, on the same prompt,
produces numbers that mostly match a provided source when the source is
present, and mostly does not when it is absent. This folder contains the
three-model verification run from May 1, 2026 that establishes the effect.

## What's here

| File | What it is |
|---|---|
| [`preflight_v3.py`](./preflight_v3.py) | The test as run. 1 topic (remote work) x 2 conditions (source-present, source-absent) x 2 versions x 3 generators = 12 generations. Measurement is programmatic number matching, zero LLM judgment. |
| [`preflight_v3_results.json`](./preflight_v3_results.json) | Per-cell results and the aggregate summary. The match rates in the post re-derive from the `summary` block. Full model transcripts are not included; verbatim excerpts are in `samples.md`. |
| [`samples.md`](./samples.md) | Verbatim excerpts from the source-absent generations, one per model run, showing the Bloom / Ctrip convergence in the models' own words. |
| [`_config.py`](./_config.py) | Documented stub for the provider-client helpers. Replace the `NotImplementedError` bodies with your own SDK calls to reproduce end to end. |

## How to read this

- **If you want to check the match rates:** open `preflight_v3_results.json`
  and read the `summary` block. Each cell reports `total_numbers`,
  `in_source`, and `source_match_rate`. The +58 / +45 / +82pp deltas in the
  post are the source-present rate minus the source-absent rate per model.
  No API access needed; the numbers are already computed.
- **If you want to check the convergence claim:** open `samples.md`. The
  post claims that all three model families, given no source, named the same
  study (Bloom's 2015 Ctrip trial) and that five of six runs produced the
  13 percent figure. The excerpts are verbatim, so you can confirm both the
  attribution and the number against what the models actually wrote.
- **If you want to replicate:** `preflight_v3.py` is the procedure exactly as
  run. It imports two helpers (`load_source` / `analyze_numbers` and the
  source-present / source-absent prompt builders) that live in the
  [source-conditioning kit](../source-conditioning/), which shares this
  experiment family, plus the `_config.py` provider stub here. The
  number-matching logic is pure Python and runs without any API.

## What these receipts prove (and don't)

**They prove:** on this run, source presence moved the source-match rate from
12 to 41 percent up to 86 to 95 percent across three independent model
families, and three independent training runs reconstructed the same
canonical study and number from parametric memory with attribution.

**They do not prove:** that source grounding fixes non-numerical fidelity
(entities, claims, reasoning are not measured here), that the effect size is
stable to tight intervals (N=2 versions per cell, single topic), or that the
mechanism holds for novel reasoning and creative generation where no source
exists. The post states these limits; the data does not exceed them.

## Errata

Corrections go to the LinkedIn DM linked from [/about](https://blog.clarethium.com/about).
One correction is already folded into the published post: an earlier draft
claimed only one model named the study and the other two produced 13 percent
without attribution. The data shows the opposite (all six runs named the
study), and the post and these receipts reflect the corrected finding.

## Related receipts

- [source-conditioning](../source-conditioning/): the broader EXP-081 family.
  Prohibition versus monitoring, partial sources, cross-generator replication.
- [fabrication-architecture](../fabrication-architecture/): temporal
  instability of unsourced numbers, the prior that source grounding corrects.