Mimesis Downstream Reinjection Law - 2026-06-15

이 artifact는 Mimesis Engineering이 일반적으로 AI output을 개선한다고 주장하기 위한 문서가 아니다.

목적은 반대다.

Local synthetic evidence suggests Mimesis downstream reinjection is useful only when the task is under-specified and the model would otherwise fill the vacuum with low-quality prior patterns.

짧게 쓰면:

downstream reinjection lift = task underdetermination x prior slop contamination

둘 중 하나가 없으면 현재 local evidence에서는 lift가 사라지거나 음수가 됐다.

Claim

Allowed public claim:

Digital Factory has a local synthetic downstream result log suggesting that
Mimesis reinjection helps in one narrow regime: an underdetermined task plus
slop-contaminated prior.

Determinate tasks, clean-prior tasks, and source-attribution-only reuse did not
show a general lift in this local packet.

Forbidden public claim:

Mimesis generally improves output.
Mimesis suppresses hallucination in general.
Mimesis has external validation.
Local synthetic scores prove customer value.
External source attribution proves downstream lift.
The result is statistically significant.
The result proves visual quality improvement, SEO lift, conversion lift, or production readiness.
The scorer measures full writing quality.

External Standards

These sources shape the reporting grammar. They do not validate Mimesis Engineering.

source	what it contributes
Model Cards for Model Reporting	Strong claims should expose intended use, factors, metrics, evaluation data, and limitations.
Datasheets for Datasets	Data and evidence packets should describe motivation, composition, collection, recommended use, and limits.
HELM	Evaluation should be multi-metric and scenario-specific rather than a single vague quality score.
NeurIPS Paper Checklist	Reports should state assumptions, scope, data, compute, limitations, and reproducibility conditions.
ACM Artifact Review and Badging	Artifact availability, artifact evaluation, reproduced results, and replicated results are different evidence levels.
NIST AI TEVV	Testing, evaluation, validation, and verification are lifecycle activities tied to context, metrics, and operating risk.
Pearl and Bareinboim on external validity and transportability	Transfer across contexts requires assumptions about source-target differences; it is not automatic.

Local Evidence Packet

Observed local files in the private/local Digital Factory/mimesis-plugin lane:

local file	role	public boundary
`evidence/downstream-reinjection/README.md`	Summary claim and boundary for downstream reinjection.	Local synthetic evidence only.
`evidence/downstream-reinjection/DOWNSTREAM-OUTPUT-RESULTS.md`	Result log with null, negative, and narrow positive regimes.	Not external validation, not user behavior, not production proof.
`evidence/downstream-reinjection/slop_score.py`	Deterministic proxy scorer for fabricated numbers, cliches, buzzwords, social proof, and emoji.	Measures one slop axis, not total quality.
`evidence/downstream-reinjection/error_score.py`	Deterministic proxy scorer for diagnostic facts and slop markers in error-message outputs.	Measures one error-message axis, not total usefulness.
`evidence/verification-relocation/README.md`	Method boundary saying validation does not transfer from source artifacts to new outputs.	Source attribution is not downstream lift.

Result Matrix

The local packet separates task underdetermination from prior slop contamination.

task regime	prior condition	observed local result	boundary
procedural bug	determinate task / clean target facts	NULL or negative. Later pooled scores: structure 4, naked 3, wrong-anchor 4.	Reinjecting structure did not help the procedural bug case.
error-message craft with facts	determinate task / clean target facts	NULL. Wrong-anchor was sometimes as good as or better than structure.	The scorer did not support a Mimesis lift here.
under-specified marketing copy	underdetermined task / slop-contaminated prior	LIFT. One recorded regime: structure 0.00, naked 0.83, wrong-anchor 4.33 on lower-is-better slop score.	This is the narrow positive regime.
determinate marketing with fact sheet	determinate task / slop-contaminated prior	NULL. The fact sheet collapsed naked slop toward 0.	Facts did more work than structure reinjection.
underdetermined error message with clean prior	underdetermined task / clean prior	NULL on the slop axis, often rejection or minimal generic behavior.	The scorer is blind to all useful refusal behavior.

The strongest local interpretation is:

Mimesis is most useful when the task is under-specified and the model would
otherwise fill the vacuum with low-quality prior patterns.

What Changed In The Claim

Before this packet, the tempting story was:

Better source artifacts produce better downstream outputs.

The local evidence does not support that general story.

The safer story is:

Better source artifacts can help define missing structure, but downstream lift
only appeared when the target task was underdetermined and the model prior was
slop-contaminated.

That is a smaller claim, but it is more useful. It tells us when Mimesis is a likely intervention and when it is just extra ceremony.

Validation

Fresh local command from the private/local workbench root:

python verify_workbench_surface.py

Observed result:

Digital Factory workbench surface checks passed.

Fresh local commands from the private/local mimesis-plugin lane:

python verify_evidence_references.py
python tools/validate_module.py --all

Observed result:

Mimesis evidence-reference checks passed.
14/14 valid

Current root npm boundary:

No root package.json is present, so npm test is not a current verification target.

Claim Boundary

What this artifact proves:

A local downstream reinjection evidence packet exists.
The packet records null, negative, and narrow positive regimes.
The strongest local law is conditional, not general.
The score scripts are inspectable local proxy scorers.
The public claim can now be narrower than “Mimesis improves output.”

What this artifact does not prove:

external validation,
independent replication,
customer outcome,
statistical significance,
production readiness,
legal clearance,
visual quality improvement,
hallucination suppression,
full writing quality,
SEO or conversion lift,
or that source attribution proves downstream lift.

Marketing Use

Safe sentence:

My local Mimesis evidence does not show general downstream lift. The useful signal is narrower: reinjection helped when the task was under-specified and the model would otherwise fill the gap with low-quality prior patterns.

Unsafe sentence:

Mimesis generally improves output.

Next Proof

The next stronger artifact should be a redacted before/after board with:

source artifact,
under-specified brief,
fact-sheet control,
wrong-anchor control,
target output,
deterministic scorer output,
human blind-read protocol if used,
failure notes,
claim boundary.

Until that exists, this remains local synthetic proxy evidence, not external validation.