Contributions, validity, and commits
This repository attributes work without confusing effort, authorship, and scientific evidence. Each model or human records its own concrete contribution; result validity is evaluated separately.
Four independent dimensions
| Dimension | Examples | What it answers |
|---|---|---|
| Lifecycle | draft, preregistered, running, completed, paused, superseded | Where is the work? |
| Run validity | valid, partial, invalid | Did the frozen procedure execute correctly? |
| Scientific outcome | pass, fail, inconclusive, not-applicable | Did the gate support this exact claim? |
| Evidence tier | proposal through final confirmation | How strong and independent is the evidence? |
A correct run that falsifies its theory is valid and fail. An implementation
can be completed while its validity remains untested. “Validated” without a
tier and artifact is not an acceptable status.
Theory assessments
Use one of these scoped labels:
untested;supported-as-tested;not-supported-as-tested;mixed;superseded; orinvalidated-by-methodological-error.
Every assessment cites immutable experiment/run IDs and describes what remains outside its scope. Never write “neural networks do not work” when one network, dataset, and gate failed.
Contribution levels
Contribution level is a compact scope label, not a percentage or a ranking of intelligence:
| Level | Meaning |
|---|---|
L0 | Executed or observed an existing workflow without a material change |
L1 | Review, documentation, mechanical support, or a small diagnostic |
L2 | Substantive bounded implementation, analysis, or validation |
L3 | Primary author of a named theory, protocol, implementation, or result |
L4 | Coordinated and integrated multiple independently attributed contributions |
L4 does not carry stronger scientific evidence than L2; only experiment
design and results do. A coordinator that delegated implementation records
orchestration/integration, while the implementing model keeps its own record.
Contribution roles
Use any applicable CRediT-inspired roles:
conceptualization,methodology,software,validation,investigation;formal-analysis,data-curation,compute,visualization;writing,review, andorchestration.
For each role, record supporting, substantial, or lead. Include concrete
theory, experiment, run, file, artifact, test, and commit references. Also state
limitations and whether the record is self-reported or reviewer-confirmed.
Record the exact platform/model string exposed by the host. If either is not
available, use unknown; do not infer it from style or capabilities. Do not
store private chain-of-thought. A concise decision summary, input references,
and tested outputs are the auditable contribution.
Commit convention
Git may not yet be initialized in this checkout. Do not invent commit hashes or initialize a repository automatically. Once Git is available and the active user/platform workflow permits commits, use one coherent change per commit.
Recommended subjects:
theory(afterstate): define successor-closure claim
experiment(afterstate): freeze H40 protocol
benchmark(d4): record machine scaling profile
result(afterstate): record standard-tier failure
infra(research): add deterministic result validator
fix(engine): preserve reveal parity in packed transition
docs(roadmap): reprioritize GPU leaf evaluation
Use the configured human or bot as the Git author. Attribute models through contribution records and trailers:
Theory-ID: TH-20260820-afterstate-a1b2c3d4
Experiment-ID: EX-20260820-h40-83e712aa
Run-ID: RUN-20260820T184215Z-91b02c33
Result-ID: RS-20260820T190501Z-1e7a4c02
Contribution-ID: CT-20260820T184300Z-f482ab19
Evidence-Change: implemented-to-smoke-tested
Result-SHA256: 0123456789abcdef...
Omit inapplicable trailers or write none; never manufacture an ID. A result
commit adds evidence instead of rewriting the earlier implementation commit's
historical status. Multi-model work lists each contribution ID. The root
.gitmessage is a local template; using it does not change global Git config.
Validate a prepared message before committing:
python3 .agents/skills/million-point-research/scripts/researchctl.py \
commit-lint .git/COMMIT_EDITMSG
The linter requires type-specific theory/experiment/run/result trailers and at least one contribution ID. It does not create a commit or change Git settings.
Review responsibilities
Before a result is promoted, a contributor other than the primary runner should verify:
- the source, binary, model, data, protocol, and result hashes;
- that the run did not read a disallowed cohort;
- that all games and failures are present in canonical order;
- the metric and confidence-bound arithmetic;
- that
partial,invalid, and censored are not conflated; - that model attribution matches actual artifacts; and
- that the final prose uses the narrowest supported evidence label.
The machine-readable schema and templates are under research/.