Validation and Claim Policy / 验证与声明规范

DoF does not equate code execution with research success. Every public claim must identify its evidence level, source artifact, and known boundary.

Evidence ladder

Level	Required evidence	Allowed claim	Disallowed inference
Import	Module imports and schemas load	Interface is syntactically available	Algorithm works
Smoke	Minimum command completes	Execution path is connected	Useful task performance
Deterministic test	Fixed assertion passes across supported environments	Contract or regression is verified	Generalization
Benchmark	Frozen protocol, artifact, seeds, and metrics are recorded	Result holds for that protocol	Transfer to another task or robot
Hardware	Robot, calibration, operator, safety gates, and incidents are recorded	Result holds for that physical setup	Certification or universal safety

Smoke qualifiers / Smoke 限定词

• synthetic-smoke means a fixed, generated fixture exercises the pipeline and its metrics. It does not add a new evidence level or imply performance on captured sensor data.
• synthetic-contact-dynamics is a synthetic smoke in which the simulator resolves hand-object contact. It can support claims about that fixture's contact, lift, and retention checks, but not policy quality, transfer to a production hand, or hardware performance.
• interface-tested means schemas, adapters, or planners connect without requiring production weights or hardware. It does not imply the underlying model is useful.
• Every smoke artifact must state both what it supports and what it cannot support; those boundaries travel with the metric JSON.

synthetic-smoke 表示固定的合成夹具能够执行管线与指标记录；它不是新的证据等级，也不能推出真实传感器数据上的性能。synthetic-contact-dynamics 表示仿真器确实求解了手—物体接触，但只能支持该夹具内的接触、抬升与保持检查，不能证明策略质量、生产级手型迁移或真机性能。所有 smoke 产物都必须同时记录“可支持的结论”和“不能支持的结论”。

Sources of truth

Claim family	Canonical source
Pipeline status, commands, artifacts, metrics	pipelines/manifest.json
Knowledge-node prerequisites, stages, documents, and learner evidence	knowledge/manifest.json
Research-route deliverables, metrics, and promotion gates	learning_paths/manifest.json
PushCube numerical results	results/benchmarks/benchmark_v2.json
Dependency compatibility	Lock files and successful CI jobs
Development-host teaching defaults	tools/robotdev/stack_matrix.json plus its dated upstream links
Foundation concepts	Primary-source registry
Scientific wording and evidence labels	Claim review and accuracy gate
Third-party terms	Bundled upstream license files and third-party notices
Hardware readiness	A hardware-specific report; none is currently claimed locally

Narrative documents summarize these sources and must not override them.

Accuracy audit

Run before every release or major documentation change:

python scripts/check_markdown_links.py
python scripts/check_markdown_format.py
python scripts/check_claims.py
python scripts/run_knowledge_map.py --validate
python scripts/run_pipeline.py --validate
python scripts/run_learning_path.py --validate
python scripts/audit_repository.py
python -m pytest tests/ -q

The automated audit verifies repository-local links, required governance files, knowledge-graph structure, Pipeline and research-route contracts, benchmark consistency, bilingual entry points, source pointers, and declared artifacts. Graph validation proves that identifiers, prerequisites, stages, documents, and coverage are internally consistent; it does not prove learner mastery. The audit also cannot prove the truth of an unexecuted physical experiment or every semantic interpretation of an external source.

Benchmark rules

• Never replace null or “not evaluated” with zero.
• Keep training data, model scale, compute, seeds, evaluation episodes, and horizon visible.
• Use closed-loop task performance as the primary policy metric; offline loss is supporting evidence.
• Report failed and negative results using the same protocol as successful results.
• Regenerate or manually update narrative tables only after changing the machine-readable source.

Hardware rules

• Local simulation does not authorize robot motion.
• A real deployment requires calibration, limit checks, watchdog, emergency stop, low-risk commissioning, rollback, and an identified operator.
• “Available model” means an asset exists; “adapter” means interface code exists; neither implies real-robot validation.

Correction process

When a conflict is found, preserve the raw artifact, identify the incorrect derived statement, fix the narrative or generator, add a regression test, and record the correction in CHANGELOG.md.