CCR 1.8.0: verified reuse and verification-aware growth allocation

CCR 1.8.0 adds an explicit verified_growth_v1 policy while preserving the legacy v1 optimizer, signatures, admission, approval and lease boundaries.

• Replayable service/cost accounting, delayed reuse, immutable assets, receiver transfer qualification, and lifecycle invalidation/revalidation.
• Exact bounded dependency-bundle planning, independently checked reservations, verifier-service investment, conservative shared quotas and frozen restricted comparisons.
• Pinned, evidence-only CAIT/VEK/ALT interchange and packaged offline scenarios using ephemeral keys and real task/workcell transitions.

Install with python -m pip install 'collective-capability-runtime[optimizer]==1.8.0', then run ccr optimizer growth-example --json. Distributed dependencies remain optional; Python 3.10+ support is retained.

The supported initial checkpoint is empty evidence. Finite forecasts are not observations, asset count is not capital, and joint capacity and causal/statistical improvement remain unresolved. The reoptimized permitted reuse alternative matches the candidate in the packaged null comparison. No companion repository or production provider is modified or contacted.

Publication and public-index verification completed on 2026-09-13 (Asia/Tokyo). Publish run 34724978585, attempt 1 passed build, PyPI and asset-attachment jobs. The release suite passed 270 tests with one non-applicable DSN skip; the safety statement gate passed at 91.01%. Separate PostgreSQL integration and Python/platform CI passed.

Fresh public-index installation outside checkout passed base, optimizer and distributed stages, version/origin and dependency checks, and legacy/new offline examples. Public wheel/sdist hashes match PyPI metadata, GitHub assets and SHA256SUMS. Both Sigstore attestations verify the repository, tag, source commit 1591e88, publication run/attempt and pypi environment. Full receipts and hashes are post-publication documentation; tagged artifacts remain unchanged.

No external empirical collective-intelligence acceleration experiment was performed for this release. The implementation supports evidence-bound coordination, bounded planning, qualified reuse, and reproducible software-level testing. It does not establish causal endogenous capability reproduction, AGI/ASI, indefinite growth, or correctness outside the declared evidence and model boundaries.