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Milestones

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  • Advance Anchor Runtime toward deterministic runtime governance through behavioral enforcement, runtime policy validation, replayability, and execution-time governance primitives. This milestone represents the research roadmap beyond the current static governance engine. Future Issues AnchorJIT Runtime policy compiler Declarative approval workflows Runtime replay Diamond Cage evaluation Behavioral benchmarks Runtime governance protocol

    No due date
  • Develop a rigorous evaluation methodology for Anchor, including precision/recall measurements, benchmark datasets, false-positive analysis, reproducibility, and comparative evaluation against existing static analysis tools. This milestone supports future academic publications and empirical validation. Future Issues Precision / Recall benchmark Ground-truth dataset False-positive analysis False-negative analysis Cross-tool comparison Reproducibility suite Benchmark automation

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  • Strengthen the governance engine by introducing evidence-based evaluation, mitigation-aware reasoning, provenance-aware severity assessment, and typed parsing. This milestone establishes the next generation of deterministic governance evaluation. Issues ✅ Separate Manifest Validation from Governance Rule Evaluation ✅ Implement Evidence-Based Evaluation for ALN-001 ✅ Implement Mitigation-Aware Rule Evaluation ✅ Require AI Taint Evidence Before Escalating Process Execution Findings

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  • Improve the developer onboarding experience by making initialization transparent, predictable, and aligned with established Python development workflows. This milestone focuses on developer trust, explicit consent, and ecosystem integration. Issues ✅ Replace hardcoded fallback signing key ✅ Migrate Git integration to the standard pre-commit framework ✅ Require explicit user confirmation before modifying Git state ✅ Require explicit consent before downloading WASM runtime ✅ Require explicit runtime activation instead of import-time monkeypatching

    No due date