docs(roadmap): mark v0.10/v0.11 done; insert v0.12 neural embedding; update detection stats#62
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docs(roadmap): mark v0.10/v0.11 done; insert v0.12 neural embedding; update detection stats#62ksek87 wants to merge 2 commits into
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…al embeddings) v0.10 is now TF-IDF cosine similarity against abstract attack archetypes — always-on, no model download, zero new dependencies. v0.11 is a separate milestone for the neural embedding upgrade with five sub-tickets (#52–#56) covering research, archetype embedding, model management, integration, and calibration. All downstream milestones renumbered accordingly. Closes no issues — roadmap structural update only. https://claude.ai/code/session_01G4f8mN9SeSHSGY1dWfFzih
…update detection stats - Roadmap table: v0.10 and v0.11 marked ✅ Done; v0.11a split out; v0.12 = neural embedding semantic layer; package-level scanning shifts to v0.13; Python SDK to v0.14 - Milestone detail: replace outdated TF-IDF/neural-embedding forward-looking text with accurate done/planned descriptions including v0.12 neural embedding blurb - Lede detection rate: 84.7% → 90.7%; four passes (not three); 161 needles (not 155) - Benchmark table: Template-1/2/3 → descriptive paradigm names; full risk-category breakdown added (10 categories, v0.11 numbers) - Signal table: Template-N references replaced with paradigm names https://claude.ai/code/session_01G4f8mN9SeSHSGY1dWfFzih
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Summary
Also updates issue #26 (main roadmap tracking issue) with completed phase checkboxes for v0.1–v0.11 and restructured phase sections reflecting current state.
Test plan
https://claude.ai/code/session_01G4f8mN9SeSHSGY1dWfFzih
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