Register reserve under agents hub and pin VAULT storage.

Classify freedom-intelligence for State Hub (agents domain), sync FI-WP-0001..0003 with hub IDs, enrich catalog entries with profile/SWOT, and pin the open-weight reserve to D:\vault\coulomb\freedom-intelligence\ with strategic S-tier policy.

inventory/catalog/BAAI__bge-m3__candidate.yaml

@@ -7,6 +7,7 @@ source:
   url: https://huggingface.co/BAAI/bge-m3
   revision: main
   model_card_url: https://huggingface.co/BAAI/bge-m3
+  project_url: https://github.com/FlagOpen/FlagEmbedding
 license:
   spdx: MIT
   url: https://huggingface.co/BAAI/bge-m3
@@ -14,6 +15,41 @@ license:
   allows_local_ops: true
   allows_fine_tune: true
   notes: ""
+profile:
+  summary: "Default multilingual dense embedding model for local RAG."
+  original_source: https://huggingface.co/BAAI/bge-m3
+  use_cases:
+    - "NetKingdom / Coulomb document and wiki retrieval"
+    - "Multilingual (DE/EN/…) semantic search"
+    - "Hybrid retrieval experiments (dense + multi-granularity features)"
+    - "Offline RAG in sandboxed agents"
+    - "Indexing ops runbooks and code comments"
+  sweet_spots:
+    - "Self-hosted production-quality multilingual embed"
+    - "MIT license; easy ops story"
+    - "Strong general retrieval without API spend"
+    - "Pairs cleanly with a small cross-encoder reranker"
+  not_ideal_for:
+    - "Generation / chat (not an LLM)"
+    - "Pure code retrieval if a code-specialized embed clearly wins A/B"
+    - "Ultra-tiny edge when EmbeddingGemma-class is enough"
+  capability_notes: >
+    BAAI BGE-M3 is a staple open embedder: multi-lingual, multi-granularity,
+    widely deployed for RAG. Primary R-tier retrieval backbone for the lab.
+swot:
+  strengths:
+    - "Proven multilingual retrieval quality for self-host"
+    - "MIT; small enough for CPU/GPU flexibility"
+    - "Mature FlagEmbedding ecosystem"
+  weaknesses:
+    - "Not optimized solely for code or for extreme long-context embed niches"
+    - "Newer embed lines (Qwen/Gemma/Nomic) may win specific A/B tests"
+  opportunities:
+    - "Domain-adapted embed fine-tune on Coulomb corpora later"
+    - "Companion bge-reranker for precision@k gains"
+  threats:
+    - "Leaderboard churn; risk of holding a stale 'default' without re-eval"
+    - "Pipeline lock-in if vector DBs assume fixed dimension without migration plan"
 size:
   total_bytes: 0
   total_human: "~2 GB"
@@ -31,12 +67,17 @@ collection:
   storage_path: ""
   brief_refs:
     - research/2026-07-24-baseline-field-survey.md
-reason: "P0 multilingual embedding staple for local RAG (docs, ops notes, DE/EN)."
-tags: [embedding, multilingual, rag]
-companions: []
+reason: "P0/R multilingual embedding staple for local RAG (docs, ops notes, DE/EN)."
+tags: [tier-r, embedding, multilingual, rag]
+companions:
+  - BAAI__bge-reranker-v2-m3__candidate
 notes: "Small download — within agent auto-collect band after license check once storage pinned."
 history:
   - at: "2026-07-24"
     event: nominated
     by: baseline-survey
     detail: "P0 recommendation from initial deep research."
+  - at: "2026-07-24"
+    event: profile_swot_added
+    by: grok
+    detail: "schema 0.2 profile + SWOT."