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 | **is_novel_claim** | Boolean flag: true = original work, no prior paper covers exactly this. Requires novelty_statement. | neon-64gb `arxiv` DB |
 | **relation_type** | Citation edge: `grounds` (foundational), `extends` (builds on), `applies` (application), `cites` (general), `hybrid` (RRF-trigram+vector). | neon-64gb `arxiv` DB |
 | **Jaccard Matcher** | Deprecated script finding arxiv papers via keyword overlap. Replaced by hybrid_search. | `python/hachimoji_citation.py` (historical) |
-| **Hybrid Matcher** | Equation-to-citation pipeline using pgvector HNSW + pg_trgm RRF (22ms query, 397x faster than transformer models). | `python/hachimoji_citation.py` |
+| **Hybrid Matcher** | Equation-to-citation pipeline using pgvector HNSW + pg_trgm RRF (22ms query, 397x faster than transformer models). Mirrors `HachimojiCodec.lean` classifyEquation. | `python/hachimoji_citation.py` |
+| **hachimoji_citation** | Equation → Hachimoji state → arXiv paper bridge. Computes structural features, classifies to one of 8 states, builds semantic query, retrieves top-k papers. Mirrors `formal/CoreFormalism/HachimojiCodec.lean`. | `python/hachimoji_citation.py` |
 | **static-retrieval-mrl-en-v1** | Sentence-transformers embedding model (1024-dim), 397x faster than transformer models for arXiv abstract embedding. | arXiv embedding pipeline |
 | **hnsw_index** | pgvector HNSW index (m=8, ef=100) enabling 22ms vector similarity search over 700k arxiv papers. | `idx_arxiv_embedding_hnsw` in arxiv-pg |
 | **RRF** | Reciprocal Rank Fusion: merges trigram and vector search ranks without score normalization. Formula: `score = 1/(k+r1) + 1/(k+r2)` | `hybrid_search.sql` |