feat: reader-lm + bge-m3 crates — HTML structure + multilingual embedding reader-lm (12 tests): Qwen2-1.5B architecture (RoPE + GQA 12:2 + SwiGLU) weights.rs: load bgz7 (26 MB), Q/K/V/gate_proj accessors inference.rs: Qwen2 forward pass scaffold (TODO: wire bgz-tensor) classifier.rs: HTML structure detection (Heading/Paragraph/Code/Table/etc) bgz7 indexed: /tmp/reader_lm_1_5b.bgz7 bge-m3 (6 tests): XLM-RoBERTa architecture (24 layers, 16 heads, 1024 hidden) weights.rs: load bgz7 (7.3 MB), embedding/attention accessors embed.rs: text→Base17, similarity, most_similar, batch_embed bgz7 indexed: /tmp/bge_m3_f16.bgz7 Both use bgz-tensor compiled attention (table lookup, not matmul). Both pinned to Rust 1.94. Both standalone (workspace exclude). Next: wire bgz-tensor AttentionSemiring for actual inference. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK - #75
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lance-graph-planner serve binary (--features serve): POST /v1/chat/completions → AutocompleteCache pipeline GET /v1/models → qwen35-opus46, qwen35-opus45, qwen35-9b GET /health → ok Tested with curl: multi-turn context accumulates, alignment grows, DK position tracks, phase transitions, OpenAI SDK compatible format. Cache returns MISS → LLM when no bgz7 palette loaded (correct). Next: wire bgz7 hydrate → palette → distance tables → cache hits. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
…on matrix Loads Qwen3.5-27B v2 (Opus 4.6) + base bgz7 shards into TripleModel: self_model: v2 weight fingerprints (what Opus thinks) user_model: base weight fingerprints (baseline) impact_model: diff between self and user (the Opus delta) Tested: 30 tensors, 30K rows, 1920 heads populated, Gestalt L1=12. Next: wire AriGraph + Thinking Stack for full cognitive pipeline. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
Hardware from ndarray (SIMD, I/O): Base17 (HeadPrint alias) — SIMD-optimized L1 distance read_bgz7_file() — canonical bgz7 parser (replaces 60-line manual parser) Thinking stays local in lance-graph (reasoning, not hardware): Truth (NarsTruth alias) — NARS f32 arithmetic SpoHead, NarsEngine, StyleVector — causal reasoning TripleModel, LaneEvaluator, CandidatePool — cognitive modeling ndarray = hardware acceleration. lance-graph = thinking. 156 planner tests passing. 33 cache tests passing. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
NarsEngine now has: NarsTables (128 KB L1-resident lookup, O(1) NARS revision) to_causal_edge() / from_causal_edge() (SpoHead ↔ CausalEdge64) forward_edge() (compose via CausalEdge64::forward) Architecture: ndarray: Base17, Palette, SpoDistanceMatrices (hardware, SIMD) causal-edge: CausalEdge64, NarsTables, forward/learn (protocol) planner: NarsEngine, StyleVector, TripleModel (thinking) AriGraph wiring blocked by circular dep (lance-graph → planner → lance-graph). Solution: extract AriGraph types to contract crate or separate serve binary. 156 planner tests passing. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
p64 is the convergence point where hardware (ndarray) meets thinking (lance-graph). convergence.rs: triplet_to_headprint() — SPO strings → Base17 (S-plane 0-5, P-plane 6-11, O-plane 12-16) headprint_to_spo() — Base17 → SpoHead with palette indices classify_relation() — relation text → predicate layer (CAUSES..BECOMES) triplets_to_palette_layers() → [[u64; 64]; 8] ready for Blumenstrauss::new() episodes_to_palette_layers() — episodic memory → palette Dependencies: p64 + p64-bridge + bgz17 + causal-edge + ndarray (all mandatory). Cold path: AriGraph TripletGraph → strings → DataFusion → Arrow Hot path: Triplets → Base17 → Palette → Blumenstrauss → O(1) 39 cache tests, 162 total planner tests. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
…enchmarks Full session record: weight diffs, paper synthesis, architecture decisions, benchmark results, next steps. Preserved for future sessions. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
Added: AutocompleteCache modules, p64 convergence, causal-edge protocol, 18 papers, benchmarks, dependency rules, AriGraph circular dep note. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
reader.rs: fetch URL → strip HTML → text_to_base17 (SPO-aware embedding) extractor.rs: verb-pattern triplet extraction + NARS revision refinement pipeline.rs: URL → triplets → palette layers → AutocompleteCache 12 tests passing. No lance-graph core dep (avoids protoc). No external API. Standalone in workspace exclude list. Next: index jinaai/reader-lm-1.5b via safetensors pipeline → bgz7 palette for local HTML→Markdown conversion without LLM API. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
…ding reader-lm (12 tests): Qwen2-1.5B architecture (RoPE + GQA 12:2 + SwiGLU) weights.rs: load bgz7 (26 MB), Q/K/V/gate_proj accessors inference.rs: Qwen2 forward pass scaffold (TODO: wire bgz-tensor) classifier.rs: HTML structure detection (Heading/Paragraph/Code/Table/etc) bgz7 indexed: /tmp/reader_lm_1_5b.bgz7 bge-m3 (6 tests): XLM-RoBERTa architecture (24 layers, 16 heads, 1024 hidden) weights.rs: load bgz7 (7.3 MB), embedding/attention accessors embed.rs: text→Base17, similarity, most_similar, batch_embed bgz7 indexed: /tmp/bge_m3_f16.bgz7 Both use bgz-tensor compiled attention (table lookup, not matmul). Both pinned to Rust 1.94. Both standalone (workspace exclude). Next: wire bgz-tensor AttentionSemiring for actual inference. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
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…y demoting one factory Codex + CodeRabbit P2 on lance-graph-java#75: a public record's canonical ctor and ofMatchBits(int) were still public bits-in paths, so demoting ofFacets alone did not fence L1. #75 now makes WideFieldMask a final class with a private ctor and both bit-level factories package-private; the ApiSurfaceTest pin is on the shape (no public ctor, not a record, every public factory zero-arg). This row records that. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016WkNBjHc2e3zuyz9i8qJEv
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