Hillock

type-constrained schema validation, running offline without cloud calls. Refusal is Control Flow (Not Prompting) Instead of prompting an LLM to "only answer if you know", Hillock's gate is an actual programmatic check. If candidate facts fail our similarity threshold with positive predicate intent, it returns a hardcoded refusal immediately. The local LLM (Ollama) is never called with un-evidenced context, saving 100% of GPU compute on unanswerable queries. What is New in v0.6 (HYDRA & HyperGraph) HYDRA (Bipolar MaxSim): Adapted ColBERT-style token-level MaxSim to discrete bipolar vectors. A 2,000-D Sub-Dimensional Projection Cascade early-rejects ~95% of candidates on CPU in ~0.5ms. HYPERGRAPH-HDC: Uses positional cyclic shifts to break binding commutativity, encoding 2-hop and 3-hop relational paths without combinatorial RAM explosion. Benchmarks (Unseeded 32-query run on a laptop CPU) Answerable Retrieval Accuracy: 54.5% (Fast-eval in 1.16s on CPU) Hard-Negative Block Rate: 60.0% (stops trick queries cold) Extraction Recall: 59.1% | Pooled Gate Accuracy: 56.2% Known Limitations Retrieval quality is strictly bounded by extraction recall: if the extractor misses a fact during ingestion, the gate honestly refuses (producing a false block). The project is AGPL-3.0 licensed with 1-click quickstart launchers (run.bat / run.sh) and a standalone 21-point CPU verification suite (verify_hillock.py). GitHub: https://github.com/roandejager/Hillock I would love to hear your thoughts, feedback, and critiques!

ebbian Plasticity Engine (plasticity.py): Gradient-free co-activation associative learning that updates synaptic weights between entity nodes across chat turns. 3. 10,000-D VSA/HDC Reservoir (reservoir.py): A Vector Symbolic Architecture hypervector space using Subword N-Grams (3,4,5-grams) and Locality-Sensitive Sign Random Projections (SimHash) over a static 10MB GloVe dictionary. It handles pronoun resolution and context similarity gating on CPU in under 1 millisecond. ### Document Ingestion (TALON Engine) Document parsing uses TALON—a 3-stage CUDA pipeline: - Stage 1: Document coreference preprocessing (Fastcoref) - Stage 2: Bi-Encoder candidate predicate routing (all-MiniLM-L6-v2 in <2ms) - Stage 3: Zero-shot span relation extraction (GLiREL Large) Ingestion completes in ~3.9–5.0 seconds for a 32-sentence academic document (6.3–8.0 sentences/sec pure GPU rate) using <1.2 GB VRAM on a GTX 1070. Generative LLMs (via local Ollama) are invoked ONLY for final response rendering once a query passes the HDC similarity gate. ### What's New in v0.4 In the v0.4 release series, we tackled zero-shot extraction noise and inverted facts: - O(1) Type-Constrained Schema Validation: Set-based domain/codomain filtering over GLiREL outputs. - Automatic Direction Correction: Detects and swaps inverted extractions (e.g., turning [Budapest born_in John von Neumann] into [John von Neumann born_in Budapest]). - Precompiled Entity Sanitization: Strips possessive artifacts ('s), trailing action verbs, and prepositional tails. - Raw extraction precision improved from 11.5% to 15.5% (+35% cleaner graph). ### Current Benchmark Status (Honest Baseline) On a fixed 32-sentence benchmark (20 answerable queries, 10 hard-negative trick queries): - Retrieval Accuracy: 55.0% - Extraction Recall: 50.0% - Extraction Precision: 15.5% - Fast-Eval Retrieval Duration: ~1.27s for 30 queries (~0.04s per query) - Unanswerable Queries: 0 GPU tokens wasted (CPU similarity gate shuts down LLM calls instantly). Repo: https://github.com/roandejager/Hillock (AGPL-3.0) I'd love to hear feedback, criticism, or ideas from the community on hypervector binding, schema constraints, or low-power edge memory architectures!