From 699722f737d8fce85c99d3ce1fdd25b06ba081a4 Mon Sep 17 00:00:00 2001 From: AdaWorldAPI Date: Sat, 31 Oct 2026 14:57:23 +0100 Subject: [PATCH 1/2] docs(research): seed D-STAUNEN-3 scope and study skeleton --- .../learning/d-staunen-3-xlog-nsl-hydration.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 docs/research/learning/d-staunen-3-xlog-nsl-hydration.md diff --git a/docs/research/learning/d-staunen-3-xlog-nsl-hydration.md b/docs/research/learning/d-staunen-3-xlog-nsl-hydration.md new file mode 100644 index 000000000..194f44382 --- /dev/null +++ b/docs/research/learning/d-staunen-3-xlog-nsl-hydration.md @@ -0,0 +1,12 @@ +# D-STAUNEN-3 | Symbolic structure and neural learning | draft +**Docs only; no second Datalog, provenance store, optimizer, palette registry.** +## First pass +- Reuse #1884 `xlog-relcomp-probe` and #1869 DT bind catalogue; keep XLOG RIR/PIR/EIR/NN behavior distinct. Join support/multiplicity is not confidence or causality; Epi5 `IndirectKnown×Open` stays open. +- Reuse #1846 `lance-graph-nsl` frozen Google NSL packing/loss oracles and adjacency. Stage signed/order-dependent graph losses. Do not substitute HDC Hamming distance for NSL L2/KL/JS gradients. +- U9 policy: DR512 signed epistemic code + never-touched != TurboVec9 512 Lloyd-Max centroids; #1839 class codec contract; #1847 residuals. Quantized neighbors for candidate discovery, exact/declared tolerance for ranking, loss, gradients. +- Structural reinforcement hydration: incoming neural traces + HDC novelty -> evidence-source guarded candidate relations in THOUGHT, not WORLD; XLOG generates/verifies bounded inferred candidates; NSL computes local graph regularization; independent observation permits promotion. +- Preserve bags and overflow refusal on counts; K3 unknown, signed zero, NaN, evidence duplicates, unique roots, slot-version. + +## Research gates +X0 owner/capability inventory; X1 native signature and source plan binding; X2 bag/overflow golden; X3 NSL neighborhood oracle; X4 loss/gradient parity; X5 9-bit/residual ablations; X6 ontology/hydration provenance; X7 no self-certifying joins; X8 benchmark with no learning/model comparison. +**Prereq:** #1846, #1847, #1884; cross-reference D-STAUNEN-1. From 0454714d4435fd46d0f76155f0e1aa905953fbb8 Mon Sep 17 00:00:00 2001 From: AdaWorldAPI Date: Sat, 31 Oct 2026 15:01:18 +0100 Subject: [PATCH 2/2] docs(research): deepen D-STAUNEN-3 CC gates, falsifiers and sources --- .../d-staunen-3-xlog-nsl-hydration.md | 57 +++++++++++++++++++ 1 file changed, 57 insertions(+) diff --git a/docs/research/learning/d-staunen-3-xlog-nsl-hydration.md b/docs/research/learning/d-staunen-3-xlog-nsl-hydration.md index 194f44382..406e75263 100644 --- a/docs/research/learning/d-staunen-3-xlog-nsl-hydration.md +++ b/docs/research/learning/d-staunen-3-xlog-nsl-hydration.md @@ -10,3 +10,60 @@ ## Research gates X0 owner/capability inventory; X1 native signature and source plan binding; X2 bag/overflow golden; X3 NSL neighborhood oracle; X4 loss/gradient parity; X5 9-bit/residual ablations; X6 ontology/hydration provenance; X7 no self-certifying joins; X8 benchmark with no learning/model comparison. **Prereq:** #1846, #1847, #1884; cross-reference D-STAUNEN-1. + +## Round 2 | CC probe matrix, typed adaptation, reference constraints + +### Do not collapse distinct logic and learning paths +XLOG upstream's typed deterministic **RIR**, probabilistic **PIR**, epistemic **EIR**, and neural-symbolic training surfaces have different query meanings; there is no universal RIR lowering for every program. Our current XLOG lab #1884 only admits the bounded compiled count rule and masked relational composition on `ndarray::simd`. It is **not** admission for XLOG recursion, probabilistic exact inference, differentiable ILP, general aggregates or whole-language portability. +- XLOG https://github.com/BrainyBlaze/xlog +- Native probe https://github.com/AdaWorldAPI/lance-graph/pull/1884 +- DT64 bind catalogue https://github.com/AdaWorldAPI/lance-graph/pull/1869 +- NSL parity and exact neighbor semantics https://github.com/AdaWorldAPI/lance-graph/pull/1846 +- Codec and cross-thought candidate research https://github.com/AdaWorldAPI/lance-graph/pull/1847 + +### Hydration action, as existing algebra (not another store) +```text +observed_neural_event + HDC novelty + sealed source identity + -> DR bound local focus + -> DT64 admitted method (source lens, K3 guards, declared effects) + -> XLOG compiled rule body / NSL graph loss under exact owned source + -> candidate neural correlation / relational inference in THOUGHT + -> independent evidence + causal ablation -> promotion candidate +``` +- `XLOG->CE64` projection must preserve `Direct×Open` base facts, `IndirectKnown×Open` composed derivations; neither is `Causes`, and no `CausalCandidate` without its certifying test. +- Bag multiplication `count_R(y)*count_S(y)` uses checked i64. Negative, zero, NaN and `-0.0` semantics must remain source-faithful; don't assign counts to F/C without calibrated legal conversion. +- Use immutable borrowed source + versioned `FocusLease`/frame. Duplicate relation keys preserve NSL's documented stable tie, first equal-weight bits and f64 ordering at freeze; signature/state lives in DT64; no duplicate adjacency tables. +- NSL loss is defined on neighbor **logits** / features and weights; contrastive local HDC Hamming distance is *not* a drop-in for the NSL TensorFlow graph loss. +- An HDC/VSA candidate can rank what evidence to ask for, but its similarity is not proof, causality, calibrated Bayesian uncertainty or neural physiology. + +### Codec/admissibility matrix +| Task | Source/format | Gate | +|---|---|---| +| Node identity | exact rank/codebook once | #1846 label oracle, reused | +| Sorted selected neighbors | f64 order in compat path | no tied-quantization top-k reversal | +| Exploratory edge weight encoding | Fisher-z i8 / z-linear U9 | not production without fixed tolerance and real-data gate | +| Graph-loss numerics | NSL f32, L2/L1/cosine/KL/JS declared | TensorFlow frozen oracle and gradient checks | +| Very close neighbor features | source-code + scale-separated residual | compare with non-quantized convergence; no false zero | +| Epistemic U9 `EpiLane9` | 511 numeric + never touched | class declaration and signed-zero / absent rules | +| TurboVec9 | 512 numeric centroids | dedicated retrieval model, not epistemic U9 | +| Pair LUT | prepared 256² or candidate 512² | check numerical cost, aliasing and native parity | + +### Execute or refuse gates +X0 independent source inventory: current `main` #1884 tests, XLOG source version, NSL TensorFlow oracle, U9 codec and exact semantics. +X1 same typed compiled RIR operation -> same `ndarray::simd` entry for ONNX/XLOG when signature matches; wrong signatures stop. +X2 join/aggregate has correct multiplicity, overflow refusal and no materialized owned result rows; null/unknown guard refuses. +X3 use NSL G01–G12 plus supplements and source-sealed graph neighbors; disable stable tie or source seal -> red. +X4 graph-loss vs TensorFlow and finite differences or analytical gradient reference; include cancellation/softmax corners. Tolerance source-specific, never assert exact gradients from an exact residual address. +X5 compare exact f64 top-k, Fisher-z i8, z-linear U9, TurboVec9 and residual-coded arms on held-out graph sets; report precision, memory and mistakes *before* speed. +X6 source-rooted inference: every candidate carries original observed event root, XLOG program hash and CE64 rail. If two thoughts read one root, evidence count remains one. +X7 source-off/novelty-shuffle/quantized-nearest-collision negatives. Require generalization to unseen structure. +X8 resource gate: native resident fold ns/batch µs per `CLAUDE.md`. Do not conflate cold parse/plan with hot execution. + +### Scientific context +- Bui et al. Neural Graph Machines, 2017: https://arxiv.org/abs/1703.04818 +- SpikeHD SNN/HDC, 2022: https://doi.org/10.1038/s41598-022-11073-3 +- SynapseHD joint SNN/HDC, 2026: https://doi.org/10.1016/j.neucom.2025.131757 +- Kinavuidi et al. continual spike-time HDC decoding, 2026: https://doi.org/10.1088/2634-4386/ae4f1e +- Denève, Bayesian Spiking Neurons I/II, 2008: https://doi.org/10.1162/neco.2008.20.1.91 and https://doi.org/10.1162/neco.2008.20.1.118 + +**CC handoff:** initial implementation belongs in existing `xlog-relcomp-probe` or `lance-graph-nsl` after owners sync; no new `lance-graph-xlog` crate and no new renderer, classifier or external graph engine until a measured source gap exists.