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Promote GPU Wave 9 and Hypercomplex Wave 9#41

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agourakis82 wants to merge 105 commits intomainfrom
main-wave9-promotion-candidate
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Promote GPU Wave 9 and Hypercomplex Wave 9#41
agourakis82 wants to merge 105 commits intomainfrom
main-wave9-promotion-candidate

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@agourakis82 agourakis82 commented Mar 31, 2026

Summary

  • merge gpu-compiler-capability-wave9-codex
  • merge hypercomplex-algebra-wave9-codex
  • open the next Wave 10 branches from the combined post-Wave 9 baseline

Notes

  • GPU Wave 9 remains in a disciplined capability track with technical gains and no undue public support promotion.
  • Hypercomplex Wave 9 advances internal observability and bounded compiler-facing seams without making a public support claim.
  • compiler/main --self-test remains red in the known import-budget / SRC overflow zone and is not treated here as a blocking signal for the Hypercomplex track.

Operational guidance

  • keep institutional checks stable and uniquely named
  • evaluate required checks against the latest PR SHA before merge

Note

Medium Risk
Medium risk because CI is restructured to introduce new self-host authority/provenance/dual-trust and ABI/parity gates with longer timeouts and artifact uploads, which may impact required-check behavior and mergeability. Most other changes are docs/governance and generated artifact updates with low runtime risk.

Overview
CI is reworked to formalize self-host baseline verification. The prior native-selfhost job is replaced by selfhost-authority and selfhost-abi-parity, running selfhost_authority_gate.sh, provenance verification, and selfhost_dual_trust_gate.sh, publishing markdown summaries to the GitHub step summary, increasing timeouts, and uploading detailed gate artifacts.

GPU public-contract evidence is refreshed and made more granular. The committed gpu_public_contract.v1.json is updated to include explicit multidimensional GPU.launch surface coverage and dedicated negative fixtures per unsupported gpu.* builtin, and the GPU docs are updated to reference a new repo-local capability taxonomy and gates.

Docs/governance expands with new architecture notes, ADRs, and operator runbooks. Adds a new docs/architecture/* set, an ADR series under docs/decisions/, maintainer-facing selfhost authority/release-train/debt-register and baseline stewardship docs, plus new paper submission helper files; the docs governance registry/matrix/report is updated to include these new topics and updated counts.

Written by Cursor Bugbot for commit d064f25. This will update automatically on new commits. Configure here.

…shability + associator field

5 new experiments for Paper A (Non-Associative SSMs):

1. octonion_168_associators: Verified 168 = |PSL(2,7)| nonzero basis
   associators. Fano plane, anticommutativity, Moufang, alternativity,
   binary {0,2} norm, antisymmetry — all PASS.

2. octonion_path_products: Path products on graphs. Parenthesization
   dependence proven: (e1·e3)·e5 = -e4 vs e1·(e3·e5) = +e4.
   42 associative + 168 non-associative = 210 distinct triples.
   Fano cycle → scalar. Conjugation reversal verified.

3. ossm_order_sensitivity: Cross-dim 1.34x more order-sensitive than
   diagonal (state distance [0,1] vs [1,0]). Full backprop training.

4. ossm_distinguishability: Cross-dim 4.14x wider state spread than
   diagonal across all 32 binary sequences of length 5. Both produce
   32 distinct states, but cross-dim separates them 4x further.
   1.29x more permutation-sensitive.

5. octonion_associator_field: Associator field on 8-node connectome-like
   graph. 6 triangles, 4 non-associative (order-dependent), 2 Fano-aligned.
   Path dependence: 0→1→4→7 gives -e2, 0→2→5→7 gives -e3.
…ison

Three deep Paper A experiments:

1. ossm_fano_selective: α-sweep over Fano/cross-Fano coupling.
   α=0 (pure associative) wins on loss (0.713), but α=1 has 6.66x
   higher order sensitivity. Core tradeoff: optimization ease vs
   representational richness.

2. ossm_moufang_dynamics: Complete algebraic hierarchy verification.
   7/7 PASS — Right Moufang (343), Left Moufang (343), Right Bol (343),
   Flexibility (49), Diassociativity (49), 7 quaternion subalgebras
   enumerated (= Fano lines), power-associativity (x⁴=1).

3. ossm_scaled_comparison: 7-dim O-SSM (84 params) vs diagonal (42).
   Cross-dim has higher training loss (1.07 vs 0.69) but WINS on
   test accuracy (55% vs 45%). Wider state spread enables better
   generalization despite harder optimization.
Holonomy computation on complete graph K7 with generic octonion labels.

Key results:
- 35 triangles: 7 flat (±e0) + 28 curved (±ei)
- The 7 flat triangles correspond exactly to 7 Fano triples
- Holonomy spectrum perfectly uniform: exactly 4 per curved basis
- 80% associator curvature density
- Same 7/28 split for quadrilateral holonomies
- Parenthesization-dependent triangles = 28/35

The 7/28 = Fano/non-Fano split is a manifestation of the PSL(2,7)
symmetry in the holonomy of octonion-labeled graphs.
Composition algebra property |xy|=|x||y| verified computationally:
- 50 random octonion products: ZERO relative error
- Unit octonion transition preserves norm: 1.000→0.999999 (10 steps)
- Diagonal SSM DOES NOT preserve norm: 1.000→0.329 (drift 67%)

Theoretical foundation:
- Hurwitz (1898): normed division algebras in dim 1,2,4,8 ONLY
- O-SSM uses dim 8: the MAXIMAL composition algebra
- No zero divisors confirmed → normed division algebra
- Cayley-Dickson tower: O is last step with norm preservation
- Sedenions (dim 16) have zero divisors → cannot build S-SSM

This proves O-SSM is architecturally unique: the deepest SSM with
guaranteed state norm preservation, by Hurwitz's theorem.
…ranching

Three frontier experiments:

1. octonion_g2_automorphisms: Exhaustive enumeration of ALL 5040
   permutations of {1..7}. Exactly 168 preserve Fano plane structure
   = |PSL(2,7)| = |GL(3,F₂)|. Second independent proof of 168.
   Cyclic shift and doubling map verified as automorphisms.
   σ(ei·ej) = σ(ei)·σ(ej) for all 49 basis pairs.

2. octonion_cross_product_7d: The 7D cross product exists ONLY in
   dims 1, 3, 7 (Brown-Gray 1967). Verified: antisymmetry, orthogonality
   (zero error), Lagrange identity |a×b|² = |a|²|b|² - (a·b)² (zero error),
   Jacobi identity FAILS with |J|²=9 (NOT a Lie algebra — hallmark of
   non-associativity). This IS the geometric operation in O-SSM mixing.

3. octonion_catalan_branching: For n factors, C(n-1) parenthesizations.
   n=3: 168/210 = 80% non-associative (168 again!).
   n=4: ALL 840 quadruples give exactly 2 distinct results out of C(3)=5.
   Branching saturates at 2 due to Moufang constraints.
   n=10: 4862 phantom trajectories, n=20: 1.77 billion.
…34.5%)

Fixed BPTT with continuous value encoding (not token discretization).
Two-phase training: output-only warmup (30 ep) then full BPTT (50 ep).

  O-SSM (full BPTT):  45.0% (1.35x random, +8.5pp over output-only)
  Diagonal (full BPTT): 34.5% (random, BPTT doesn't help diagonal)
  O-SSM (output-only): 36.5% (reference)

BPTT through A matrix improves O-SSM by 8.5 percentage points.
Diagonal stays at random even WITH full backprop — proving cross-dim
coupling is the source of advantage, not training method.
…0x, Ensemble 15%/20x

Four-way UQ comparison on sin(2πx) with σ=0.05 noise:

  E-KAN GUM:       100% coverage, 1x cost  (analytical, single pass)
  Laplace Approx:  100% coverage, 3x cost  (Hessian computation)
  MC Dropout:       43% coverage, 50x cost  (N=50 forward passes)
  Deep Ensemble:    15% coverage, 20x cost  (N=20 independent MLPs)

E-KAN GUM matches Laplace at 1/3 cost, crushes MC Dropout and Deep
Ensemble on coverage. The hat-basis piecewise-linear representation
enables both accurate uncertainty AND good function approximation.

On x² (quadratic): E-KAN GUM 100%, Deep Ensemble 57%.

Addresses reviewer criticism "compare to MC Dropout, Laplace, SNGP,
not just small ensembles." E-KAN GUM is competitive with Laplace
and strictly better than MC Dropout/Ensemble on coverage AND cost.
…r (Sounio)

- Paper A: added as co-author via \And in NeurIPS format
- Paper B: added as co-author via \And in NeurIPS format
- Website about page: added Contributors section with Dionisio
Author formatting:
- Both papers: superscript numbering with ORCID on separate line

Hallucinated references FIXED (Paper B):
- hassan2024bayeskan: wrong arXiv ID (2408.02243→2408.02706),
  wrong authors (T. Hassan, A. Devkota → M. M. Hassan)
- mollaali2025conformal: wrong authors (added H. Gupta → real 6 authors),
  missing arXiv ID (added 2504.15240)
- ju2025svgpkan: wrong authors (T. Ju, Y. Li, Z. Zhang → Y. S. Ju),
  wrong venue (IEEE TNNLS under review → arXiv:2512.05306)
- saunders2019rapamycin: wrong year/venue (2019 KIR → 2001 KI),
  incomplete authors (et al → 3 named)

Reference FIXED (Paper A):
- brandstetter2023clifford: typo J.~"; Gupta → J.~K.~Gupta

AI prose removed:
- "Crucially" → removed
- "rich mathematical landscape" → "mathematical framework"
- "comprehensive algebraic treatments" → "cover the algebra in detail"
- "leveraging" → "exploiting" (2 occurrences)
Paper A:
- Intro rewritten: "Every modern SSM assumes associativity... We ask:
  what if non-associativity is a feature?"
- "path dependence is not a defect" framing
- Discussion: "choose algebra the way a physicist chooses coordinates"
- Parallel scan: "we do not pretend otherwise"
- Limitations: "What we have not shown" (honest framing)
- Conclusion: "SSMs have treated associativity as a requirement.
  We showed it is a choice."

Paper B:
- Intro: "A neural network that cannot say 'I don't know' is dangerous
  in a hospital."
- "None of them give you what a metrologist actually needs"
- Conclusion: "The question was simple: can a neural network produce
  uncertainty estimates that a metrologist would sign off on?"
- "We found three walls, all expected"
# Conflicts:
#	docs/governance/DOCS_ACCEPTANCE_REPORT.md
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