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Chess knight per-eigenspace irrep multiplicities under D₄ / B₄ — findings (2026-05-11)

Spike: For the knight move-graph adjacency matrix in 2D (8×8 = 64 cells) and 4D (8⁴ = 4096 cells), is the per-eigenspace decomposition under D₄ (2D) and B₄ (4D) integer-exact at machine precision? Even though knight has no closed-form eigenvalues, the per-eigenspace irrep structure was queued as candidate "structural prediction without closed-form eigenvalues" — a stronger §3.5.3(C) sub-instance for the chess case.

Method: Concertmaster role, MPM-discipline. 2D: D₄ projector method (P_ρ = (d_ρ/|G|) Σ_g χ_ρ(g) U_g; rank(P_ρ V_λ)/d_ρ = multiplicity). 4D: character orthogonality via class-trace inner products (mult = (1/|G|) Σ_C |C| · χ_ρ(C) · tr(U_{g_C}|V_λ)) — avoids materializing 384 × 4096×4096 dense projectors. B₄ character table computed from Specht/Frobenius formula via Murnaghan-Nakayama for S_n characters and the power-sum-Schur bridge for B_n. Cited rules SSOT: Rinaldi-Unciuleanu & Chiru 2026 (docs/srmech/hoodoos/rinaldi-unciuleanu-chiru-2026.xml, §3.6 names knight as the non-product case).

Dispatch: Conductor → concertmaster, 2026-05-11. Follow-up to today's chess D₄/B₄ rep-theory spike (Fermata 2: queue per-eigenspace knight irrep decomposition).

Bottom line: Integer-exact at machine precision in both 2D and 4D. 2D: 45 distinct eigenvalues × 5 D₄ irreps = 225 (irrep × eigenspace) combos; zero non-integer. 4D: 825 distinct eigenvalues × 20 B₄ irreps = 16,500 combos; zero non-integer (max deviation 2.98 × 10⁻¹⁴ — essentially machine precision). Sum-rules pass for every irrep in both dimensions. Knight gains a §3.5.3(C) sub-instance: even without closed-form eigenvalues, the per-eigenspace irrep structure IS closed-form-predictable from rep theory at machine precision — the eigenspace dimensions are exactly the B₄ irrep dimensions {1, 2, 3, 4, 6, 8}.

Reproducible: python docs/srmech/notes/chess-knight-irrep-multiplicities-script.py. Runtime ~44 s on commodity workstation. Deterministic seed 20260511. Memory peak ~265 MB (the 4096-eigenvector matrix held briefly).


1 — 2D knight × D₄ — per-eigenspace decomposition

45 distinct eigenvalues in the spectrum of the 64×64 knight adjacency. Every eigenspace is pure (single non-zero D₄ irrep). Eigenspace dimension histogram:

Eigenspace dim Count Irrep type
1 32 A₁, A₂, B₁, B₂ (1-dim irreps; 8 each)
2 12 E (only 2-dim irrep)
8 1 E with mult 4 (λ=0 kernel)

Sum-rule (per-irrep total across all eigenspaces, expected = natural-rep multiplicity on R⁶⁴):

Irrep dim Per-eigenspace total Expected Match
A₁ 1 10.000 10
A₂ 1 6.000 6
B₁ 1 6.000 6
B₂ 1 10.000 10
E 2 16.000 16

Total dim = 10 + 6 + 6 + 10 + 2·16 = 64 ✓. All multiplicities integer-exact to within 1 × 10⁻¹⁴.

Notable eigenvalues: - λ = +6.0107 (Perron / max): A₁, dim 1 - λ = −6.0107 (anti-Perron / min, bipartite partner): B₂, dim 1 - λ = 0.0 (kernel): 4 copies of E, dim 8 - λ = ±2.0 exactly: B₂ / A₁ respectively, dim 1 each — rational eigenvalues in knight 2D's otherwise-irrational spectrum

Cross-method check. The 2D result was verified by TWO independent methods (D₄ projectors with rank-via-SVD AND D₄ character orthogonality with class traces); both give identical integer multiplicities.


2 — 4D knight × B₄ — per-eigenspace decomposition

825 distinct eigenvalues in the spectrum of the 4096×4096 knight adjacency. 824 of 825 eigenspaces are pure (single non-zero B₄ irrep); only the λ=0 kernel hosts multiple irreps.

Eigenspace dimension histogram (matches B₄ irrep dimensions exactly):

Eigenspace dim Count B₄ irreps with this dim
1 72 (4)|(), ()|(4), (1,1,1,1)|(), ()|(1,1,1,1) [4 irreps]
2 40 (2,2)|(), ()|(2,2) [2 irreps]
3 120 (3,1)|(), ()|(3,1), (2,1,1)|(), ()|(2,1,1) [4 irreps]
4 192 (3)|(1), (1)|(3), (1,1,1)|(1), (1)|(1,1,1) [4 irreps]
6 240 (2)|(2), (1,1)|(1,1), (2)|(1,1), (1,1)|(2) [4 irreps]
8 160 (2,1)|(1), (1)|(2,1) [2 irreps]
96 1 λ=0 kernel: 10·(2)|(2) + 6·(1,1)|(1,1) — mixed

The eigenspace dimensions are precisely the 6 distinct B₄ irrep dimensions {1, 2, 3, 4, 6, 8}. This is itself a structural prediction: rep theory alone tells you the menu of possible eigenspace dimensions for any B₄-equivariant operator on R⁴⁰⁹⁶.

Knight 4D is bipartite (move changes parity of x+y+z+w; verified empirically — the 2048×2048 same-parity block of the adjacency is exactly zero). Bipartiteness implies: - Spectrum is symmetric: λ in spectrum ↔ −λ in spectrum. - Rank deficiency: independent rank check confirms rank(A) = 4000, nullity = 96 — the 96-dim kernel matches the bipartite-graph rank formula. - The 96-dim kernel splits as 10·(2)|(2) + 6·(1,1)|(1,1) (both are 6-dim B₄ irreps). - "Even-half" irreps (α ↔ β swap pairs) appear at eigenvalues and −λ with mirror multiplicities.

Sum-rule (per-B₄-irrep total across all 4D eigenspaces, expected = natural-rep multiplicity on R⁴⁰⁹⁶):

Irrep dim Per-eigenspace total Expected Match
(2)|(2) 6 100.0 100
(1)|(3), (3)|(1) 4 80.0 each 80 each
(1)|(2,1), (2,1)|(1) 8 80.0 each 80 each
(2)|(1,1), (1,1)|(2) 6 60.0 each 60 each
(3,1)|(), ()|(3,1) 3 45.0 each 45 each
(1,1)|(1,1) 6 36.0 36
(4)|(), ()|(4) 1 35.0 each 35 each
(2,2)|(), ()|(2,2) 2 20.0 each 20 each
(1,1,1)|(1), (1)|(1,1,1) 4 16.0 each 16 each
(2,1,1)|(), ()|(2,1,1) 3 15.0 each 15 each
(1,1,1,1)|(), ()|(1,1,1,1) 1 1.0 each 1 each

All 20 irreps sum-rule-check pass. Total dim × multiplicity over all irreps = 4096 ✓ (the full R⁴⁰⁹⁶ ambient).

Notable eigenvalues: - λ = +36.0631 (Perron / max): (4)|(), dim 1 - λ = −36.0631 (anti-Perron / min, bipartite partner): ()|(4), dim 1 - λ = ±2.0 exactly: hosts (1,1,1,1)|() and ()|(1,1,1,1) respectively (the "sign-determinant" 1-dim B₄ irreps), dim 1 each — rational eigenvalues in an otherwise-irrational 4D spectrum - λ = 0 (kernel): dim 96, splits as 10·(2)|(2) + 6·(1,1)|(1,1)

Numerical precision (max deviation from nearest integer multiplicity, 4D): 2.975 × 10⁻¹⁴. Comfortably within machine precision (LAPACK dsyevr floor ~ O(n · ε_machine) ≈ 9 × 10⁻¹³ on N=4096).


3 — Structural prediction without closed-form eigenvalues

This is the load-bearing result for the dispatch.

The original chess D₄/B₄ rep-theory spike (2026-05-11) established that closed-form eigenvalue prediction matches at machine precision for rook, king, bishop (in both 2D and 4D, under three structural primitives: Cartesian product, strong product, parity stratification). Knight was the predicted non-match — explicitly per Rinaldi-Unciuleanu & Chiru 2026 §3.6, knight admits no clean product or parity factorization.

This follow-up shows: the absence of closed-form eigenvalues for the knight does NOT preclude structural prediction. The eigenspace decomposition into D₄ / B₄ irreps is integer-exact to machine precision in BOTH 2D and 4D. The prediction is weaker than "you can write down the eigenvalues" but stronger than "no structure" — you can predict:

  1. The menu of possible eigenspace dimensions — exactly the irrep dimensions of the symmetry group.
  2. The total multiplicity of each irrep across all eigenspaces — exactly its multiplicity in the natural rep on R^N (independent of the operator).
  3. Most eigenspaces are pure — only λ=0 mixes irreps (and only when the operator has a non-trivial kernel).
  4. The bipartite structure — when the operator is bipartite-equivariant, irreps pair off as (α, β) ↔ (β, α) at eigenvalues and −λ.

This is a rep-theoretic invariant distinct from closed-form eigenvalue prediction. The chess spike's six previous sub-instances (rook 2D/4D, king 2D/4D, bishop 2D/4D) used the spectrum-formula identity; the knight 2D/4D follow-up uses the eigenspace-structure identity — same closed-form-from-rep-theory motif, different load-bearing prediction.

Motif refinement for §3.5.3(C)

The chess instance is the strongest §3.5.3(C) instance because it now spans two distinct prediction modes within a single domain:

Mode Pieces What is closed-form-predicted Empirical match
Mode I: closed-form eigenvalues rook, king, bishop (2D + 4D) full spectrum at machine precision 6/6 cases ≤ 3.7 × 10⁻¹³
Mode II: closed-form eigenspace structure (new) knight (2D + 4D) eigenspace dims = irrep dims; per-irrep totals = natural-rep multiplicities; mixing only in kernel 2/2 cases ≤ 2.98 × 10⁻¹⁴

Eight sub-instances now stand under D₄ / B₄ within a single domain, all at machine precision. Compare: - MFO Phase B 18-block (D₃ on Sierpinski Gasket): single sub-instance. - Finance block-correlation (S_k × S_m on synthetic correlation matrix): single sub-instance. - Chess (D₄ / B₄ on hypercube move graphs): now eight sub-instances under two prediction modes.


4 — Implementation: B₄ character table from first principles

For reproducibility, the B₄ character table (20×20) was computed from first principles using the standard Specht / Frobenius formula:

χ^{B_n}_{(α,β)}((λ⁺, λ⁻)) =
   coefficient of s_α(x) · s_β(y) in
   ∏_i (p_{lp_i}(x) + p_{lp_i}(y)) · ∏_j (p_{lm_j}(x) − p_{lm_j}(y))

In our implementation: for each cycle of λ⁺ or λ⁻, distribute it to either the α-side or the β-side; negative cycles assigned to the β-side contribute a sign of −1. Then χ is summed over all distributions of S_|α|-character × S_|β|-character of the cycle-multisets. S_n characters computed via Murnaghan-Nakayama rule.

Verification. The computed B₄ character table satisfies: - Row-orthogonality <χ_ρ, χ_σ>_G = δ_{ρσ} for all 20·19/2 + 20 = 210 inner products (verified). - Sum of squared dimensions = 384 = |B₄| (verified: 4·1² + 2·2² + 4·3² + 4·4² + 4·6² + 2·8² = 4+8+36+64+144+128 = 384). - B₄ irrep dimensions: {1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 6, 6, 6, 6, 8, 8} — match standard reference (e.g., Fulton-Harris Appendix B).

Class data: 20 conjugacy classes indexed by signed bipartitions of 4. Centralizer formula |C_{B_n}(g)| = ∏_k (2k)^{m_k^+} m_k^+! · (2k)^{m_k^-} m_k^-! (James-Kerber 4.2.10). Class sizes sum to 384 ✓.


5 — Honest verdict per metric

Load-bearing-question answer: Per-eigenspace irrep multiplicities are integer-exact at machine precision in BOTH 2D and 4D. Max deviation from nearest integer: - 2D: ≤ 1 × 10⁻¹⁴ (per-eigenspace projector formula on 64-dim space) - 4D: 2.975 × 10⁻¹⁴ (per-eigenspace character-orthogonality formula on 4096-dim space)

Tolerance target was 1 × 10⁻⁶ (forgiving — accommodates accumulated FP error from eigh on 4096-dim matrix). All 16,500 (4D irrep × eigenspace) combos and 225 (2D combos) are integer-exact to far tighter than that bound.

Sum-rule check passes for all 5 D₄ irreps (2D) and all 20 B₄ irreps (4D). Per-irrep totals exactly match the natural-rep decomposition multiplicities computed independently via Burnside / fixed-point counting.

Caveats — what was tested: - Adjacency-matrix eigenvalues (unweighted; integer 0/1 entries). The unsigned graph Laplacian L = D − A would shift the spectrum but preserve the eigenspace structure (since L is also D₄/B₄-equivariant); the multiplicity structure transfers identically. - Empty board (no other pieces). Knight move-graph is the canonical empty-board displacement graph. - 8-cell side length only. Generalizing to side length N is straightforward — the move set is unchanged, only the on-board check varies; D₄/B₄ equivariance survives.

Caveats — what was NOT tested: - Higher-dim knight (5D, 6D) for B_N regression. - Toroidal variants ((Z/8Z)^d) — different equivariance group structure. - Weighted move-graph variants. - B₄ irrep label correctness against an external source (the table was verified internally via orthogonality and dim²-sum but not against a tabulated reference such as GAP / Magma / James-Kerber); the integer-exactness of multiplicities is independent of label convention.

Caveats — methodological: - numpy.linalg.eigh (LAPACK dsyevr) is deterministic and well-tested; accumulated FP error scales as O(N · ε_machine) ≈ 10⁻¹³ for N=4096. Multiplicity deviations are about an order of magnitude smaller because character-orthogonality is itself an integer sum after dividing by |G|; the inner products of eigenvectors with their permuted selves are the load-bearing FP operations. - Eigenspace grouping tolerance was 1 × 10⁻⁸. Smallest empirical gap between distinct eigenvalues was 2.7 × 10⁻⁵ — comfortably above tolerance, no risk of merging distinct eigenspaces.


6 — Anomalies

Anomaly 1 (predicted, confirmed). The λ=0 eigenspace in 4D is the only one that mixes irreps. It has dim 96 = 60 + 36, decomposing as 10·(2)|(2) + 6·(1,1)|(1,1). Independent rank check confirms this is the true 96-dimensional kernel of the knight 4D adjacency (rank 4000 + nullity 96 = 4096). The 2D analog: λ=0 hosts 4 copies of E (dim 8) — single irrep type, but multiple copies, so technically "mixed in multiplicity" though all the same irrep. The 4D mixing is dimensionally richer: two distinct 6-dim irreps coexist. This is the only place per-eigenspace structure is non-discriminating in 4D — at the kernel, the rep theory predicts the dim (96) and the irrep content ((2)|(2) + (1,1)|(1,1) with multiplicities 10 and 6) but not which specific eigenvectors host which irrep.

Anomaly 2 (predicted, confirmed). Knight bipartiteness implies ±λ mirror pairs. Verified empirically: every non-zero eigenvalue has a partner −λ; corresponding irreps are swap pairs (α, β) ↔ (β, α). Examples: +36.063 → (4)|(), −36.063 → ()|(4); +2 → ()|(1,1,1,1), −2 → (1,1,1,1)|().

Anomaly 3 (new). The eigenvalues λ = ±2 are integer-exact in the knight 4D spectrum (machine-precision integer). The knight has 48 displacement vectors; some of these admit rational eigenvalue contributions from a Fourier-like substructure. Specifically: the (1,1,1,1)|() and ()|(1,1,1,1) are 1-dim "sign" irreps of B₄ (B_n analog of the alternating-sign rep). The 1-dim sign-rep eigenvectors on the hypercube must have specific sign patterns under the B_4 action; for the knight 4D specifically, the value happens to be integer-exact ±2. Not a coincidence, but worth noting: it's the analog of the λ=−2 rational eigenvalue in rook 2D and bishop 2D (also at machine-precision integer). Would be interesting to derive closed-form via the displacement-set Fourier expansion; not done here.

Anomaly 4 (new structural prediction). The histogram of eigenspace dimensions in 4D is exactly the histogram of irrep dimensions of B_4 (each irrep contributes (mult-in-natural-rep / irrep-dim) eigenspaces; the dim-96 kernel hosts two irreps so contributes 1 eigenspace). This is the structural prediction even more compactly: for a generic B₄-equivariant operator with full-rank-on-each-isotypic-block behaviour, the eigenspace dim histogram is determined by the natural-rep decomposition. Knight 4D is the first project instance demonstrating this empirically at machine precision.


7 — Fermata records

Fermata 1: §3.5.3(C) chess instance refinement. The chess instance now spans two prediction modes (closed-form eigenvalues for rook/king/bishop; closed-form eigenspace structure for knight). Conductor decision: refine §3.5.3(C) chess sub-instance from "six chess sub-instances under three structural primitives" to "eight chess sub-instances under two prediction modes" (Mode I: closed-form eigenvalues; Mode II: closed-form eigenspace structure)? Recommendation: YES. The motif refinement is mathematically distinct — Mode I predicts spectrum, Mode II predicts isotypic decomposition. Both are rep-theoretic structural predictions from group G.

Fermata 2: B₄ irrep label canonicalization. The script's B₄ irreps are labelled (α)|(β) for pairs of partitions. Labels were verified internally (orthogonality + dim²-sum) but not against external reference. For project-wide use as a reusable B_n primitive (potential future ephemerides / chess-spectral / power-grid applications), cross-check against e.g. GAP CharacterTable("WeylB",4) recommended. Recommendation: queue this if/when B_n character table is needed elsewhere; the integer-exact-multiplicity verdict here is independent of label convention.

Fermata 3: cross-domain candidate — Sierpinski-Gasket per-eigenspace decomposition. MFO Phase B's 18-block result (D₃ on SG λ=6 eigenspace: 22A + 18B + 40E) was a single-eigenspace fingerprint. The chess knight result is the same structural prediction extended to every eigenspace of a non-product graph operator. Conductor question: is per-eigenspace D₃ decomposition for the FULL SG decimation Laplacian (not just λ=6) also integer-exact at machine precision? Recommendation: queue; this would be the MFO sibling of today's spike, testing whether the SG Laplacian's full eigenspace structure (not just one chosen eigenspace) is rep-theoretically predictable.

Fermata 4: extension candidates within chess. Queen (= rook ∪ bishop) is the other non-product piece. In 2D, queen has D₄ equivariance; per-eigenspace D₄ irrep multiplicities should also be integer-exact (same structural argument). In 4D, queen is the 6-axis-pair bishop union 4-axis rook — a much richer non-product structure. Recommendation: queue, low priority. The motif is already established by knight.


8 — Reproducibility

Script: chess-knight-irrep-multiplicities-script.py

Per-eigenspace NDJSON: chess-knight-irrep-multiplicities-per-eigenspace-2026-05-11.ndjson — 16,725 records: 225 (2D = 45 eigenspaces × 5 D₄ irreps) + 16,500 (4D = 825 eigenspaces × 20 B₄ irreps).

Reproduction: python docs/srmech/notes/chess-knight-irrep-multiplicities-script.py

Runtime: ~44 s on commodity workstation. Deterministic across runs. Memory peak: ~265 MB (the 4096×4096 knight adjacency, eigenvector matrix, and 20 permutation arrays all held briefly). Dominant cost: eigh(4096×4096) ≈ 15 s + per-class-trace inner products ≈ 15 s + S_n / B_4 character table ≈ 1 s.

Library versions: numpy 2.4.4 only (LAPACK via linalg.eigh); standard library. No SGD, no learned parameters, no test-set tuning. No sympy / no external character-table dependency.

Citation: Rinaldi-Unciuleanu & Chiru 2026, A Mathematical Framework for Four-Dimensional Chess: Extending Game Mechanics Through Higher-Dimensional Geometry, AppliedMath 6(3) 48, DOI 10.3390/appliedmath6030048. Vendored at docs/srmech/hoodoos/rinaldi-unciuleanu-chiru-2026.xml. §3.6 names knight as the non-product case; this spike's contribution is the per-eigenspace structural prediction.

B_n character formulation: Specht / Frobenius formula via power-sum-Schur bridge. Standard reference: James-Kerber, Representation Theory of the Symmetric Group (Encyclopedia of Mathematics and its Applications 16, 1981), Theorem 5.5; equivalent to Geissinger 1977. S_n characters via Murnaghan-Nakayama rule. Centralizer formula (James-Kerber 4.2.10) verified by class-size sum = |B_4| = 384.

Project contribution: the per-eigenspace integer-exact multiplicity identity for the knight move-graph is the project's contribution (not in Rinaldi-Unciuleanu & Chiru 2026, which names "spectral analysis of move graphs" as future work). Same MPM-provenance pattern as the prior chess D₄/B₄ spike.