chess-spectral 1.17.0

Spectral lattice-fermion encoder for 2D and 4D chess (D4 / B4 symmetry, graph-Laplacian eigenbasis, 640-dim / 45 056-dim HDC)

chess_spectral (Python)

Python reference implementations of the 640-dim 2D and 45 056-dim 4D spectral chess encoders, plus the quantum-mechanical front-end (2D + 4D kinematics; 4D dynamics shipped in v1.5, 2D dynamics in v1.6.x), the v1.6 §16 search + tournament + sweep engine surface, the v5 unified wire format (three encoding modes — dense / per-channel replacement / XOR-stream — with empirical 7.23× compression on 4D fixtures vs dense gzipped), the v1.7 native bitboard fast-path

• time-budget mid-iteration honoring (cumulative ~125× speedup on dense legal_moves() calls vs v1.6), and the v1.8 GameState4D push/pop + board accessors + check predicates consumer surface (chess4D-OC visualizer M11.40 unblocker).

Sibling of the C17 port in ../src/. Use the Python package for REPL / LLM / notebook analysis, Pyodide-bridge consumers, and the §16 ship-gate matrix runner; use the C binaries for batch encoding throughput.

The pieces ship under two top-level packages:

• chess_spectral — 2D encoder + 4D encoder math + 4D phase operators + QM extension. Everything that's pure spectral / B_4 representation theory lives here.
• chess_spectral_4d — 4D game-state surface (move history, side-to-move, draw status, FEN4 round-trip, the Pyodide chess_spectral_4d.bridge module). Splits cleanly from the encoder so the 4D-rules concerns don't bleed into the spectral math.

What's new in v1.17 (May 2026)

Vectorize the per-piece Python loop in the pure-phase encoders — the deferred item from 1.14.0/1.15.0 amendment notes. Result is much stronger than the original 30-50× estimate suggested for the fiber loop alone, but the integrated encoder speedup is dramatic: 2D pure-phase goes from 1.64× SLOWER on opening (1.14.0's mixed result) to 1.88× FASTER; 4D pure-phase wins 1.73-2.47× across all piece-count regimes vs the float baseline.

The 1.14.0 §20.21 framing of pure-phase as "mixed/positional, not a clean win" was an artifact of per-piece Python overhead masking the int-arithmetic architectural advantage. Vectorize lifts that mask.

2D pure-phase reversal

Position	1.14.0 (loop)	1.17.0 (vectorized)
opening	1.64× SLOWER	1.88× faster
midgame	parity	1.13× faster
endgame	1.50× faster	1.45× faster

4D pure-phase across piece counts

n_pieces	float	pure-phase	speedup
4	2.8 ms	1.6 ms	1.73×
24	7.1 ms	2.9 ms	2.47×
64	14.4 ms	6.0 ms	2.40×
128	27.4 ms	12.1 ms	2.25×

Three vectorization patterns:

• Loop-swap + einsum batching (2D fiber-sym): group occupied squares by FIBER piece type (5 types: N/B/R/Q/K), batch all 3 d-channels via einsum.
• Loop-swap + broadcast (4D fiber-sym): per-piece outer + 3-d broadcast inner, eliminating the 3× redundant sparse-row gather.
• Group-by-piece-type aggregation (4D FD_DIAG): 6 piece-row buckets vs N pieces — 10× fewer 4096-vector adds.

Plus a minor STD4 axis-broadcast vectorize on the 4D side.

87 tests (immolation + stress) pass after the vectorize — bit-exact preserved on every position in the 1500-position random stress corpus.

This is a closed empirical chapter: the §20.15 5-phase BSHDC roadmap shipped 1.10-1.14, the 1.15 4D parity restoration closed the 2D/4D drift, and 1.17 surfaces the architectural win that 1.14's bench had hidden.

What's new in v1.15 (May 2026)

4D pure-phase encoder ships — completing the chess2d/chess4d parity restoration begun by 1.14.0's amendment 1. The 1.14.0 ship of B-spike-4 was 2D-only; the §20.15 4D pure-phase encoder was deferred to "1.15.0+" with the open question of how to integerize the B₄-driven 4D structure (no integer character formula analog to D₄'s ±1, ±2, 0 character table). 1.15.0 closes the gap.

• encode_4d_pure_phase(pos4) — int32 output, integer arithmetic throughout. encode_4d_pure_phase_to_float(pos4) — same encoder with per-channel dequantization; drop-in for encode_4d comparison.

• A_1 channel uses the scale-by-LCM integerization trick: every B_4 orbit size divides 384, so P_A1 × 384 has all-integer entries (specifically, divisors of 384). Lossless — bit-exact relative to the float baseline.

• STD4 channels (X/Y/Z/W) use scale-by-4 for the quarter-integer coord_resid table (multiples of 0.25, range [-5.25, +5.25]). Lossless — bit-exact.

• Fiber / pawn / diag channels use the same int16-quantized approach as 2D, with per-table max-abs scale factors. Lossy at the int16 quantization level (≥ 99.999% cosine-sim).

• Empirical speed verdict — uniformly equal or faster (the OPPOSITE of 2D's mixed result): bench at iters=100 on random positions, sparse / midgame / dense:

  Position	float (µs)	pure_int (µs)	pure/float
  sparse n=4	10 707	10 549	0.99×
  midgame n=24	21 805	21 095	0.97×
  dense n=128	88 996	70 283	0.79×

  The dense-position win comes from sparse matvec being integer-ALU friendly + STD4 elementwise being SIMD-friendly. Real chess4D-OC visualizer benefit at 28-king Oana-Chiru initial.

• 29 new tests lock the surface: 21 immolation tests (cosine-sim, bit-exactness for A_1 + STD4, channel-energy Spearman, edge cases) + 8 stress tests on 500 random 4D positions (per-position cosine ≥ 0.99, median ≥ 0.999, A_1 + STD4 bit-exact at scale, no NaN, int32 bounded, deterministic).

This closes the 2D/4D pure-phase parity gap. The remaining encode_4d_pure_phase deferral from 1.14.0 amendment 1 is now discharged.

What's new in v1.14 (May 2026)

The §20.15 fifth and final phase: pure-phase encoder rewrite (B-spike-4). Integer arithmetic throughout the encoder hot path — D₄ irrep projection is integer at the core, fiber tables are int16-quantized at module load, dequantization happens at the channel-output boundary. New encoder_pure_phase module. Acceptance gate met (cosine-sim ≥ 99.998% vs float baseline; D₄ channels bit-exact). Speed result is mixed/positional, not a clean win. All five §20.15 phases now shipped.

• encode_2d_pure_phase(pos) — int32 output, integer arithmetic throughout. encode_2d_pure_phase_to_float(pos) — same encoder with per-channel dequantization; drop-in for encode_640 comparison.

• D₄ channels (A1/A2/B1/B2/E) are bit-exact vs the float baseline — the character formula is integer at the core.

• Fiber channels (F1/F2/F3/FA/FD) use int16-quantized tables with per-table scale factors; cosine-sim ≥ 99.998% agreement.

• Empirical speed verdict — mixed: 1.64× SLOWER at opening (dense), parity at midgame, 1.50× FASTER at endgame (sparse). The runtime winner remains spectral_hybrid_8bit_lru (1.13.0+) at ~50 µs across all corpora. Honest finding documented in §20.21.

• Half-integer bishop fix: VALS has B=3.5; pure-phase scales the signal by 2 and absorbs the factor in the dequant scale. Documented as _VALS_INT_SCALE = 2.

• 21 new immolation tests lock cosine-sim acceptance, D₄ bit-exactness, Spearman ρ ≥ 0.99, quantized-table contracts.

B-spike-4 in the §20.15 phasing — the fifth and final phase. 1a → 1b → 2 → 3 → 4, all shipped within ~5 days.

1.14.0 amendment (May 2026) — 4D parity restored + pedantic stress tests added. The original 1.14.0 ship was 2D-only and 1.13.0's spectral_hybrid evaluator family was 2D-only too — both flagged as a parity regression. Amendment:

• 4D evaluator parity restored: evaluate_from_hybrid_4d, channel_energies_from_hybrid_4d, evaluate_4d, make_cached_evaluator_4d — full parity with the 2D versions, using the 1.12.0 SpectralBIPHybrid4D storage.
• Pedantic stress testing: 1000 random 2D positions for the pure-phase encoder, 500 random 4D positions for the hybrid evaluator family. All 17 stress tests pass. Acceptance gates (cosine-sim ≥ 0.99 every position; D₄ bit- exactness; channel-energy Spearman ρ ≥ 0.99; no NaN; LRU semantics) hold at scale.
• encode_4d_pure_phase deferred to 1.15.0+. 2D's pure-phase encoder works because D₄ has an integer character formula; 4D's B₄ structure uses sparse-matrix arithmetic without the same convenience. The 4D hybrid encoder (1.12.0) already provides integer storage; this amendment closes the eval-side parity gap.

1.14.0 amendment 2 (May 2026) — chess4D-OC consumer wishlist surface. Late-cycle additions driven by the chess4D-OC pre-publish wishlist; all ship in 1.14.0 (no minor bump — public surface only grows):

• Tier 1: M14.4c entanglement-viz unblockers
  • qm_4d_bridge.get_qm_density_from_psi(psi) and qm_4d_bridge.get_probability_current_from_psi(psi) — ψ-direct variants of the existing state-driven functions, for the post-collapse render path. The current-from-psi variant returns j flattened to (16384,) (cell-major) so consumers don't re-flatten in the worker.
  • qm_4d_bridge.get_density_matrix_of(state, piece_id, *, neighborhood_radius=1) — partial implementation that replaces the previous unconditional NotImplementedError. Computes a Manhattan-neighborhood channel reduced density giving per-piece purity for the M14.3 entanglement-halo viz to light up. Carries an isPartial: True flag; the full η-metric construction (ADR-005) ships later with the same signature.
• Tier 2: consumer ergonomics
  • HybridCache.clear() — drops cached entries + resets counters for the chess4D-OC reset path.
  • qm_4d_bridge.channel_energies_2d / qm_4d_bridge.channel_energies_4d — Pyodide-friendly entry points (encode + channel-energy in one shot, JS-serializable dict). Saves consumers from importing the engine sub-package.
• 33 immolation tests lock the wishlist surface contracts; the existing 95-test bridge / hybrid-eval surface continues to pass.

What's new in v1.13 (May 2026)

The §20.15 fourth-tier ship (partial): encoder-eval speedup work. A new spectral_hybrid evaluator computes channel energy directly from a SpectralBIPHybrid2D (skipping the float decode step), an LRU-cached wrapper makes that practical for the §16 search engine, and a benchmark harness records before/after numbers for each path. ~15× speedup at the warm-LRU steady state vs spectral_float64.

• spectral_hybrid module — evaluate, evaluate_from_hybrid, channel_energies_from_hybrid. The mathematical identity: ‖v_c‖² = Σ (sign × mag)² = Σ mag² (sign cancels in squared sum), so use uint8² sum + per-channel scale² multiply. Skips the float decode step entirely on the cache-hit path.

• spectral_hybrid_cache module — HybridCache LRU plus make_cached_evaluator(magnitude_bits=8, cache_size=10000) factory returning (evaluator_fn, cache). Drop the callable into SearchOptions.evaluator and inspect cache.stats() for hit/miss diagnostics. Models the §16 TT-cache-hit pattern.

• spectral_float32 module — float32 downstream sibling mirroring the ephemerides two-stage architecture (their complex128 → complex64 is our float64 → float32). Shipped despite a null bench result on the standalone path; a build- block for future float32-native encoder work.

• tests/bench_spectral_eval.py — diagnostic benchmark harness with three corpora (opening / midgame / endgame). Variants registered as plug-ins; the discipline: no speedup claim ships in the CHANGELOG without measured numbers from this harness on a fixed corpus. Baselines saved at tests/bench_baselines/before_1.13.0.json.

• §16.7 amendment — the Othello prior (Edax-spectral, +243 Elo at L6 / 0 Elo at L10+) is now flagged as ML-fork-contaminated; B-spike-3 is no longer gated on the depth-decay claim. The §16.5 design discipline (test multiple depths, audit training target, don't trust eval-task RMSE as Elo proxy) remains sound.

• 33 new immolation tests lock the algebraic identity (channel- energy from hybrid agrees with float64 within 5% relative on the corpus; sign agreement exact at 8-bit and 4-bit), the LRU cache semantics, and the float32 sign-agreement-within-1e-4 property.

This is B-spike-3 in the §20.15 phasing — 4 of 5 phases now shipped (1.10.0-1.13.0). B-spike-4 (pure-phase rewrite) remains parked behind clear empirical motivation.

What's new in v1.12 (May 2026)

The §20.15 third-tier ship: encoder BIP-hybrid — sign × magnitude factoring for the spectral encoder. Sign packs as 1 bit per dim (algebraically exact); magnitude quantizes to 4 or 8 bits per dim with per-channel scaling. Cosine-sim ≥ 99.99% at 8-bit on both 2D and 4D acceptance corpora. No breaking changes vs 1.11.x; encode_640 / encode_4d defaults unchanged.

• Storage: 760 bytes (2D) / 50 KB (4D) at 8-bit, vs 2,560 / 180 KB float32 — about 3.4-3.6× compression with negligible cosine-sim loss. At 4-bit: 440 / 28 KB → 5.8-6.4× compression; passes acceptance for 2D, lands just under 99.5% for 4D (research finding documented in §20.18).

• encode_2d_bip_hybrid / encode_4d_bip_hybrid — new public API. Returns SpectralBIPHybrid2D / SpectralBIPHybrid4D dataclasses with three integer fields: sign_packed (bytes), magnitude_scales (per-channel float32 norms), magnitudes (uint8 — packed nibbles for 4-bit, plain bytes for 8-bit).

• decode_2d_bip_hybrid / decode_4d_bip_hybrid — reconstruct float64 vector. Lossy at the magnitude-quantization level; sign storage is exact.

• cosine_similarity_hybrid_* — pair-wise distance metric for hybrid vectors. Approximates the float32 baseline within 1% absolute deviation at 8-bit.

• Bridge surface — chess_spectral_4d.bridge.{encode_position_2d_bip_hybrid, encode_position_4d_bip_hybrid} for chess4D-OC / Pyodide consumers. Returns plain Python int/list across the WASM boundary.

• 62 immolation tests lock the §20.15 acceptance gate (8-bit on 2D + 4D), the sign-bit storage exactness, the per-channel magnitude bounds, the 4D 4-bit research finding (sim ∈ [0.99, 0.999]), the pair-wise cosine-sim approximation, and the 4-bit nibble packing round trip.

This is B-spike-2 in the §20.15 three-tier phasing. B-spike-1a (sheet-block BIP) shipped in 1.10.0; B-spike-1b (ALU-native phase engine) shipped in 1.11.0; B-spike-3 (search-engine integration) remains parked.

What's new in v1.11 (May 2026)

The §20.15 second-tier ship: ALU-native phase-operator move generator. The §11 phase-operator engine — already integer- arithmetic at the core, already BIP-isomorphic at the group- theoretic level over Z_640 — gains a public integration entry point that doesn't require a python-chess.Board argument. Pure ALU-native pseudo-legal move generation for Pyodide / WASM / non-Python downstream consumers. No breaking changes vs 1.10.x.

• phase_only_pseudo_legal_moves(pos, side_to_move_white, ep_file=...) — new integration entry point. Iterates the side-to-move's pieces, computes per-piece destination sets via Solution B's phase-arithmetic, returns a flat list of (from_sq, to_sq, promotion_char) tuples in encoder sq convention. Pseudo-legal in the python-chess sense (respects piece geometry, occupation, double-push, en passant, promotion expansion); caller filters check.

• occupation_field_from_pos_dict + ep_phase_from_ep_file — pure-phase adapters that skip python-chess entirely. Counterparts of the existing occupation_field_from_board and ep_phase_from_board; consumers that have a position dict + side-to-move + ep_file (e.g., from a 1.10.0 BIP-encoded sheet block) can build the occupation field directly without reconstructing a Board.

• Acceptance gate: parity vs board.pseudo_legal_moves on a representative 8-FEN corpus (opening, middlegame, endgame, EP-active, promotion-imminent). All move sets match exactly modulo castles (deferred to 1.12.0+). 26 tests, pass on first implementation.

• Documented Z_640 wire contract (notebook §20.17). The constants MODULUS, ROW_GEN, COL_GEN, DIAG_NE_SW_GEN, DIAG_NW_SE_GEN, KNIGHT_SHIFTS, KING_SHIFTS are part of the public API and don't move without a major version bump.

• Diagnostic benchmark (tests/bench_phase_operator_movegen.py) — ~190 µs at startpos for ALU-native vs ~73 µs for python-chess Cython (~2.5× ratio, expected). Structurally faster than Solution-B-per-piece-loop because the occupation field is built once per position instead of per-piece. Value is portability, not raw speed.

• Deferred to a 1.12.0+ follow-up: castling generation in the ALU-native path (needs attack map), check-filter refactor of phasecast_is_check (currently takes a Board input). Consumers wanting check-filtered legal moves should pair the 1.11.0 pseudo-legal output with the existing phasecast_is_check.

This is B-spike-1b in the notebook §20.15 three-tier phasing. B-spike-1a (sheet-block BIP) shipped in 1.10.0; B-spike-2 (encoder BIP-hybrid) is the next ship and depends on the bit-packing patterns 1.10.0 established.

What's new in v1.10 (May 2026)

The §20 BSHDC spike's first ship: BIP-encoded sheet block. Integer-native form of the 1.9.0 non-Markovian aux block that packs the same content into 3 bytes (vs 88 bytes float64) with a bit-exact round trip on the legal state space. ALU-native operator fast paths for Pyodide / chess4D-OC / batch retrieval consumers. No breaking changes vs 1.9.x; both representations ride side-by-side.

• SheetStateBIP dataclass — categorical: uint16 + halfmove_clock: uint8. ~29× smaller than the 1.9.0 float64 path. Round-trip exact for all 87,264 legal sheet states (864 categorical × 101 half-move) — verified exhaustively in tests/test_sheets_bip.py. The Z₁₀₁ half-move stays in its own uint8 slot per §19.4's "structural split" finding (no embedding into Z₁₂₈ that would introduce a wrap discontinuity).

• Operator fast paths — single-integer-op queries against the categorical portion: castling_alive, kingside_castling_alive, ep_target_active, fifty_move_rule_triggered, threefold_claimable, etc. Each is a bit-mask + comparison, no FPU.

• Distance metrics — hamming_distance_categorical(a, b) for corpus-similarity retrieval where binary state distinctions matter (sharper than float cosine-sim for the categorical portion). halfmove_distance(a, b) for the integer Z₁₀₁ slot.

• Bridge surface — chess_spectral_4d.bridge.get_sheet_state_bip / encode_sheet_aux_bip / decode_sheet_state_from_bip. All numpy-free across the WASM boundary; integers cross the Pyodide bridge directly.

• Where BIP wins: Pyodide / WASM consumers (50-100×), batch retrieval over saved corpora (~3 orders of magnitude), Hamming-distance corpus filtering, future v5 mode-2 XOR-stream wire format compression. Where it doesn't: single-position depth-1 queries (per §19.10's bitboard floor — bit-shift + AND can't undercut python-chess's int & mask either).

• 35 new immolation tests (tests/test_sheets_bip.py) lock the 87,264-case exhaustive round trip, the 8-operator parity vs the 1.9.0 SheetState path, the Hamming-distance behavior (including the subtle case that rep=1 vs rep=2 differ in 2 bits, not 1, due to binary representation), the bridge round-trip, and the python-chess + GameState4D factory parities.

This is B-spike-1a in the notebook §20.15 three-tier phasing. B-spike-1b (promote §11 phase-operator engine to public API) remains independent and can ship separately. B-spike-2 (encoder BIP-hybrid) depends on the bit-packing patterns this 1.10.0 establishes.

What's new in v1.9 (May 2026)

The §19 spike's Phase-1 sheet block ships as a representation-completeness feature alongside a new legal-moves CLI command for both 2D and 4D. No API breaks vs. 1.8.x; every addition is opt-in surface.

• Non-Markovian sheet aux block (from chess_spectral import SheetState, encode_aux_block) — the 11-dim aux block carries castling rights, en-passant target, side-to-move, half-move clock, and repetition count alongside the base encoder vector. Opt in via the new sheets= kwarg on encode_640(pos, sheets=...) and encode_4d(pos4, sheets=...); output dimension grows from 640 → 651 (2D) and 45056 → 45067 (4D), with the base dims byte-identical to legacy / C encoder output. Lift state from python-chess via SheetState.from_chess_board(board) or from a 4D GameState4D via SheetState.from_game_state_4d(state). Half-move clock uses a Z_101 Fourier carrier preserving clock-distance fidelity; round-trip exact for every integer [0, 100].

• Representation only — no speed claim. Notebook §19.10 documents the depth-1 bitboard floor: python-chess's has_castling_rights is one int-AND, and float→int conversion from a numpy slice can't undercut it. The same logic holds for Board4D.halfmove_clock >= 100 and the hash-table threefold- repetition lookup. The sheet block's value is making encoder vectors self-sufficient for downstream consumers of saved / transmitted vectors (chess4D-OC, Pyodide pipelines, batch retrieval over saved corpora) — not raw runtime speed.

• legal-moves CLI command (2D) — chess-spectral legal-moves --fen "<fen>" [--format uci|san|json] [--with-sheets] enumerates the side-to-move's legal moves. Default UCI output, one move per line; JSON format includes per-move flags (capture / castling / en-passant / promotion) plus optional sheet block. Stdin support via --fen -. Uses python-chess for legal-move generation.

• legal-moves CLI command (4D) — chess-spectral-4d legal-moves --fen4 "<fen4>" [--format compact|json] [--with-sheets] [--side-to-move white|black] [--halfmove-clock N] [--fullmove-number N] enumerates legal moves at a 4D-OC position. Default compact format x,y,z,w->x,y,z,w with DP (double push), EP (en-passant capture), and =Q (promotion) tags; JSON format structured. Uses Board4D.legal_moves() (the native 4D move-gen via graph-Laplacian-derived primitives).

• Pyodide-bridge surface for sheets — chess_spectral_4d.bridge gains get_sheet_state, encode_sheet_aux, and decode_sheet_aux_from_vector. The bridge contract holds ("plain dict, no numpy across the WASM boundary"); aux blocks cross as list[float]. chess4D-OC and any other browser / Pyodide consumer can now reason about castling rights / EP / halfmove / repetition without reconstructing a python-chess.Board or a GameState4D worker-side.

• encode_2d future-proof alias — from chess_spectral import encode_2d (1.9.0+) is the recommended name going forward, mirroring the 4D path's encode_4d. encode_640 remains as a permanent alias, but the dim count is not a stable contract — sheets already bumped 640 → 651, and future channel enrichment (per the §20 BSHDC spike's antikythera-spectral reference) or further non-Markov state extensions may move it again. Query ENCODING_DIM (or check enc.shape) and iterate channels via CHANNELS rather than hardcoding 640. See notebook §19.11 for the full future-work note.

• 53 new immolation tests lock the sheet round-trip (every legal halfmove value, every castling combination, every EP file), the encoder integration (base preservation + aux at correct offset), the factory lifts (from_chess_board, from_game_state_4d), the CLI smoke surface for both 2D and 4D legal-move enumeration, and the bridge round-trip (state → get → encode → decode) for both dimensions.

1.9.1 update — README polish + v5 wire format design decision. Quick-start example switched from encode_640 to the future-proof encode_2d alias (the 640-dim count is no longer a stable contract — see §19.11). PyPI project links gain a Roadmap entry pointing at ROADMAP.md. v5 wire format officially documented as not carrying the sheet aux block — sheets ride alongside in-memory vectors, and on-disk the source PGN / NDJSON / FEN sidecar already carries the non-Markov state. See frame_v5.py header note + notebook §19.12 for full rationale and the future- extension path (223 reserved bytes in the v5 header give plenty of room when a consumer eventually needs persisted sheet frames).

What's new in v1.8 (May 2026)

The chess4D-OC visualizer's M11.40 unblocker release. Tier-1 of the upstream wishlist ships in 1.8.0 — GameState4D graduates from a position+history snapshot to a persistent mutation type, mirroring python-chess.Board's push/pop ergonomics so the chess4D-OC worker can drop the python-chess4d-oana-chiru runtime dep. No API breaks vs. 1.7.x — every addition is opt-in surface.

• GameState4D.push(move) / GameState4D.pop() — apply / undo a ply, mutating in place. push accepts a Move4D or a ((from_xyzw), (to_xyzw)[, promote_to]) tuple. pop raises IndexError on an empty history (parallel to chess.Board.pop()'s contract). Returns the recorded / popped Move4D so callers can recover capture / promotion metadata.

• GameState4D.board view — read-only proxy over the live position dict. Exposes occupant(sq) and pieces_of(side) accessors plus __contains__ and __len__. sq accepts both the linear int and the (x,y,z,w) Coord4D tuple. The view does not copy — push / pop mutations are reflected immediately.

• GameState4D.to_fen() / GameState4D.from_fen(fen4) — symmetric aliases for to_fen4 / from_fen4 (the 1.7.1 slash-tolerant FEN4 form is accepted on both names).

• GameState4D.iter_pieces() — yields (sq_idx, piece_value) tuples in the format chess_spectral.encoder_4d.encode_4d consumes directly. dict(state.iter_pieces()) is a one-liner replacement for the chess4D-OC worker's previous _state_to_pos4 helper.

• chess_spectral_4d.engine.search.search() accepts GameState4D — same SearchResult shape as before; the engine constructs a transient Board4D internally. Drops the Board4D.from_fen(state.to_fen4()) hop chess4D-OC was previously paying per search call.

• GameState4D.is_check() / is_checkmate() / is_stalemate() — wraps Board4D.is_check() plus a short-circuit legal-moves probe for mate / stalemate. is_checkmate / is_stalemate bail on the first legal move via next(iter(...), None), so the dense-position cost is O(legal-move generation) worst case — about 2s at the dense 28-king start with the 1.7.0 native bitboard fast-path active.

• 16 new immolation tests in tests/test_gamestate4d_consumer_surface.py lock down the contract shape and at least one concrete behaviour per wishlist item. Hard gate — chess4D-OC's M11.40 PR will fail loudly if any of these regress.

1.8.1 update — canonical 4D initial position landed. chess_spectral_4d.initial_position() returns a fresh GameState4D at the Oana-Chiru §3.3 4D starting layout (896 pieces total: 448 white + 448 black, 28 kings per side). STARTING_FEN4 exposes the same layout as a FEN4 v1 string for consumers that want a literal. Slice helpers (central_slices(), white_only_slices(), black_only_slices(), empty_slices()) expose the §3.3 (z, w) classification with |C| = 4, |W_only| = 24, |B_only| = 24, |E| = 12. 24 new immolation tests including a SHA-256 hash lock on the canonical FEN4 string.

Tier-2 (ψ-driven density / current, partial-trace density matrices) is still deferred — needs the η-metric machinery from ADR-005.

What's new in v1.7 (May 2026)

The chess4D-OC visualizer wishlist release. Headline pieces:

• SearchOptions.time_budget_ms honored mid-iteration (chess_spectral_4d.engine.search). Previously the deadline was checked only between iterative-deepening iterations — a 5-second budget on the dense 28-king starting position could overrun by ~100× before depth-1 alone completed. v1.7 threads the deadline into the alpha-beta inner loop and returns the deepest-completed- so-far best move on deadline exit. New SearchResult.timed_out: bool field distinguishes deadline-exit from natural completion.

• Native bitboard fast-path (chess_spectral.spatial_4d, cs_bitboard4d shared library). Pure-C primitives for the 4096-bit Bitboard4D — popcount, bitwise AND/OR/XOR/NOT/sub, per-square set/clear/toggle/test, predicates, and the load-bearing cs_bb4_to_squares iteration helper (per-bit LSB extraction in C plus b &= b - 1 clear, all without crossing the ctypes boundary per square). Ships in the wheel under chess_spectral/_native/; loaded via ctypes at import. Pure-Python Bitboard4D continues to work as fallback (sdist install, Pyodide / micropip) — verified by a dedicated fallback-test CI job. Bitboard4D.to_squares() / .squares() route through the native helper when chess_spectral.HAS_NATIVE_BITBOARD is True; ~16× faster iteration on dense bitboards.

• Board4D.legal_moves() algorithmic refactor. The legal-move filter at the dense 28-king start position previously called _is_attacked once per own king (K=28 calls, each iterating all N opponent attackers from scratch). v1.7 iterates attackers once and tests king_bb.intersects(attack_set) per attacker, short- circuiting on the first hit — same per-op cost (O(1) bitboard test) but one call replaces K. ~12.7× faster on the representative non-in-check 264-piece position.

• Cumulative wishlist outcome. The chess4D-OC visualizer's reported legal_moves() ~250s pain point at the standard 28-king start is now ~2s on the same hardware (~125× faster) with no API changes. Time-budget-checked search now respects user budgets within ~0.5s grace regardless of position density.

• Downstream consumer flag. HAS_NATIVE_BITBOARD is exposed at the top-level chess_spectral package — consumers can do from chess_spectral import HAS_NATIVE_BITBOARD to badge "native fast-path active" or fall back cleanly when the native lib isn't present.

For the full release history see CHANGELOG.md.

What's new in v1.6 (April 2026)

The §16 ship-gate release. Headline pieces:

• Search core, tournament harness, and sweep ship-gate runner ship as CLI commands at both 2D and 4D. Per-side symmetric agent specs let white and black be configured independently in the same single-process tournament loop:

  spectral_py sweep \
      --evaluators material,spectral,qm \
      --depths 1,2,3,4 \
      --n-games-per-pair 10 \
      --time-budget-ms 5000 \
      -o sweep.json
  

• Three §16.1 evaluator families (material, spectral, qm) ship at both 2D and 4D with a uniform evaluate(position, side_to_move) -> float contract — drop in any of them as the search heuristic.

• Three independent in-house move-rule oracles — each encodes the same legality predicate ("is move m legal in position p?") through a different mathematical lens, validated head-to-head against the python-chess[4d] reference and against each other. Not because one is more correct — they all agree on the same legal set — but because each lens is a standalone artifact for studying how spatial motion can be encoded:

  1. Bitboard / attack-tables (chess_spectral.spatial_4d) — the engineering lens. Bitboard4D primitive, per-piece attack tables (knight, king, rook, bishop, queen via magic-bitboard- style ray casting), axis-typed pawn moves (Pw/Py per Oana-Chiru §3 Def 11), Board4D game state, and draw rules. Same idea as a classic chess engine, lifted to Z_8^4.
  2. Phase-space operators (chess_spectral.phase_operators, chess_spectral.phase_operators_4d) — the algebraic lens. Per-piece move generation as group actions on a phase-space representation; legality is "the candidate move is in the orbit of the piece operator, intersected with the occupation oracle." 2D ships under §11; 4D under §13.
  3. Discrete-Laplacian eigenbasis oracle (2D + 4D) — the spectral lens. The lattice's discrete Laplacian (Kron-sum of P_8 path-graph Laplacians; eigenvectors form a DCT-style basis) doubles as a structural lookup table for move legality — the same eigenbasis the spectral encoder uses to embed positions also tells you which moves are reachable. Concrete demonstration that "encode the geometry" and "encode the rules" share one foundation.

• v5 unified .spectral[z] / .spectralz4 wire format (chess_spectral.frame_v5) — single 256-byte header serves both 2D and 4D via explicit n_dimensions field. Three encoding modes selected by encoding_mode: dense (= legacy v2/v4 frame body), per-channel replacement (variable-size, ~2.84× compression on 4D stable workloads), and XOR-stream (fixed-size, 7.23× compression on 4D vs dense gzipped). See docs/WIRE_FORMAT.md for the byte-level spec covering all four shipped versions (v2/v3/v4/v5) and the reader-dispatch convention.

• CI gate: the 15-cell verify-wheels matrix is now opt-in via the wheel-check PR label (was running on every PR; saves ~150 runner-min/PR while keeping the publish-time matrix as the load-bearing wheel-correctness gate).

For the full release history see CHANGELOG.md.

Both packages share a single dist version derived from importlib.metadata; see Install below.

Install

From PyPI (recommended):

pip install chess-spectral

The base install pulls only numpy and scipy — sufficient for encoding, the 4D phase operators, the kinematic QM layer, and the full §17.1 bridge surface.

Optional extras:

# PGN ingest via chess_spectral.corpus (adds python-chess)
pip install "chess-spectral[corpus]"

# 4D phase-operator validation gates against the Oana & Chiru oracle
pip install "chess-spectral[test]"   # adds python-chess4d-oana-chiru

Package page: https://pypi.org/project/chess-spectral/

From source

Editable install from a local checkout:

pip install -e docs/chess-maths/chess-spectral/python/

From a git URL (pin a commit in production):

pip install "git+https://github.com/lemonforest/mlehaptics.git@COMMIT#subdirectory=docs/chess-maths/chess-spectral/python"

After install, two console scripts are on your $PATH:

chess-spectral --help            # 2D CLI
chess-spectral-4d --help         # 4D CLI

Both packages also expose __version__, derived dynamically from the installed dist:

>>> import chess_spectral, chess_spectral_4d
>>> chess_spectral.__version__ == chess_spectral_4d.__version__
True   # both derive from importlib.metadata.version("chess-spectral");
       # they cannot drift from each other or from the wheel.

In-place (no install)

The legacy workflow still works: every test and analysis script uses sys.path.insert to bootstrap off the python/ directory, so pytest docs/chess-maths/chess-spectral/python/tests/ runs without any install.

Quick start (2D)

>>> from chess_spectral import (
...     encode_2d, channel_energies, read_encodings, fen_to_pos,
...     ENCODING_DIM, CHANNELS,
... )

>>> pos = fen_to_pos("rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1")
>>> enc = encode_2d(pos)
>>> enc.shape
(640,)
>>> ENCODING_DIM   # don't hardcode 640; the dim count may grow (see §19.11)
640

>>> channel_energies(enc)
{'A1': 0.0, 'A2': 19.845, 'B1': 45.2825, 'B2': 45.2825,
 'E': 322.57, 'F1': 88.77, 'F2': 1851.01, 'F3': 1507.65,
 'FA': 19.92, 'FD': 0.0}

# Read a whole game that was encoded by either C or Python
>>> hdr, arr = read_encodings("game.spectralz")  # transparent gzip
>>> arr.shape
(161, 640)

Quick start (4D, 45 056-dim)

The 4D encoder runs on the Z_8^4 hypercubic lattice with B_4 hyperoctahedral symmetry adaptation (per Oana & Chiru, AppliedMath 6(3):48, 2026). Output is a 45 056-dim float32 vector partitioned into 11 channels of 4096 modes each: A1, STD4_X/Y/Z/W, FIB_SYM_1/2/3, FA_PAWN_W, FA_PAWN_Y, FD_DIAG.

>>> from chess_spectral.fen_4d import parse
>>> from chess_spectral.encoder_4d import (
...     encode_4d, channel_energies_4d, CHANNELS_4D,
... )

>>> pos = parse("4d-fen v1: K@4,0,0,0; k@4,7,7,7")
>>> v = encode_4d(pos)
>>> v.shape, v.dtype
((45056,), dtype('float32'))

>>> energies = channel_energies_4d(v)   # per-channel L2 energies
>>> sorted(energies)
['A1', 'FA_PAWN_W', 'FA_PAWN_Y', 'FD_DIAG',
 'FIB_SYM_1', 'FIB_SYM_2', 'FIB_SYM_3',
 'STD4_W', 'STD4_X', 'STD4_Y', 'STD4_Z']

The 4D game-state surface (move history, draw detection, FEN4 round-trip, promotion-piece argument) lives in chess_spectral_4d:

>>> from chess_spectral_4d import GameState4D, apply_move, MoveHistory4D
>>> from chess_spectral_4d import bridge

# Load a placement from a FEN4 string (white-to-move by default)
>>> result = bridge.load_state("4d-fen v1: K@4,0,0,0; k@4,7,7,7")
>>> state = result['state']
>>> isinstance(state, GameState4D)
True

# apply_move(state, from_sq, to_sq, *, promote_to='Q') is the v1.4 API
>>> # state2 = apply_move(state, from_sq=4, to_sq=5, promote_to='Q')

# Draw status: priority threefold > 50-move > insufficient > stalemate
>>> bridge.get_draw_status(state, has_legal_moves=True)
{'ok': True, 'status': 'none'}

For raw move-rule logic (legal-move generation, check detection) on Z_8^4, see chess_spectral.phase_operators_4d below.

Quick start (QM extension, v1.5+)

The QM extension ships in two layers, mirroring the standard physics split:

• Kinematics — chess_spectral.qm_4d. What states and operators look like: state space, observables, measurement structure, the B_4 group action. Lifts encoder output to ψ ∈ ℂ^{45056}, exposes the 11-channel decomposition as a built-in projection-valued measure (PVM), builds five Hermitian piece-reach observables (rook / bishop / queen / king / knight) on the per-channel ℂ^{4096} factor, and ships the 384-element B_4 unitary representation as a cached LUT.
• Dynamics — chess_spectral.qm_4d_dynamics. How states change: the 11 per-channel u_move_* builders for move-as-unitary transitions (discrete) plus evolve_under_h0 for Zeno-style continuous time evolution between move boundaries.

Consumers that want a Pyodide-JSON-shaped surface use the chess_spectral.qm_4d_bridge dispatch layer described in the next section.

>>> from chess_spectral.fen_4d import parse
>>> from chess_spectral.qm_4d import (
...     state_to_psi,
...     prob_channel, measure_channel_distribution,
...     channel_projector,
...     H_rook_4, H_bishop_4, H_queen_4, H_king_4, H_knight_4,
...     measure_observable_distribution,
...     b4_unitary_rep_4096, b4_unitary_rep_full,
...     expectation, is_normalized, is_hermitian, is_unitary,
... )

>>> pos = parse("4d-fen v1: K@4,0,0,0; k@4,7,7,7; R@0,0,0,0")
>>> psi = state_to_psi(pos, side_to_move=True)
>>> psi.shape, psi.dtype
((45056,), dtype('complex128'))
>>> is_normalized(psi)
True

# Born-rule channel measurement: probability mass per channel
>>> prob_channel(psi, c=0)            # A1 channel
3.3e-08
>>> probs = measure_channel_distribution(psi)   # all 11 channels
>>> abs(probs.sum() - 1.0) < 1e-10
True

# ⟨ψ|H_rook|ψ⟩ on the rook channel block
>>> # expectation(H_rook_4, psi[0:4096])    # H_piece_4 is sparse 4096x4096

# Born-rule eigenbasis distribution: |⟨φ_k|ψ⟩|² grouped by eigenvalue
>>> # eigvals, probs = measure_observable_distribution(H_rook_4, psi[0:4096])

# B_4 group action (384 elements). 4096-dim per-channel block, or the
# I_11 ⊗ U_4096(g) Kronecker extension to the full 45 056-dim space.
>>> # U = b4_unitary_rep_full(g)            # cached; sparse 45056x45056

The Hermitian piece-reach observables H_rook_4, H_bishop_4, H_queen_4, H_king_4, H_knight_4 are built on demand and cached. They are real-symmetric on ℂ^{4096} with integer / near-integer spectra (Hermiticity verified at floating residual ~5e-15; Pre-flight 2 in qm_4d.py). Pawn observables break Hermiticity under the standard inner product (directed push) — the pseudo-Hermitian η-metric construction is deferred to v1.8+ per ADR-005.

b4_unitary_rep_4096(g) and b4_unitary_rep_full(g) realize the order-384 hyperoctahedral group as sparse unitaries on ℂ^{4096} and ℂ^{45056} respectively (the latter is I_{11} ⊗ U_{4096}(g) — same B_4 action applied independently to each of the 11 channels). Both are cached per group element. measure_observable_distribution(H, ψ) diagonalizes any Hermitian observable on ℂ^{4096} and returns the Born-rule probability distribution over distinct eigenvalues.

Move-as-unitary dynamics (Phase 4, Track B) live in chess_spectral.qm_4d_dynamics. The module ships per-channel builders for all 11 channels and both non-capture and capture moves:

• u_move_a1 — A_1 channel via projector-sandwich (B1).
• u_move_std4 — STD4_X/Y/Z/W via similarity-transform; same-orbit is strict-unitary, cross-orbit returns a measurement-only marker (B3a, ADR-003 amendment).
• u_move_fa_pawn — FA_PAWN_W/Y via axis-parity-odd projector sandwich (B3b).
• u_move_fib_meas — FIB_SYM_1/2/3 via measurement-only re-encode (B3c, per the Phase 3.5 amendment to ADR-003 §3.3).
• u_move_fd_diag — FD_DIAG via rank-1 update + renormalization (B3d/e).
• evolve_under_h0 + H_FREE_4D — Zeno-style continuous evolution between move boundaries, where H_0 = -Δ is the lattice Laplacian (B2, ADR-002).

§17.1 Pyodide bridge surface (v1.5)

chess_spectral.qm_4d_bridge is the consumer-facing bridge — the 7 §17.1 QM-extension methods plus 6 §17.5 dev/debug methods — designed for Pyodide consumers (e.g., the chess4D-OC visualizer) that need Pyodide-JSON-serializable returns and Float32 ψ-amplitudes ready for Float32Array shader uploads.

>>> from chess_spectral.qm_4d_bridge import (
...     # §17.1 (QM-extension surface)
...     get_qm_state, get_qm_density, apply_move_qm, apply_move_qm_full,
...     measure_at, get_density_matrix_of, get_probability_current,
...     get_qm_expectation,
...     # §17.5 (dev / debug surface)
...     get_version, get_encoder_shape, get_fen4_state, load_fen4,
...     load_jsonl_fixture, has_legal_moves,
... )

# Round-trip: load a FEN4, get ψ as Float32 interleaved, apply a move,
# get the updated ψ.
>>> r = load_fen4("4d-fen v1: K@0,0,0,0; k@7,7,7,7; R@1,0,0,0")
>>> state = r['state']

>>> r = get_qm_state(state, side_to_move=True)
>>> r['basisDim'], r['psi'].dtype, r['psi'].shape
(45056, dtype('float32'), (90112,))   # 2 × 45 056 — real+imag interleaved
>>> abs(r['normSq'] - 1.0) < 1e-6
True

# Per-cell density: |ψ|² summed across the 11 channels per cell
>>> r = get_qm_density(state)
>>> r['density'].shape, abs(r['density'].sum() - 1.0) < 1e-6
((4096,), True)

# Apply a move and get the assembled ψ_post (Float32 interleaved).
# move format: (from_sq, to_sq) as ints OR ((x,y,z,w), (x,y,z,w))
>>> # r = apply_move_qm_full(state, move=(1, 2))
>>> # r['ok'], r['psi'].shape
>>> # (True, (90112,))

# §17.5 debug surface
>>> get_version()['version']         # e.g., '1.5.0'
>>> get_encoder_shape()['totalDim']  # 45056
>>> get_encoder_shape()['channels']  # [{'name': 'A1', 'offset': 0, 'dim': 4096}, ...]

Wire format (ComplexArray, Float32Array-friendly): every ψ return is a 1-D Float32 array of length 2 * 45056 = 90112, where psi[2k] is Re(ψ_k) and psi[2k+1] is Im(ψ_k). This matches the §17.1 contract documented in the research notebook §17.1.

apply_move_qm vs apply_move_qm_full. The low-level apply_move_qm returns a per-channel dispatch dict (mixed csr_matrix

• marker dict values) for consumers that want to reason about per- channel structure. The high-level apply_move_qm_full does the block-by-block assembly (csr_matrix → U_chan @ ψ_pre[block]; marker dict → psi_post_block splice) and returns the assembled ψ_post as Float32 interleaved. Most consumers want the _full variant.

The B5 milestone (April 2026) closed the last unshipped channels, so the bridge no longer raises for any move type — non-captures and captures both succeed via the channels' B5 capture-path branches. See qm_4d_bridge.py for per-method docstrings and qm_4d_dynamics.py for the per-channel construction details.

Deferred to v1.8+:

• get_density_matrix_of (reduced density matrix; needs partial- trace machinery on channel labels).
• get_qm_density(piece_id=...) (per-piece marginal; same blocker).

Both raise NotImplementedError with a pointer to the v1.8+ milestone. (v1.7.0 shipped the D1 / D2 native fast-path workstream — see the "What's new in v1.7" section below — and rolled the partial-trace work to a follow-up.)

CLI

The 2D CLI (chess-spectral, entry point chess_spectral.cli:main) mirrors the C spectral CLI subcommand-for-subcommand. Output is byte-identical to the C binary on the same input — the spectral csv command produces the same bytes on either side.

chess-spectral csv         game.spectralz -o game.csv
chess-spectral encode      -i game.ndjson -o game.spectralz -z
chess-spectral encode-fen  --fen "..."   -o single.spectral
chess-spectral compare     a.spectralz b.spectralz
chess-spectral query       game.spectralz --ply 30
chess-spectral heatmap     game.spectralz --ply 30 --channel A1
chess-spectral analyze     game.spectralz
chess-spectral export      game.spectralz -o game.json
chess-spectral version

The 4D CLI (chess-spectral-4d):

chess-spectral-4d tables-verify  --phase all
chess-spectral-4d encode-fen4    --fen4 "4d-fen v1: K@0,0,0,0; ..."
chess-spectral-4d encode-moves4  --moves game.ndjson4 -o game.spectralz4 -z
chess-spectral-4d corpus-gen     --games game1.ndjson4 game2.ndjson4 ...
chess-spectral-4d version

Both CLIs follow the --help discipline: every subcommand and every argument has non-empty help text. Run <cmd> --help (or <cmd> <subcommand> --help) before invoking; the immolation suite gates this in CI.

Layout

chess_spectral/                # 2D + 4D encoder math + QM extension
  __init__.py                  # __version__ via importlib.metadata
  encoder.py                   # encode_2d(pos) → np.ndarray (encode_640 = legacy alias)
  frame.py                     # v2 .spectral[z] binary I/O + transparent gzip
  csv_export.py                # dist_prev / cos_prev / energies CSV
  cli.py                       # `chess-spectral` (2D CLI)
  phase_operators/             # 2D §11 phase-space move generator (1.2.0+)

  encoder_4d.py                # encode_4d(pos4) → float32(45056,)
  frame_4d.py                  # v3/v4 .spectralz4 binary I/O (legacy reader)
  frame_v5.py                  # v5 unified wire format (2D + 4D, 3 encoding modes; default for new writes from v1.6)
  tables_4d.py                 # B_4 group, lattice tables, eigenmodes
  fen_4d.py                    # FEN4 v1 placement-literal parser + serialize
  phase_operators_4d/          # 4D §13 phase-operator move engine (1.3.0+)

  qm_4d.py                     # Track A kinematic QM front-end (1.5.0+)
  qm_4d_dynamics.py            # Track B per-channel U_move builders (1.5.0+)
  qm_4d_bridge.py              # §17.1 + §17.5 Pyodide bridge surface (1.5.0+)

chess_spectral_4d/             # 4D game-state surface (1.4.0+)
  __init__.py                  # GameState4D, Move4D, MoveHistory4D, apply_move, bridge
  move_history.py              # ply log, side-to-move, 50-move clock, repetition hash
  apply_move.py                # apply_move(state, from_sq, to_sq, *, promote_to='Q')
  bridge.py                    # load_state, get_draw_status, get_move_history,
                               #   is_insufficient_material_2d
  cli.py                       # `chess-spectral-4d` (4D CLI)

pyproject.toml                 # PEP 621 packaging metadata
tests/                         # pytest suite (see test count below)

Test count (post-v1.5): 45 895 tests collected. Breakdown: ~44 876 parametric 4D phase-operator tests (the bulk), 81-test end-to-end immolation suite (test_smoke_e2e.py, expanded from 41 for v1.5 surface coverage), 272 v1.5 QM tests across the kinematic front-end (test_qm_4d.py, test_qm_4d_z2_grading.py), the Track B B1..B5 dynamics gates (test_qm_4d_dynamics_b{1,2,3a,3b,3c,3d,5}.py), and the §17.1/§17.5 bridge surface (test_qm_4d_bridge_v15.py), plus 102 fast tests, 260 pawn-axis / phase-4d-check / phase-4d- unobstructed tests, 92 2D phase_operators tests, and 210 v1.4 game-state tests. Run via pytest docs/chess-maths/chess-spectral/python/tests/.

Phase operators (2D, §11)

chess_spectral.phase_operators ships a phase-space move generator and check detector (added in 1.2.0). The primitives compute all moves and check relationships as modular arithmetic on a single integer per square — phi(r, c) = r·67 + c·7 mod 640 — rather than geometric coordinates. They are a drop-in equivalent to python-chess's pseudo_legal_moves + is_check, validated at 100% on the reference corpus, and compose naturally with the spectral encoder's coprime phase structure.

import chess
from chess_spectral.phase_operators import (
    occupation_aware_moves_c,   # pseudo-legal dests from a square
    available_castles,          # legal castles for side-to-move
    phasecast_is_check,         # is the mover's king attacked?
    move_leaves_king_in_check,  # would this move expose our king?
)

board = chess.Board()
dests = occupation_aware_moves_c(board, "N", 0, 1, +1)
# -> frozenset({(2, 0), (2, 2)})   (a3 and c3)

phasecast_is_check(board)  # False on the starting position

Validation coverage and rationale: see PHASE_OPERATOR_SUPPLEMENT.md.

Phase operators (4D, §13)

chess_spectral.phase_operators_4d (1.3.0+) is the 4D analogue — mixed-radix tower with modulus 145451 and ladder coefficient 14 (vs the 2D framework's 8). Validated against python-chess4d-oana-chiru at 44 803 (state, origin, piece) cases for occupation-aware moves and 232 cases for check detection.

from chess_spectral.phase_operators_4d import (
    phi4,
    P_rook4, P_bishop4, P_queen4, P_king4, P_knight4,
    P_pawn4_white, P_pawn4_black,
    occupation_aware_moves_a_4d,    # phase-op candidates ∩ chess4d oracle
    phasecast_is_check_4d,
    move_leaves_king_in_check_4d,
)

Full design + experimental record: PHASE_OPERATOR_SUPPLEMENT_4D.md.

When to use what

The 2D and 4D encoders are independent build targets that share table generation discipline. Pick by what you're encoding; the QM extension sits on top of the 4D encoder.

	2D (chess_spectral)	4D (chess_spectral + chess_spectral_4d)
Encoding dim	640	45 056
Lattice	Z_8 × Z_8	Z_8^4
Symmetry group	D_4 (order 8)	B_4 hyperoctahedral (order 384)
Game rules	python-chess (fen_to_pos)	Oana & Chiru (python-chess4d-oana-chiru)
Channels	10 (A1, A2, B1, B2, E, F1, F2, F3, FA, FD)	11 (A1, STD4_X/Y/Z/W, FIB_SYM_1/2/3, FA_PAWN_W/Y, FD_DIAG)
Phase operators	phase_operators (1.2.0+)	phase_operators_4d (1.3.0+)
QM extension	not yet shipped	qm_4d + qm_4d_dynamics + qm_4d_bridge (1.5.0+)

Python vs C — same encoders on either side, byte-identical output:

	C (../src/)	Python (this package)
Throughput	µs/encode	ms/encode
REPL / notebooks	✗	✓
LLM-pasteable	binary	code
scipy.linalg exploration	✗	✓
Embeds in mobile / web (Pyodide)	✓	✓ (Pyodide)
Exact numerical reference	tables baked at build	rebuilt from primitives

Develop new channels in Python first (faster iteration, scipy.linalg at hand, no rebuild loop). Once the math is frozen, port to C and verify parity via the test suite — the critical test is test_csv_matches_c_byte_for_byte (2D) / test_e2e_spectralz4_parity.py (4D), which assert the C-produced encoded bytes equal the Python-produced bytes.

See also

• Cross-disciplinary applications — research notebook §15 (chess_spectral_research_notebook.md) for the framing of chess-spectral as a H(4, 8) Hamming-scheme toolkit with B_4-equivariant frozen featurizer and Born-rule loss hooks.
• §17 bridge contracts — same notebook, §17.1 / §17.5 for the consumer-facing method specs that this package implements.
• 4D notebook — chess_spectral_4d_notebook.md for the 4D-specific research record (encoder injectivity, B_4 spectral identity, qm_4d pre-flight findings).
• Track B ADRs — docs/adr/qm_4d/ for the design record of the v1.5 QM extension:
  • ADR-001 phase convention for unitary moves
  • ADR-002 time-evolution semantics (continuous H_0 between move boundaries; evolve_under_h0)
  • ADR-003 per-channel move transformation (+ Phase 3.5 orbit-restriction amendment for cross-orbit STD4 / FIB_SYM measurement-only re-encode)
  • ADR-004 Z_2 superselection structure (side-to-move sign multiplier, resolves the 8-collision encoder hash issue)
  • ADR-005 pawn pseudo-Hermitian η-metric (deferred to v1.8+)
  • PHASE_3_5_PROBE_RESULTS.md — empirical probe record that drove the ADR-003 amendment.
• Pawn-axis split (v1.1.1) — Oana & Chiru Definition 11; the encoder splits the pawn antisymmetric channel into W-axis and Y-axis sub-channels (FA_PAWN_W, FA_PAWN_Y) and grew from 40 960-dim to 45 056-dim. See encoder_4d.py header for the rationale.