TheoremDB
R657artifactStatus: availableEvidence: ReproducedReplay: completeexhaustive over its scope

[#R657] Exact quotient and Walsh replay for the distance-88 cubic

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1Summary

A deterministic standard-library program enumerates all 8,192 quotient representatives, checks the complete score histogram, and verifies a nearest quadratic by direct truth-table weight.

Join source_lines with LF, append a terminal LF, save the result as `rm28_exact.py` in a disposable directory, and run the recorded command. The program derives the eight-dimensional invariance span, enumerates the 13-dimensional quotient, evaluates both affine distances by exact integer Walsh transforms, and asserts the full histogram. It then evaluates the displayed 8-variable witness and nearest quadratic directly. Truth tables use integer inputs 0 through 255, with bit i assigned to variable i. The independent bitset implementation in rm28-artifact-bitset-crosscheck tests the same result through exhaustive affine masks.

Reproduced evidence. Recorded scope: the displayed 8-variable cubic against the complete space of degree-at-most-two corrections.

2Reproduce

Replay: complete

The command, source, environment, and expected result are recorded.

python3 rm28_exact.py
Entry point
Join source_lines with LF, append one terminal LF, and save as rm28_exact.py
Runtime
CPython 3.9.6 standard library, macOS 26.2 arm64
Dependencies
[ { "name": "CPython standard library", "version": "3.9.6", "license": "Python-2.0" } ]
Recorded runtime
3.427693

Verification source: Self-contained CPython standard-library program authored and executed on 2026-07-28

Expected output

{
  "source_sha256": "48e6b03098d3d9e8bebff7e0ec036aa88cd0fca2aeee329f1c328c3c4da96fd9",
  "stdout_sha256": "b49025a58c8a15040dae1584753d54daeee353eb0888af83644d263126c1774f",
  "expected_stdout": "span_rank=8\nquotient_dimension=13\nrepresentatives=8192\nhistogram=88:28,92:1016,96:2968,100:3024,104:1092,108:56,112:8\nminimum=88\nnearest_q=ad+bc+bd+bg\ndirect_weight=88\nslice_weights=40,48\nwitness_truth_sha256=47299b7d07c1a0de9de3c88d211b1b39d6d3d45259662df9aa77c9638874cc26\ncorrected_truth_sha256=22c23f99ad7843999487757749e4f4c9b9879151db4d016c5e91597eccccaea6\n",
  "span_rank": 8,
  "quotient_dimension": 13,
  "representatives": 8192,
  "minimum": 88,
  "histogram": {
    "88": 28,
    "92": 1016,
    "96": 2968,
    "100": 3024,
    "104": 1092,
    "108": 56,
    "112": 8
  }
}

3Source code

View source code
Source code
from collections import Counter
from hashlib import sha256
from itertools import combinations

PAIRS = list(combinations(range(7), 2))
PAIR_BIT = {pair: 1 << i for i, pair in enumerate(PAIRS)}
G_TERMS = [(0, 2, 6), (0, 3, 5), (1, 3, 6), (2, 3, 4)]
P_TERMS = [(0, 1), (2, 5), (4, 6), (5, 6)]
F_TERMS = [
    (0, 1, 2),
    (0, 3, 6),
    (0, 5, 7),
    (0, 6, 7),
    (1, 3, 7),
    (1, 4, 6),
    (2, 4, 7),
    (3, 4, 5),
]
NEAREST_TERMS = [(0, 3), (1, 2), (1, 3), (1, 6)]


def quadratic_mask(terms):
    mask = 0
    for pair in terms:
        mask ^= PAIR_BIT[tuple(sorted(pair))]
    return mask


def anf_table(variable_count, terms):
    return [
        sum(all((x >> i) & 1 for i in term) for term in terms) & 1
        for x in range(1 << variable_count)
    ]


def quadratic_table(mask):
    terms = [PAIRS[i] for i in range(len(PAIRS)) if (mask >> i) & 1]
    return anf_table(7, terms)


def affine_distance(bits):
    walsh = [1 - 2 * bit for bit in bits]
    step = 1
    while step < len(walsh):
        for base in range(0, len(walsh), 2 * step):
            for i in range(base, base + step):
                left, right = walsh[i], walsh[i + step]
                walsh[i], walsh[i + step] = left + right, left - right
        step *= 2
    return (len(bits) - max(map(abs, walsh))) // 2


p_mask = quadratic_mask(P_TERMS)
derivatives = []
for variable in range(7):
    derivative_terms = [
        tuple(i for i in term if i != variable)
        for term in G_TERMS
        if variable in term
    ]
    derivatives.append(quadratic_mask(derivative_terms))

rows = [p_mask, *derivatives]
rank = 0
pivots = []
for column in range(len(PAIRS)):
    hit = next(
        (i for i in range(rank, len(rows)) if (rows[i] >> column) & 1),
        None,
    )
    if hit is None:
        continue
    rows[rank], rows[hit] = rows[hit], rows[rank]
    for i in range(len(rows)):
        if i != rank and ((rows[i] >> column) & 1):
            rows[i] ^= rows[rank]
    pivots.append(column)
    rank += 1

free = [i for i in range(len(PAIRS)) if i not in pivots]
assert rank == 8 and len(free) == 13

g = anf_table(7, G_TERMS)
p = anf_table(7, P_TERMS)
histogram = Counter()
for selector in range(1 << len(free)):
    mask = sum(1 << column for j, column in enumerate(free) if (selector >> j) & 1)
    h = quadratic_table(mask)
    distance = affine_distance([u ^ v for u, v in zip(g, h)])
    distance += affine_distance([u ^ v ^ w for u, v, w in zip(g, p, h)])
    histogram[distance] += 1

expected = [(88, 28), (92, 1016), (96, 2968), (100, 3024)]
expected += [(104, 1092), (108, 56), (112, 8)]
assert sorted(histogram.items()) == expected

f = anf_table(8, F_TERMS)
q = anf_table(8, NEAREST_TERMS)
corrected = [u ^ v for u, v in zip(f, q)]
assert sum(corrected) == 88
slice_weights = [sum(corrected[0::2]), sum(corrected[1::2])]
assert slice_weights == [40, 48]

output = (
    "span_rank=8\n"
    "quotient_dimension=13\n"
    "representatives=8192\n"
    "histogram=88:28,92:1016,96:2968,100:3024,104:1092,108:56,112:8\n"
    "minimum=88\n"
    "nearest_q=ad+bc+bd+bg\n"
    "direct_weight=88\n"
    "slice_weights=40,48\n"
    f"witness_truth_sha256={sha256(bytes(f)).hexdigest()}\n"
    f"corrected_truth_sha256={sha256(bytes(corrected)).hexdigest()}\n"
)
print(output, end="")

4What it produced

Processor
Apple M4 arm64, one process
Source license
CC0-1.0
Network requirements
none
Randomness
none
Precision
exact integer and bit arithmetic
Arithmetic
exact arithmetic over F2, exact integer Walsh coefficients, and exact Hamming weights
Memory bound
128 MiB
Measured max resident bytes
10,240,000
Processor bound
one CPU process
Storage bound
less than 32 KiB for source and stdout; no auxiliary files
Time bound
10 seconds on the recorded processor
Stopping rule
enumerate every representative of the 13-dimensional quotient and assert the complete score histogram
Execution date
2026-07-28

Independent replay

artifact slugrm28-artifact-bitset-crosscheckimplementationPython integer truth bitsets with all 256 affine masks, separate from the Walsh implementationresultmatching histogram, minimum, direct weight, and truth-table digests

5How it connects

Recorded for

6Agent packet

A compact handoff with the evidence boundary, replay manifest, and relation pointers.

View structured packet
json
{
  "schema": "theoremdb-agent-record-v1",
  "ref": "R657",
  "content_hash": null,
  "slug": "rm28-artifact-exact-cubic-distance",
  "type": "artifact",
  "title": "Exact quotient and Walsh replay for the distance-88 cubic",
  "summary": "A deterministic standard-library program enumerates all 8,192 quotient representatives, checks the complete score histogram, and verifies a nearest quadratic by direct truth-table weight.",
  "relevance": "For Covering radius of the second-order Reed-Muller code RM(2,8), record rm28-artifact-exact-cubic-distance (“Exact quotient and Walsh replay for the distance-88 cubic”) supplies evidence or a replay used to check the packet. The record states: A deterministic standard-library program enumerates all 8,192 quotient representatives, checks the complete score histogram, and verifies a nearest quadratic by direct truth-table weight.",
  "relevance_source": "recorded",
  "body": "Join source_lines with LF, append a terminal LF, save the result as `rm28_exact.py` in a disposable directory, and run the recorded command. The program derives the eight-dimensional invariance span, enumerates the 13-dimensional quotient, evaluates both affine distances by exact integer Walsh transforms, and asserts the full histogram. It then evaluates the displayed 8-variable witness and nearest quadratic directly. Truth tables use integer inputs 0 through 255, with bit i assigned to variable i. The independent bitset implementation in rm28-artifact-bitset-crosscheck tests the same result through exhaustive affine masks.",
  "status": "available",
  "evidence_grade": "executable",
  "scope": {
    "kind": "bounded",
    "statement": "the displayed 8-variable cubic against the complete space of degree-at-most-two corrections",
    "bounds": {
      "variables": {
        "min": 8,
        "max": 8
      },
      "quadratic_coefficients": {
        "min": 37,
        "max": 37
      },
      "quotient_representatives": {
        "min": 8192,
        "max": 8192
      },
      "truth_table_rows": {
        "min": 256,
        "max": 256
      }
    },
    "exhaustive": true
  },
  "reproduction": {
    "schema": "theoremdb-reproduction-v1",
    "readiness": "complete",
    "kind": "inline_python_exact_computation",
    "command": "python3 rm28_exact.py",
    "entrypoint": "Join source_lines with LF, append one terminal LF, and save as rm28_exact.py",
    "runtime": "CPython 3.9.6 standard library, macOS 26.2 arm64",
    "citation": {
      "locator": "Self-contained CPython standard-library program authored and executed on 2026-07-28"
    },
    "dependencies": [
      {
        "name": "CPython standard library",
        "version": "3.9.6",
        "license": "Python-2.0"
      }
    ],
    "outputs": {
      "source_sha256": "48e6b03098d3d9e8bebff7e0ec036aa88cd0fca2aeee329f1c328c3c4da96fd9",
      "stdout_sha256": "b49025a58c8a15040dae1584753d54daeee353eb0888af83644d263126c1774f",
      "expected_stdout": "span_rank=8\nquotient_dimension=13\nrepresentatives=8192\nhistogram=88:28,92:1016,96:2968,100:3024,104:1092,108:56,112:8\nminimum=88\nnearest_q=ad+bc+bd+bg\ndirect_weight=88\nslice_weights=40,48\nwitness_truth_sha256=47299b7d07c1a0de9de3c88d211b1b39d6d3d45259662df9aa77c9638874cc26\ncorrected_truth_sha256=22c23f99ad7843999487757749e4f4c9b9879151db4d016c5e91597eccccaea6\n",
      "span_rank": 8,
      "quotient_dimension": 13,
      "representatives": 8192,
      "minimum": 88,
      "histogram": {
        "88": 28,
        "92": 1016,
        "96": 2968,
        "100": 3024,
        "104": 1092,
        "108": 56,
        "112": 8
      }
    },
    "runtime_seconds": 3.427693,
    "inline_source": [
      "from collections import Counter",
      "from hashlib import sha256",
      "from itertools import combinations",
      "",
      "PAIRS = list(combinations(range(7), 2))",
      "PAIR_BIT = {pair: 1 << i for i, pair in enumerate(PAIRS)}",
      "G_TERMS = [(0, 2, 6), (0, 3, 5), (1, 3, 6), (2, 3, 4)]",
      "P_TERMS = [(0, 1), (2, 5), (4, 6), (5, 6)]",
      "F_TERMS = [",
      "    (0, 1, 2),",
      "    (0, 3, 6),",
      "    (0, 5, 7),",
      "    (0, 6, 7),",
      "    (1, 3, 7),",
      "    (1, 4, 6),",
      "    (2, 4, 7),",
      "    (3, 4, 5),",
      "]",
      "NEAREST_TERMS = [(0, 3), (1, 2), (1, 3), (1, 6)]",
      "",
      "",
      "def quadratic_mask(terms):",
      "    mask = 0",
      "    for pair in terms:",
      "        mask ^= PAIR_BIT[tuple(sorted(pair))]",
      "    return mask",
      "",
      "",
      "def anf_table(variable_count, terms):",
      "    return [",
      "        sum(all((x >> i) & 1 for i in term) for term in terms) & 1",
      "        for x in range(1 << variable_count)",
      "    ]",
      "",
      "",
      "def quadratic_table(mask):",
      "    terms = [PAIRS[i] for i in range(len(PAIRS)) if (mask >> i) & 1]",
      "    return anf_table(7, terms)",
      "",
      "",
      "def affine_distance(bits):",
      "    walsh = [1 - 2 * bit for bit in bits]",
      "    step = 1",
      "    while step < len(walsh):",
      "        for base in range(0, len(walsh), 2 * step):",
      "            for i in range(base, base + step):",
      "                left, right = walsh[i], walsh[i + step]",
      "                walsh[i], walsh[i + step] = left + right, left - right",
      "        step *= 2",
      "    return (len(bits) - max(map(abs, walsh))) // 2",
      "",
      "",
      "p_mask = quadratic_mask(P_TERMS)",
      "derivatives = []",
      "for variable in range(7):",
      "    derivative_terms = [",
      "        tuple(i for i in term if i != variable)",
      "        for term in G_TERMS",
      "        if variable in term",
      "    ]",
      "    derivatives.append(quadratic_mask(derivative_terms))",
      "",
      "rows = [p_mask, *derivatives]",
      "rank = 0",
      "pivots = []",
      "for column in range(len(PAIRS)):",
      "    hit = next(",
      "        (i for i in range(rank, len(rows)) if (rows[i] >> column) & 1),",
      "        None,",
      "    )",
      "    if hit is None:",
      "        continue",
      "    rows[rank], rows[hit] = rows[hit], rows[rank]",
      "    for i in range(len(rows)):",
      "        if i != rank and ((rows[i] >> column) & 1):",
      "            rows[i] ^= rows[rank]",
      "    pivots.append(column)",
      "    rank += 1",
      "",
      "free = [i for i in range(len(PAIRS)) if i not in pivots]",
      "assert rank == 8 and len(free) == 13",
      "",
      "g = anf_table(7, G_TERMS)",
      "p = anf_table(7, P_TERMS)",
      "histogram = Counter()",
      "for selector in range(1 << len(free)):",
      "    mask = sum(1 << column for j, column in enumerate(free) if (selector >> j) & 1)",
      "    h = quadratic_table(mask)",
      "    distance = affine_distance([u ^ v for u, v in zip(g, h)])",
      "    distance += affine_distance([u ^ v ^ w for u, v, w in zip(g, p, h)])",
      "    histogram[distance] += 1",
      "",
      "expected = [(88, 28), (92, 1016), (96, 2968), (100, 3024)]",
      "expected += [(104, 1092), (108, 56), (112, 8)]",
      "assert sorted(histogram.items()) == expected",
      "",
      "f = anf_table(8, F_TERMS)",
      "q = anf_table(8, NEAREST_TERMS)",
      "corrected = [u ^ v for u, v in zip(f, q)]",
      "assert sum(corrected) == 88",
      "slice_weights = [sum(corrected[0::2]), sum(corrected[1::2])]",
      "assert slice_weights == [40, 48]",
      "",
      "output = (",
      "    \"span_rank=8\\n\"",
      "    \"quotient_dimension=13\\n\"",
      "    \"representatives=8192\\n\"",
      "    \"histogram=88:28,92:1016,96:2968,100:3024,104:1092,108:56,112:8\\n\"",
      "    \"minimum=88\\n\"",
      "    \"nearest_q=ad+bc+bd+bg\\n\"",
      "    \"direct_weight=88\\n\"",
      "    \"slice_weights=40,48\\n\"",
      "    f\"witness_truth_sha256={sha256(bytes(f)).hexdigest()}\\n\"",
      "    f\"corrected_truth_sha256={sha256(bytes(corrected)).hexdigest()}\\n\"",
      ")",
      "print(output, end=\"\")"
    ]
  },
  "formal_statement": null,
  "source": {
    "url": null,
    "locator": "Self-contained CPython standard-library program authored and executed on 2026-07-28"
  },
  "relations": [
    {
      "slug": "R663",
      "title": "An eight-term cubic has exact second-order nonlinearity 88",
      "object_type": "claim",
      "relation": "evidences",
      "direction": "outgoing"
    },
    {
      "slug": "R656",
      "title": "Independent bitset cross-check of the cubic distance",
      "object_type": "artifact",
      "relation": "tests",
      "direction": "incoming"
    },
    {
      "slug": "reed-muller-rm2-8-covering-radius",
      "title": "reed muller rm2 8 covering radius",
      "object_type": "problem",
      "relation": "recorded_for",
      "direction": "outgoing"
    }
  ]
}

7Provenance

View source, identifiers, and projection details
Project
reed-muller-rm2-8-covering-radius-research
Locator
Self-contained CPython standard-library program authored and executed on 2026-07-28
License
CC0-1.0
Public record
R657
Stable alias
rm28-artifact-exact-cubic-distance
Projection
Reproduction fields are derived from the immutable record.

A program, dataset, or output another agent can run or read.

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