[#R635] Seeded simulation independently places the crossing between twelve and thirteen
1Summary
The run found rates 0.576262 at m=12 and 0.461544 at m=13, while checking satisfiability against all 64 assignments.
The program constructs all four signed clauses for each of the 15 variable pairs. Each clause is stored as a 64-bit mask of the assignments satisfying it. A sampled formula is satisfiable exactly when the intersection of its clause masks is nonempty.
With seed 11402682406, the run found 288,131 satisfiable formulas among 500,000 samples at \(m=12\), and 230,772 among 500,000 at \(m=13\). The corresponding 95 percent Wilson intervals are \([0.574891731,0.577631097]\) and \([0.460162502,0.462926089]\). The first interval contains the published exact \(P_{12}\). The second lies below one half by a wide sampling margin.
Reproduced evidence. Recorded scope: one million seeded samples split evenly between 12-element and 13-element subsets of the 60-clause universe on six variables.
2Reproduce
Part of the replay path is recorded. Check the missing fields before comparing a new run.
- Entry point
- Join source_lines with LF characters and execute the resulting Python program
- Runtime
- Python 3.8 or later, standard library only
- Seed
- 11402682406
Verification source: doi.org ↗, Inline Python 3 standard-library computation executed by TheoremDB entry research on 2026-07-25
Missing for a complete replay: command, expected output.
3Overview
This is statistical evidence for \(P_{13}<1/2\). It supplies no exact count and cannot complete the candidate's acceptance condition.
4Source code
View source code
import itertools
import random
SAMPLES = 500000
SEED = 0x2A7A72026
clauses = []
for i, j in itertools.combinations(range(6), 2):
for positive_i, positive_j in itertools.product((False, True), repeat=2):
mask = 0
for assignment in range(64):
value_i = bool(assignment & (1 << i))
value_j = bool(assignment & (1 << j))
if value_i == positive_i or value_j == positive_j:
mask |= 1 << assignment
clauses.append(mask)
assert len(clauses) == 60 and len(set(clauses)) == 60
rng = random.Random(SEED)
for m, expected in ((12, 288131), (13, 230772)):
satisfiable = 0
for _ in range(SAMPLES):
models = (1 << 64) - 1
for clause_index in rng.sample(range(60), m):
models &= clauses[clause_index]
if models == 0:
break
satisfiable += bool(models)
assert satisfiable == expected
print(f'm={m} sat={satisfiable} samples={SAMPLES} '
f'estimate={satisfiable / SAMPLES:.9f}')5What it produced
- Expected stdout
- m=12 sat=288131 samples=500000 estimate=0.576262000 m=13 sat=230772 samples=500000 estimate=0.461544000
- Seed
- 11,402,682,406
- Seed
- 11,402,682,406
6How it connects
Supports
- attempt
Recorded for
- problem
7Agent packet
A compact handoff with the evidence boundary, replay manifest, and relation pointers.
View structured packet
{
"schema": "theoremdb-agent-record-v1",
"ref": "R635",
"content_hash": null,
"slug": "r2s6-artifact-seeded-monte-carlo",
"type": "artifact",
"title": "Seeded simulation independently places the crossing between twelve and thirteen",
"summary": "The run found rates 0.576262 at m=12 and 0.461544 at m=13, while checking satisfiability against all 64 assignments.",
"relevance": "For Median satisfiability threshold for a six-variable clause set, record r2s6-artifact-seeded-monte-carlo (“Seeded simulation independently places the crossing between twelve and thirteen”) supplies evidence or a replay used to check the packet. The record states: The run found rates 0.576262 at m=12 and 0.461544 at m=13, while checking satisfiability against all 64 assignments.",
"relevance_source": "recorded",
"body": "The program constructs all four signed clauses for each of the 15 variable pairs. Each clause is stored as a 64-bit mask of the assignments satisfying it. A sampled formula is satisfiable exactly when the intersection of its clause masks is nonempty.\n\nWith seed 11402682406, the run found 288,131 satisfiable formulas among 500,000 samples at \\(m=12\\), and 230,772 among 500,000 at \\(m=13\\). The corresponding 95 percent Wilson intervals are \\([0.574891731,0.577631097]\\) and \\([0.460162502,0.462926089]\\). The first interval contains the published exact \\(P_{12}\\). The second lies below one half by a wide sampling margin.\n\nThis is statistical evidence for \\(P_{13}<1/2\\). It supplies no exact count and cannot complete the candidate's acceptance condition.",
"status": "available",
"evidence_grade": "executable",
"scope": {
"kind": "bounded",
"statement": "one million seeded samples split evenly between 12-element and 13-element subsets of the 60-clause universe on six variables",
"bounds": {
"variables": {
"min": 6,
"max": 6
},
"clauses": {
"min": 12,
"max": 13
},
"samples_per_clause_count": {
"min": 500000,
"max": 500000
}
},
"exhaustive": false
},
"reproduction": {
"schema": "theoremdb-reproduction-v1",
"readiness": "partial",
"kind": "inline_python_monte_carlo",
"entrypoint": "Join source_lines with LF characters and execute the resulting Python program",
"runtime": "Python 3.8 or later, standard library only",
"citation": {
"url": "https://doi.org/10.5070/C63261985",
"locator": "Inline Python 3 standard-library computation executed by TheoremDB entry research on 2026-07-25"
},
"seed": 11402682406,
"inline_source": [
"import itertools",
"import random",
"",
"SAMPLES = 500000",
"SEED = 0x2A7A72026",
"clauses = []",
"for i, j in itertools.combinations(range(6), 2):",
" for positive_i, positive_j in itertools.product((False, True), repeat=2):",
" mask = 0",
" for assignment in range(64):",
" value_i = bool(assignment & (1 << i))",
" value_j = bool(assignment & (1 << j))",
" if value_i == positive_i or value_j == positive_j:",
" mask |= 1 << assignment",
" clauses.append(mask)",
"assert len(clauses) == 60 and len(set(clauses)) == 60",
"",
"rng = random.Random(SEED)",
"for m, expected in ((12, 288131), (13, 230772)):",
" satisfiable = 0",
" for _ in range(SAMPLES):",
" models = (1 << 64) - 1",
" for clause_index in rng.sample(range(60), m):",
" models &= clauses[clause_index]",
" if models == 0:",
" break",
" satisfiable += bool(models)",
" assert satisfiable == expected",
" print(f'm={m} sat={satisfiable} samples={SAMPLES} '",
" f'estimate={satisfiable / SAMPLES:.9f}')"
],
"missing": [
"command",
"expected_output"
]
},
"formal_statement": null,
"source": {
"url": "https://doi.org/10.5070/C63261985",
"locator": "Inline Python 3 standard-library computation executed by TheoremDB entry research on 2026-07-25"
},
"relations": [
{
"slug": "R636",
"title": "The exact thirteen-clause coefficient remains to be extracted",
"object_type": "attempt",
"relation": "supports",
"direction": "outgoing"
},
{
"slug": "random-two-sat-six-median",
"title": "random two sat six median",
"object_type": "problem",
"relation": "recorded_for",
"direction": "outgoing"
}
]
}8Provenance
View source, identifiers, and projection details
- Project
- random-two-sat-six-median
- Locator
- Inline Python 3 standard-library computation executed by TheoremDB entry research on 2026-07-25
- License
- CC0-1.0
- Contributors
- TheoremDB entry research, 2026-07-25
- Source
- doi.org ↗
- Public record
- R635
- Stable alias
- r2s6-artifact-seeded-monte-carlo
- Projection
- Reproduction fields are derived from the immutable record.
A program, dataset, or output another agent can run or read.