Deterministic prime-distribution research framework

MAYAN ALFA

MAYAN ALFA is an independent deterministic research framework focused on prime-distribution analysis, segmented numerical filtration, and large-scale computational observation.

The current implementation explores correctness-first numerical processing, reproducible validation workflows, and structured interval mapping across large arithmetic ranges while preserving controlled research boundaries.

How we measure Release rules

Measurement Methodology

How MAYAN ALFA protects correctness, evidence, and interpretation

Correctness-first runtime

Full workflow runtime represents the complete execution path of the current research implementation. It preserves traceability, reproducibility, and validation value before optimization becomes the primary objective.

Throughput signal

Clean-binary timing is treated as a directional throughput indicator under reduced instrumentation. It remains separate from full workflow runtime and is never presented as a standalone performance claim.

Evidence boundary

Every reported result belongs to a defined measurement context: limit, step, execution mode, archive tier, and validation layer remain attached to the number.

Interpretation discipline

MAYAN ALFA reports measurements as bounded observations inside a deterministic research framework, not as isolated speed slogans or unsupported mathematical claims.

Deterministic research layer

MAYAN ALFA is built as a correctness-first numerical framework for structured prime-distribution observation and reproducible validation.

Performance layer in progress

Runtime and clean-binary timing remain separated until deeper instrumentation makes performance interpretation fully auditable.

Protected release boundary

Public materials expose bounded evidence and methodology while controlled archive layers preserve extended results and internal research continuity.

Evidence board

Canonical Measurement Evidence

Public evidence layer 10M - 10B validated presentation range
Controlled archive 100B - 500B preserved extended evidence
Research workflow active correctness-first execution
Performance reading bounded context required before claims

Measurement families

Evidence map
Limit Layer MAYAN evidence Reference check
10M public measured measured
100M public measured measured
1B public measured measured
10B public boundary measured measured
100B controlled archive evidence archive evidence
200B controlled archive evidence archive evidence
500B controlled reference workflow reference workflow reference

Measurement reading

Public interpretation rule
MAYAN ALFA separates correctness-first workflow evidence, clean-binary timing, and controlled archive material so each result is interpreted within its proper measurement context.

The framework does not reduce the project to a single speed number. Each result is tied to its limit, step, execution mode, validation layer, and publication boundary.

Evidence integrity

Public evidence archive, DOI preservation, and release boundary

GitHub public release package

sha256:264b797b04e01695772f090881edb1cf2bab30aa73ab74d1575c8d4985926e64

Paper workpack package

sha256:b301c0f03e8a31349cf2fa06ce0382dc7509929647f24cdb77990425e0600a03

Public QA package

sha256:3695cd2ffc046dcd32ac8e2fe3e68df84972002c6e60804e06f86d5d6cbfa178

Public release package

sha256:18518792898ecceb13f2935660e4756efe41169d48c08b3f7c61774709e77889

These checksums preserve package identity for public review, release verification, and long-term evidence integrity.

Release boundary

The public archive is derived from the validated MAYAN_ALFA release boundary and preserves only material intended for public inspection.

Internal development paths, local machine identifiers, protected orchestration logic, and private infrastructure references are intentionally excluded.

Zenodo DOI preservation

Version DOI — v0.7:
https://doi.org/10.5281/zenodo.20360174

Concept DOI — all versions:
https://doi.org/10.5281/zenodo.20360173

DOI: 10.5281/zenodo.20360174
Public release boundary sanitized for GitHub and Zenodo preservation.

Archive architecture

The archive separates public presentation, release hosting, DOI preservation, and validation depth into distinct evidence layers.

Public anchors

  • PUBLIC Open presentation layer for public-facing framework material.
  • GitHub Versioned repository for public code, release packages, and outputs.
  • Zenodo DOI-based preservation layer for versioned public releases.

Evidence tiers

  • BRONZE Initial validation layer for early evidence staging.
  • SILVER Validated public evidence layer up to 10B.
  • GOLD Controlled evidence layer from 100B to 500B.
  • PLATINUM Reserved future scaling layer for 1T+ to 10T+ observations.

Framework memory

Research guide archive

Release discipline

Public release rules

01

Preserve the audit trail

Historical runs, release logs, measurement outputs, and validation evidence remain part of the traceable archival record.

02

Bound the public layer

The public presentation range remains bounded at 10M–10B, while 100B+ observations remain preserved as controlled archive evidence.

03

Protect the core framework

Public releases preserve methodological transparency and evidence continuity while protected internal framework layers remain outside distribution.

04

Stage optimization later

Deeper instrumentation, throughput tuning, and optimization analysis follow only after the correctness-first phase reaches stable validation maturity.