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.
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.
Measurement Methodology
How MAYAN ALFA protects correctness, evidence, and interpretation
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
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
BRONZE
archive evidence
archive evidence
200B
SILVER
archive evidence
archive evidence
500B
GOLD
workflow reference
workflow reference
Measurement reading
Public interpretation ruleMAYAN 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 controlled archive continuity
Public release package identity
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 continuity.
Release boundary and DOI preservation
The public archive is derived from the validated MAYAN ALFA release boundary and preserves only material intended for public inspection inside the `10M–10B` public window.
Internal development paths, local machine identifiers, protected orchestration logic, and non-public archive references remain 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
Public release boundary sanitized for GitHub and Zenodo preservation.
Archive architecture
The archive separates public presentation, release hosting, DOI preservation, and controlled validation depth into visibly distinct evidence layers.
Public anchors
- PUBLIC Open presentation layer for framework explanation, benchmark interpretation, and public-safe evidence summaries.
- GitHub Versioned repository for public code, release packages, benchmark outputs, and reproducible documentation.
- Zenodo DOI-based preservation layer for versioned public releases and citation continuity.
Controlled archive tiers
- BRONZE / 10M–100B Research toolkit archive layer extending beyond the public range while preserving controlled access boundaries.
- SILVER / 10M–200B Publication archive layer for deeper controlled review, broader continuity, and structured archive comparison.
- GOLD / 10M–500B Controlled validation archive with the selected `100B–500B` extension held outside the public release window.
- Future ultra-scale branch Reserved for later ultra-scale observations beyond the current BRONZE / SILVER / GOLD access structure.
Framework memory
Research guide archive
Foundation
Vision, Identity, and Release Foundation
The guide layer preserves long-term memory for framework doctrine, public/private separation, release logic, archive reasoning, and future publication discipline.
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 the controlled BRONZE / SILVER / GOLD archive tiers preserve the broader 10M–100B, 10M–200B, and 10M–500B continuity model.
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.
