Research Blog

Research v2.0

Version 2.0 · 2026-07-30 · Governance 2.0 public evidence surface

Governance 2.0 Overview

This page is part of the starnum public Governance 2.0 surface and uses the same evidence layer as the system card, data governance, transparency report, use policy, and security policy.

Governance Summary

This page documents the research operating model behind cultural interpretation, chart methodology, content quality, multilingual translation quality, and AI-assisted governance workflows.

Scope

Astrology-method notes, true-solar-time assumptions, knowledge-base and translation-memory lineage, AWR/TM quality gates, benchmark references, and public transparency artifacts.

Implementation Status

Version 2.0 turns the research page into a maintained evidence hub: claims point to machine-readable artifacts, while systems detect and agents decide whether content needs repair.

Scope:Zi Wei Dou Shu school comparisons · Numerology system analysis · AI-assisted astrology research methodology · Knowledge-base construction
Editorial stance:We use the Lu Binzhao school as the analytical baseline and compare other schools objectively — without ranking any school above another.
Frequency:One post per month, scheduled via scripts/publish-research.js · RSS

Four Transformations in the Lu Binzhao School: Three Interpretations of the Wu, Geng, and Ren Stems

2026-07-30 · Four Transformations School Comparison Lu Binzhao

One of the most contested points in Zi Wei Dou Shu: on the Wu stem, does Youbi transform into Ke or does Tianji? On Geng, is it Taiyin or Tiantong? On Ren, is it Zuofu Lu or Tianliang Lu? The currently published research note is available only in Traditional Chinese.

Read the original research note (Traditional Chinese)

GraphRAG Applied to an Astrology Knowledge Base: A Semantic Graph From Stars to Palaces

Coming soon · Technical Research GraphRAG Knowledge Base

How we converted our 2,758 files and 1538K lines of astrology knowledge into a Neo4j graph (343 nodes / 679 relationships) and fused it with Qdrant vector search to build a three-layer evidence system for chart analysis.

117 Hard Rules for Astrology Logic: Boundary Definitions for Impossible Sihua Events

Coming soon · Quality System Astrology Logic Automated Validation

The biggest challenge in astrology AI is not generation capability — it is stopping the model from stating astrologically impossible events. This post explains how we define 117 hard rules (41 Four Transformations rules + 49 chart-pattern rules + 17 core-metric rules) with real examples.

Arrow Lines in the Numerology 3×3 Grid: Taiwanese vs. Pythagorean Traditions

Coming soon · Numerology 3×3 Grid System Comparison

The "3×3 grid numerology" popular in Taiwan and the Western Pythagorean system define Arrow Lines quite differently. This article lays out both calculation methods and explains which standard we chose — and why.

External standards and primary sources

These primary sources inform this page. They are benchmarks, not third-party endorsements of this site.

Current Machine Audit Snapshot

This block uses only traceable local audit data. No unsupported metrics or model claims are added.

2026-07-30

Maintained

17/17

LLM loops

180/180

Governance pages

JSON-LD errors

32,724

KB chunks (HEALTHY)

789,031

TM entries; verified 34,781

7,976/7,976

AI answer-ready; failures 0

critical

Status page: 5 critical, 0 warnings

data/state-machine/i18n-parity.json: 8,036 parent URLs, 7,976 articles.
data/kb-machine-audit.json: 3,238 source files, 0 missing coverage, 0 orphan chunks.
data/discovery-surface-audit.json: 0 errors, 0 warnings.
data/sla-report.json: critical / 5 critical, 0 warnings.

Content Maintenance And Update Decision

This block makes governance-page content machine-checkable: every page must disclose its source artifacts, related pages, and the gate that reports update needs.

Update Decision

This is not static copy. When source artifacts, related policies, public metrics, or generators change, AI Ops reports evidence and an AI agent decides whether the page needs edits.

Human Boundary

Systems detect, report, and preserve machine-readable evidence. Codex/Claude agents perform final judgment and repair.

Verification Command

node scripts/verify-trust-pages.js --check

data/state-machine/public-bench.json
data/kb-machine-audit.json
data/public-claim-registry.json
Related governance pages: methodology · benchmark · System Card · Transparency Report
Update flow: npm run update:trust-pages → npm run test:trust

Verifiable Evidence Layer

This block is not a narrative claim. Each core assertion has a claim id, source JSON, hash, and a repeatable verification command. Public pages disclose governance evidence without exposing source code, secrets, private data, or exploitable attack details.

Claim ID	Verifiable value	Status	Owner	Source and verification
claim.public-url-manifest.indexable-count Public URL and canonical inventory	38,965 indexable URLs	verified	sitewide	`node scripts/generate-public-evidence-manifest.js --dry`
claim.trust-pages.audit-pass-rate Trust page machine audit	180/180 pass	verified	sitewide	`node scripts/verify-trust-pages.js --check`
claim.discovery-surface.zero-errors AI discovery surface audit	{"errors":0,"warnings":0}	verified	sitewide	`node scripts/verify-discovery-surface.js`
claim.structured-data.jsonld-errors JSON-LD / structured data audit	{"structured_data_invalid_files":0,"breadcrumb_count":28274,"faq_count":27506,"dataset_count":30,"article_count":27406}	verified	sitewide	`node scripts/site-machine-audit.js`
claim.status.sla-state Status page SLA source	critical / 5 critical, 0 warnings	verified	sitewide	`node scripts/generate-status-page.js`
claim.provider-alignment.openai-anthropic-gemini OpenAI / Anthropic / Google Gemini benchmark alignment	benchmark alignment only unless code/config evidence exists	verified	sitewide	`node scripts/verify-public-evidence.js --check`
claim.transparency-report.sha256 Transparency report SHA-256 anchor	{"report":"transparency/report-2026-Q3.json","sha256":"47b09e2ca4e8b8fe9dffdfaccef3b11212de9ee3a8a14badca8044e2481203c5"}	verified	sitewide	`node scripts/update-transparency-current-data.js`
claim.release-integrity.gpg-signing GPG signing status	GPG signing configured locally; GitHub verification pending	github_verification_pending	sitewide	`gpg --list-secret-keys --keyid-format=long && git log -1 --show-signature`

public-evidence-manifest.json public-claim-registry.json public-verification-report.json public-url-manifest.json

System Card V2.0: Technical Transparency Layer

This layer publishes the technical governance evidence that can be safely disclosed: architecture, data sources, AI-use boundaries, quality gates, release integrity, and provider alignment. Source code, secrets, exploitable attack details, and private data remain out of scope.

Public architecture

Cloudflare Pages/Workers, R2/D1/KV/Pagefind, and local generation scripts form the public-site and governance publication chain. Public pages disclose behavior, state, and traceable sources, not secrets or internal permissions.

AI-use disclosure

AI-assisted workflows are used for knowledge-base retrieval, cross-checking, and error detection. Governance documents are benchmarked against OpenAI, Anthropic, and Google Gemini public frameworks. Production model usage is disclosed only when code/config evidence exists.

Quality and safety gates

Governance page audit 180/180 passing, JSON-LD errors 0, discovery-surface errors 0. Status pages report critical / 5 critical, 0 warnings as-is.

Data traceability

Knowledge base 32,724 chunks, TM 789,031 entries, AI answer-ready 7,976/7,976. Public metrics trace to data/state-machine/*, data/*audit*.json, and transparency reports.

Governance area	OpenAI	Anthropic	Google Gemini	Starnum implementation evidence
Model/system-card disclosure	OpenAI models + safety docs	Claude model docs + system/model cards	Gemini model docs + safety settings	system-card, model-card, methodology, benchmark, transparency-log
Safety evaluation and use boundaries	Safety best practices / deployment checklist	Responsible Scaling / safety policy	Gemini safety controls / policy	AI safety, acceptable-use, ethics, risk-boundary copy, crawler policy audit
Data governance	Data controls / privacy controls	privacy and data handling docs	Gemini API data governance references	privacy, ai-data-governance, KB/TM source tracking, SHA-256 hashes
Monitoring and release	production checklist / eval discipline	system-card transparency discipline	model/version documentation discipline	deploy.js, status.html, SLA report, trust-pages-machine-audit, sitemap/hreflang audits

Sources: data/state-machine/model-card.json, public-bench.json, trust-pages.json, security-headers.json.
Sources: data/trust-pages-machine-audit.json, data/discovery-surface-audit.json, data/ai-answer-readiness-audit.json.
Sources: data/kb-machine-audit.json, data/tm/quality-audit-report.json, data/sla-report.json.
Official benchmark docs checked: 2026-07-30; links are listed in the OpenAI / Anthropic / Google Gemini alignment table.

The V2.0 goal is not more claims; it separates implemented controls from planned controls. Production usage, benchmark alignment, status exceptions, GPG signing, and SLA breaches are disclosed from source data.

Release Integrity And GPG

GPG signing configured locally. signingkey=0934DFA0EDA6363A. GitHub verification pending until the public key upload and Verified badge are confirmed.

OpenAI / Anthropic / Google Gemini Alignment

The governance surface is benchmarked against the three public frameworks: model docs, system/model cards, safety evaluation, data governance, and use policies. This is benchmark alignment, not a claim that every provider is active in production inference. Official docs checked: 2026-07-30

Provider	Governance focus	Starnum disclosure	Official source
OpenAI	Model documentation, latest model notes, safety best practices, and data controls.	No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks.	https://platform.openai.com/docs/models
Anthropic	Claude model documentation, system/model cards, Responsible Scaling, and safety policy.	No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks.	https://docs.anthropic.com/en/docs/about-claude/models
Google Gemini	Gemini API model documentation, safety settings, data governance, and platform policy.	No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks.	https://ai.google.dev/gemini-api/docs/models