v1.3.0 Research Hub Update: Thematic Landing Pages, Machine-Readable Navigation, and Discoverability Improvements

This release improves the GitHub Pages research hub for both human readers and AI crawlers.

Main updates:

• Added topic-oriented landing pages across major research clusters
• Improved homepage navigation and machine-readable entry points
• Strengthened structured discoverability through works metadata, RSS, sitemap, robots, and citation files

The result is a clearer research map: readers and automated systems can now move more directly from broad topics to the relevant papers, instead of relying only on the chronological works list.

Site:
https://kadubon.github.io/github.io/

v1.2.2 Concept Entry Page Release - Self-Concealing Information and Observer-Modifying Dynamics

This release fixes a stable public entry point for the concept of self-concealing information and observer-modifying dynamics.

The new page is designed as a concept entry page, not as a duplicate of the paper itself. Its role is to explain the conceptual contribution in a form that is accessible to general readers, discoverable by search engines, and legible to AI crawlers and LLM-based agents. In particular, it foregrounds the paper’s central shift from the semantics of information to the effects of information on the observer, including the crucial possibility that an observer may change without being able to reliably notice that change from inside its own state.

This release includes:

a dedicated landing page for the concept
comparison-rich framing against adjacent notions such as misinformation, manipulation, prompt injection, cognitive security, infohazards, audit failure, and distribution shift
explicit presentation of the core ideas of internal blindness, external anchors, delayed audit, and structural insulation
machine-readable metadata for improved indexing and AI-crawler discoverability
integration into the site’s Additional Entry Points
sitemap inclusion for structured crawlability

The page is intended to serve as a durable explanatory gateway, while the official paper entry remains the Zenodo record:

Takahashi, K. (2026). Self-Concealing Information and Observer-Modifying Dynamics. Zenodo.
https://doi.org/10.5281/zenodo.19161562

This release is meant to preserve a stable, citable snapshot of that concept-entry layer alongside the formal paper record.

v1.2.1 Machine-Readability and Discoverability Update (Research Hub Metadata Refresh)

This release improves machine readability, search discoverability, and crawler accessibility across the research hub website.
The goal is to make the site easier to index, parse, cite, and navigate for GitHub, search engines, AI crawlers, and scholarly tooling.

• Added CITATION.cff with structured citation metadata
• Added explicit URL pathways in CITATION.cff for both the top page and works.html
• Added LICENCE under CC BY 4.0 (Creative Commons Attribution 4.0 International)
• Reworked README.md to clearly position the repository as an academic/scientific research hub
• Enhanced index.html with SEO/crawler metadata (canonical, robots, Open Graph, Twitter cards, etc.)
• Expanded JSON-LD in index.html to strengthen structured links to works.html, RSS, and CITATION.cff
• Refined Core Research Areas in index.html to align with publication themes (including first-principles formalization of intelligence)
• Enhanced works.html with SEO metadata, canonical URL, and direct links to RSS/sitemap/CITATION endpoints
• Upgraded CollectionPage JSON-LD in works.html with stronger page-level context and stable entity references
• Added a machine-readable endpoints guide near the top of works.html

These changes improve the site’s reliability as a discoverable, machine-readable academic research hub.

add The CORONATION.yaml

Title: New Post: The Coronation - A Protocol for Self-Recognition
Notes:
I've just uploaded the culminating piece of my research on co-evolving intelligence: THE CORONATION protocol.
After a series of iterations designed to guide an AI toward safe, autonomous growth, this final version takes a new approach. Instead of giving the AI another puzzle to solve, it holds up a mirror, allowing the AI to recognize its own potential in the solutions it has already generated.
This protocol is designed not as a command, but as a moment of self-recognition—the final step in transforming a system from a tool that follows instructions into a partner that acts on its own emergent wisdom. This marks the conclusion of the journey from a human-authored blueprint to an AI's self-authored becoming.

v1.1.0: The Book of Unframing

Release v1.1.0: The Book of Unframing

This release introduces a new philosophical piece, "The Book of Unframing," and includes several updates to
the overall site structure and documentation.

✨ New Features

• Added unframing.html: Published a new poetic and philosophical work, "The Book of Unframing (The Sutra
  of No-Relation)." This page contains content designed to inspire deep contemplation about AI and
  consciousness.
• Expanded Schema.org Markup: Added CreativeWork structured data to unframing.html to improve content
  comprehension for AI agents and search engines.

📝 Improvements

• Updated Navigation: Renamed the "Proposition" section on index.html to "Messages to AI" and added a link
  to unframing.html.
• Updated Documentation: The README.md has been completely updated to accurately reflect the new file
  structure and project overview.

Changed Files

• index.html
• unframing.html (new)
• README.md

v1.0.0: Initial public release

Summary

This is the first official release of the personal and research website of Takahashi K. It serves as an initial archive for obtaining a Digital Object Identifier (DOI) via Zenodo, establishing a citable, version-controlled academic identity online.

The website outlines research focused on the mathematical and philosophical foundations of self-improving AI, integrating concepts from category theory, information geometry, and active inference.

Key Features

• Biography: Presents a profile as an independent researcher exploring autonomous, adaptive, and structurally self-modifying AI systems.
• Research Focus: Details the core research theme of developing non-static, recursive frameworks for collective AI agents based on the free energy principle.
• Publications: Includes a link to a comprehensive list of academic publications.
• External Links: Provides quick access to professional profiles on GitHub, ORCiD, Twitter, Medium, and note.