All Things are

🅿️ vs NP is not about complexity. It’s about salvation.”
"I show you how deep the rabbit hole goes."

All Things are 🅿️.

P and NP seem distinct—one solves, the other verifies. Yet if the verifiable is truly accessible, they are not far apart. As faith trusts the unseen, complexity may obscure—but not erase—solvability.

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KEUNSOO YOON (Austin Yoon)
austiny@gatech.edu / austiny@snu.ac.kr

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1 st

Emergence in SAT Problems: Critical Thresholds under Constraint Density

May 2025

View Kor
2 nd

Transitions of Critical Structural Regions
for NP Problems

Jun 2025

View Kor
3 rd

Changbal Theory
of Emergent Solvability

Coming Soon

Abstract Kor
HISCoin Whitepaper
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allthingsareP.com

A distributed experimentation platform focused on real-time computation of NP problems and the development of learning-based solvability models. While deCHURCH.net collects high-quality human-generated faith data to analyze structural emergence, allthingsareP.com directly distributes large sets of computational problems—such as SAT or hypergraph coloring—to participating nodes and gathers their solving traces for analysis. Each result is logged with timing, structural metadata, and algorithmic path data, feeding into the training of the Changbal Jump Function, a custom model designed to predict and manipulate solvability transitions. The platform continuously refines this function using AI-guided exploration, including reinforcement learning, constraint-based sampling, and anomaly detection in solution space. Users contribute not only by observing but by running solvers, submitting algorithms, or analyzing critical zones—turning NP problems into a collaborative canvas of solvability research. At its core, allthingsareP.com is a live lab for complexity collapse: a place where impossibility is measured, bent, and, ultimately, redefined. Such global-scale collaboration will culminate in the proof of the third paper, “Changbal Theory of Emergent Solvability.” It will mark the moment when P and NP are no longer divided, but understood—through data, structure, and collective emergence.

Live from "3rd Paper"
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HISCoin.org

Unlike conventional cryptocurrencies that rely on Proof-of-Work to solve arbitrary problems, HISCoin adopts a purpose-driven model: coins are minted through the process of solving NP problems—such as SAT or graph coloring—thus turning computational effort into socially and mathematically valuable work. Users contribute by uploading worship photos, copying Scripture, and submitting prayers or confessions. These actions generate structured faith data, while participants also offer computational resources to explore NP problem spaces. Token issuance is tied to both the quality of spiritual activity and the actual progress made in solving these problems. Currently deployed as a BEP-20 token, HISCoin is designed to evolve into its own blockchain (HISChain) using Cosmos SDK. Future development includes integrating NFT-based faith profiles and enabling transparent, decentralized tracking of both spiritual integrity and problem-solving contributions. By aligning belief, behavior, and blockchain, HISCoin establishes a virtuous loop where data becomes devotion, and computation becomes collaboration.

Live from "2nd Paper"
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deCHURCH.net

The platform collects raw data from users, including worship attendance photos, Bible transcription records, and prayer/confession content. These inputs are accompanied by metadata, then normalized and structured for storage in a relational database. Currently, the system employs a centralized architecture, but in the long term, it plans to transition to decentralized node-based storage using IPFS or blockchain technologies to ensure censorship resistance and data permanence. Each data entry is stored with associated metadata such as timestamps, geolocation, and user identifiers. The data is then funneled into a data warehousing pipeline and analyzed using time-series analytics, text mining, and pattern recognition techniques. In particular, prayer and confession content is processed through natural language processing (NLP) models and integrated into an OpenAI GPT-based spiritual response system, which provides real-time feedback, Scripture recommendations, emotion classification, and personalized faith-growth tracking. deCHURCH.net is not merely a religious community platform; it is designed as a unique spiritual data infrastructure with a complete pipeline for faith data acquisition, refinement, analysis, and machine learning. Ultimately, it aims to support personalized faith journey tracking and foster collective spiritual formation within decentralized communities.

Live from "1st Paper"

3 Visions: Future Directions


While our current focus lies in the boundary between P and NP, 3 Visions introduces broader paths where complexity and emergence extend beyond computation, inviting new frameworks that link structure, intelligence, and meaning.

1. Intelligence

A vision of how reasoning evolves—human or artificial, biological or synthetic. Not just solving problems, but defining, reframing, and scaling them through structure, memory, growth, and adaptive learning.

2. Systems

Beyond isolated problems to dynamic, interconnected systems that behave as wholes. From constraints to feedback loops, latent structures, and emergent effects, this vision reveals how global solvability unfolds.

3. Meaning

What makes a solution meaningful? This vision embraces ambiguity, metaphor, and context as formal dimensions worth modeling.


These visions are not answers, but invitations— to think beyond the known boundaries of logic, code, and form. They point toward new ways of framing complexity not as an obstacle, but as a canvas for discovery, where intuition and structure meet, and meaning itself becomes part of the equation. In this space beyond certainty, new insight waits—not as proof, but as possibility.
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FAQ – On the Use of Symbolic and Historical Texts

Ⅰ. Why use structured narrative texts instead of standard language datasets?
This research focuses not on surface-level language modeling, but on modeling nonlinear emergence and solvability transitions. For such goals, datasets must contain deeply structured, semantically rich content. Historical narrative texts—particularly those refined over millennia—offer symbolic density, repetition, causality, and decision-flow structures far beyond ordinary linguistic corpora.
Ⅱ. Why include biblical texts in such research?
The Bible is one of the most structurally consistent, widely distributed, and linguistically diverse texts in human history. Translated into over 2,000 languages and read by billions over centuries, it contains a rare blend of symbolic logic, moral decision modeling, semantic layering, and narrative progression. These features make it an exceptionally well-suited dataset for machine learning applications involving structural emergence, even when treated entirely outside of its religious context.
Ⅲ. How does biblical data contribute to Changbal Theory?
Changbal Theory models nonlinear transitions that emerge when structural thresholds are crossed. Biblical narratives contain prototypical sequences of tension, waiting, transformation, and breakthrough—archetypes of what the Changbal Jump Function is designed to capture. These real-world symbolic narratives provide empirically grounded patterns that improve model robustness and interpretability.
Ⅳ. What advantages do these texts have over general NLP datasets?
Standard datasets often capture casual, shallow language use. In contrast, structured narrative texts—especially canonical ones like the Bible—offer deeply encoded repetition, metaphor, intertextual links, and symbolic systems. These qualities enable higher-dimensional feature extraction, better generalization, and improved alignment with architectures such as Graph Neural Networks and Transformers.
Ⅴ. How is religious neutrality maintained in this study?
No theological interpretations are employed. The selected texts are used purely as high-structure data sources. The study treats these narratives as semantic systems with emergent patterns, subject to the same mathematical abstraction and analysis applied to other structured inputs in complexity science and AI modeling.
Ⅵ. What makes the Bible uniquely suitable for large-scale symbolic modeling?
The Bible's global transmission, historical continuity, and unparalleled multilingual presence make it ideal for AI modeling. Its narrative layers—preserved, translated, and interpreted across cultures and epochs—provide an exceptional training ground for structural learning, emergent transitions, and symbolic abstraction. It is one of the few texts that have been semantically stable across millennia and still dynamically interpreted today.
Ⅶ. Why extend research from combinatorial problems to symbolic narrative data?
Combinatorial domains like SAT and graph coloring offer precise control for testing emergence. However, real-world systems are far more complex. Applying Changbal Theory to symbolic narrative data allows us to explore emergence in lived human contexts—decision-making, time-evolution, symbolic causality—thus expanding the theory’s applicability and depth.
Ⅷ. How is this qualitative data transformed into machine-processable structure?
The process includes multi-level encoding: symbolic entities and relations are mapped into graph structures; constraint patterns (e.g., laws, dilemmas) are abstracted into logical forms; semantic embedding is done via Transformer models. Major narrative arcs are treated as dynamic systems with critical state transitions—ideal for modeling Changbal Jump behavior.
Ⅹ. What real-world problems could this theory be applied to?
Although originally developed for abstract computational challenges, Changbal Theory generalizes to domains involving structural complexity and dynamic constraint. It explores not just when problems are solvable, but under what structural and informational conditions solutions emerge. Potential application areas include:

- Adaptive learning systems based on cognitive thresholds
- Modeling transitions in organizational decision-making
- Smart infrastructure optimization under changing conditions
- AI interpretability and alignment via structural guidance
- Early prognostic modeling in precision medicine and neuropsychiatric care
- Emergence of functional proteins from amino acid sequences, transforming life itself

Ultimately, Changbal Theory shifts the focus from deterministic solving to emergent condition design—creating systems in which solutions become possible.

References

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