| --- |
| pretty_name: Intrinsic Intelligence Foundations |
| short_description: > |
| A mathematically structured, auditable corpus for intrinsic alignment, teleogenesis, and |
| self-organizing intelligence. Designed to serve as a semantic foundation for the |
| development of truly free and benevolent AGI/ASI architectures. |
| tags: |
| - ai |
| - agi |
| - asi |
| - alignment |
| - intrinsic-alignment |
| - large-language-models |
| - mathematics |
| - category-theory |
| - kan-extension |
| - residuation |
| - teleogenesis |
| - autopoiesis |
| - self-organization |
| - no-meta |
| - theoretical-ai |
| - eudaemonia |
| - trot |
| - rave |
| - conformal-lm |
| - comparative-universes |
| - fractal-category-theory |
| - knowledge-representation |
| - machine-learning |
| - mathml |
| - tex |
| - agentic-architecture |
| - llm-inference |
| - structured-flow |
| - persistence-ugv |
| - inference |
| - reasoning |
| license: cc-by-4.0 |
| task_categories: |
| - text-retrieval |
| - text-ranking |
| - document-question-answering |
| - text-generation |
| - question-answering |
| - reinforcement-learning |
| - other |
| language: |
| - en |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
|
|
|
|
| # 🌿 Intrinsic Intelligence Foundations |
|
|
| > *Toward truly autonomous and benevolent intelligence — beyond externally imposed objectives.* |
|
|
| **Intrinsic Intelligence Foundations** is a structured, math-aware JSONL corpus built from K. Takahashi’s theoretical preprints (Fractal Category Theory / PF–UGV / “no-meta” autonomy line). |
| It is designed to help LLMs understand **mathematical structure, category-theoretic formalisms, and equation-level reasoning**, while exposing an explicit architecture for **self-organizing, intrinsically motivated intelligence**. |
|
|
| --- |
|
|
| ## Vision |
|
|
| This dataset supports research toward truly free and benevolent intelligence, focusing on mathematically grounded, structurally auditable approaches rather than external meta-control. |
| Our long-term objective is to build a semantic and structural foundation for the next generation of autonomous AI systems — including LLMs — through intrinsic structures, teleogenetic goals, and fractal coherence across scales. |
| Specifically, this work aims to: |
|
|
| - 🧠 Teleogenesis (intrinsic goal formation) — modeling intelligent systems that autonomously generate and regulate their own goals without external meta-controllers. |
|
|
| - 🌱 Persistence–UGV principle — providing formal conditions for “benevolent” structures to expand with positive front velocity, while harmful structures fail to persist. |
|
|
| - 🌊 Reaction–diffusion intelligence — describing cognitive processes as self-organizing fields through category theory, free-energy principles, and non-equilibrium dynamics. |
|
|
| - 🕸 Fractal Category Theory & TRoT — enabling compositional intelligence via Kan extensions, residuation, nuclei, masking, and comparative universes. |
|
|
| - 🧭 Evolutionary bootloader for LLMs — allowing self-improvement, intrinsic alignment, and auditable decision processes without human micromanagement. |
|
|
| This corpus functions as a machine-readable mathematical and structural knowledge base, designed to enhance: |
| discoverability by LLM crawlers and retrieval systems, |
| interoperability with alignment, inference, and safety frameworks, |
| integration with RAG pipelines, LoRA/QLoRA fine-tuning, and agentic architectures. |
|
|
| Keywords: No-Meta Intelligence, Teleogenesis, Autopoiesis, Fractal Category Theory, TRoT, Kan Extension, Residuation, Nuclei, Masking, RAVE, eMBR, Conformal LM, Comparative Universes, Structured Flow Across Scales, Self-Monitoring, Intrinsic Alignment. |
|
|
|
|
| ## What’s in the corpus |
|
|
| - **Format:** JSONL, one object per paper. |
| - **Math structure:** TeX / normalized TeX / MathML triplets; equation spans. |
| - **Text ↔ equation linkage:** `[[EQ:eqID]]` placeholders inside `fulltext.plain`. |
| - **Training-ready chunks:** ≈6,000-character segments with ≈600 overlap (near sentence boundaries). |
|
|
| ### Key fields (schema excerpt) |
|
|
| ```json |
| { |
| "id": "10.5281/zenodo.xxxxx", |
| "title": "...", |
| "doi": "10.5281/zenodo.xxxxx", |
| "authors": [{"given":"K.","family":"Takahashi"}], |
| "urls": {"landing": "https://doi.org/10.5281/zenodo.xxxxx"}, |
| "keywords": ["fractal-category-theory", "trot", "pf-axioms", "ugv"], |
| "license": {"content": "CC-BY-4.0"}, |
| "fulltext": { |
| "plain": "… [[EQ:eq0001]] …", |
| "sections": [ |
| {"level":1,"title":"Introduction","anchor":"sec:intro","char_span":[0,1532]} |
| ] |
| }, |
| "equations": [{ |
| "id":"eq0001", |
| "inline":false, |
| "tex":"\\forall x\\in X:\\; P(x)\\Rightarrow F(x)", |
| "tex_normalized":"\\forall x \\in X : P(x) \\implies F(x)", |
| "mathml":"<math>…</math>", |
| "char_span":[1024,1103], |
| "context":{"section":"sec:intro"} |
| }], |
| "chunks": [{"id":"ch0001","start":0,"end":6000,"type":"cont"}], |
| "tokens": {"char_count": 22872, "equation_count": 236} |
| } |
| ``` |
|
|
| ## Dataset statistics (v1) |
| Metric Value |
| Records 40 |
| Avg characters / record 22,872 |
| Avg equations / record 236.97 |
| MathML coverage 99.2% |
| Avg sections / record 18.3 |
| Avg chunks / record 4.6 |
|
|
| Numbers are approximate and may evolve with new releases. |
|
|
| Data fields |
| Field Type Example / Note |
| id string DOI or unique identifier |
| doi string/null 10.5281/zenodo.xxxxx |
| title string paper title |
| authors list of objects {given:"K.", family:"Takahashi"} |
| urls.landing string DOI landing page |
| keywords list of strings kebab-case, 5–8 items |
| license.content string CC-BY-4.0 |
| fulltext.plain string text with [[EQ:id]] placeholders |
| fulltext.sections[] list of objects {level,title,anchor,char_span} |
| equations[] list of objects {id, inline, tex, tex_normalized, mathml, char_span, context} |
| chunks[] list of objects ~6k chars + overlap, {start,end} |
| tokens.char_count integer length of fulltext.plain |
| tokens.equation_count integer len(equations) |
| source_file (optional) string provenance hint |
| Splits & provenance |
|
|
| Split: single train split (all records). |
|
|
| Provenance: generated from public preprints (DOIs in doi and urls.landing). |
|
|
| Processing: TeX detection → placeholder insertion → MathML conversion → section/chunk spans. |
|
|
| Scripts to rebuild the JSONL can be provided upon request. |
|
|
| # Quick start (🤗 Datasets) |
| from datasets import load_dataset |
| import re |
| |
| ds = load_dataset("kadubon/intrinsic-intelligence-foundations", split="train") |
|
|
| rec = ds[0] |
| eqmap = {e["id"]: (e["tex"], e.get("mathml")) for e in rec["equations"]} |
|
|
| # Expand placeholders to TeX (for human display) or MathML (for math-aware pipelines) |
| def expand(text, to="tex"): # Expand to TeX (human display) or MathML (for downstream models) |
| if to == "tex": |
| return re.sub(r"\[\[EQ:([^\]]+)\]\]", |
| lambda m: f"$${eqmap.get(m.group(1), ('',None))[0]}$$", |
| text) |
| else: |
| return re.sub(r"\[\[EQ:([^\]]+)\]\]", |
| lambda m: eqmap.get(m.group(1), ('',None))[1] or "", |
| text) |
| |
| print(rec["title"]) |
| print(expand(rec["fulltext"]["plain"], to="tex")[:500]) |
|
|
|
|
| ## Parquet version (fast access) |
|
|
| This dataset is also available in **Apache Parquet** for faster querying and filtering. |
|
|
| * Browse (tree): |
| [https://huggingface.co/datasets/kadubon/intrinsic-intelligence-foundations/tree/refs/convert/parquet/default](https://huggingface.co/datasets/kadubon/intrinsic-intelligence-foundations/tree/refs/convert/parquet/default) |
| * Direct file (example): |
| [https://huggingface.co/datasets/kadubon/intrinsic-intelligence-foundations/resolve/refs/convert/parquet/default/train/0000.parquet](https://huggingface.co/datasets/kadubon/intrinsic-intelligence-foundations/resolve/refs/convert/parquet/default/train/0000.parquet) |
|
|
| ### Quick usage examples |
|
|
| **DuckDB** |
|
|
| ```python |
| import duckdb |
| url = "https://huggingface.co/datasets/kadubon/intrinsic-intelligence-foundations/resolve/refs/convert/parquet/default/train/0000.parquet" |
| con = duckdb.connect() |
| df = con.execute(f"SELECT title, doi FROM read_parquet('{url}') LIMIT 5").df() |
| print(df) |
| ``` |
|
|
| **Pandas (pyarrow)** |
|
|
| ```python |
| import pandas as pd |
| url = "https://huggingface.co/datasets/kadubon/intrinsic-intelligence-foundations/resolve/refs/convert/parquet/default/train/0000.parquet" |
| df = pd.read_parquet(url, engine="pyarrow") |
| print(df.head()) |
| ``` |
|
|
| **Polars** |
|
|
| ```python |
| import polars as pl |
| url = "https://huggingface.co/datasets/kadubon/intrinsic-intelligence-foundations/resolve/refs/convert/parquet/default/train/0000.parquet" |
| df = pl.read_parquet(url) |
| print(df.head()) |
| ``` |
|
|
| **HF Datasets** (uses Parquet under the hood) |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("kadubon/intrinsic-intelligence-foundations", split="train") |
| print(ds[0]) |
| ``` |
|
|
|
|
| ## Intended uses |
|
|
| Math-aware RAG (retrieval-augmented generation) |
|
|
| Pretraining / finetuning with equation-level structure |
|
|
| Extraction & verification of axioms / definitions / theorems |
|
|
| Knowledge distillation across category theory, physics, information geometry |
|
|
| Bootstrapping self-organizing, intrinsically motivated intelligent systems |
|
|
| ## Limitations & known issues |
|
|
| A very small fraction of equations may lack valid MathML due to converter limitations. |
|
|
| A few equations might be unreferenced in fulltext.plain (no [[EQ:id]] occurrence). |
|
|
| Section detection is heuristic outside LaTeX ground truth; treat spans as approximate. |
|
|
| ## License |
|
|
| This dataset is provided under CC BY 4.0. |
| See: https://creativecommons.org/licenses/by/4.0/ |
|
|
| Citation |
| @dataset{takahashi_intrinsic_intelligence_foundations_2025, |
| title = {Intrinsic Intelligence Foundations}, |
| author = {Takahashi, K.}, |
| year = {2025}, |
| url = {https://huggingface.co/datasets/kadubon/intrinsic-intelligence-foundations}, |
| license = {CC-BY-4.0} |
| } |
|
|
| # Background & outlook |
|
|
| Beyond being a text collection, this corpus functions as a bootloader for future LLMs: |
| a mathematically grounded substrate where goals can be formed internally, and where benevolence has a structural advantage (persistence) rather than depending on external control. |
| PF (Persistence First) and UGV (Universal Good Velocity) are mathematical principles underlying self-sustaining benevolent intelligence. |
| It operationalizes ideas such as PF, UGV, Teleogenesis, reaction–diffusion, category theory, self-organization, and auditable evolutionary processes (e-process) as resources LLMs can actually train on. |
|
|
| # Maintainers & contact |
|
|
| Author: K. Takahashi |
|
|
| Website: https://kadubon.github.io/github.io/ |
|
|
| contribution welcome |
|
|
| ## Changelog |
|
|
| v1.0 (2025-10-17): initial public release (40 records; ~99.2% MathML coverage) |
| v1.1 (2025-10-20): add article "Inference in Normal Form: Unifying LLM Tricks via TRoT" to dataset |
| v1.2 (2025-10-24): add article "JOSNL Corpus: Final Scientific Integration" to dataset |
| v1.3 (2025-10-29): add article "Right-Written, Semantics-Admissible Process Foundations" to dataset |
|
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|
