--- 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 *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":"…", "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