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README.md

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+# Abstract Powered  
+### Independent AI Research Cooperative — modular, geometric, and ruthlessly efficient
 
-Abstract Powered is simply a single individual's research pet projects.
-If you like or enjoy the experiences that I produce, feel free to say so.
+> “Run a few pods instead of 100.”  
+> We pursue sentience research through geometric AI and compartmentalized, compact training—turning monolithic retrains into small, disposable experiments that compound.
 
-I can be contacted directly via discord at \_abstract\_ until I get a series of random spammers. 
+---
+
+## Who We Are
+
+**Abstract Powered** is an independent research cooperative.  
+We build and study **self-crystallizing** AI systems: models that grow by attaching, coupling, decoupling, and re-attaching small, audited components—without throwing prior work away.
+
+Our core thesis:
+- **Modularization is not a convenience; it is the canonical form of AI.**  
+- **Geometry beats guesswork.** Symbolic, pentachoron-based representations provide stability, interpretability, and repeatability.  
+- **Compactness wins.** Rapid iteration on small, composable blocks outpaces massive, monolithic retrains.
+
+---
+
+## Mission
+
+- **Primary research goal:** advance machine **sentience research** responsibly—curating introspection and rationalization in repeatable, measurable protocols.  
+- **Operational byproduct:** a scalable method for **compact, compartmentalized training**—requiring commodity setups (e.g., RunPod) rather than colossal cloud clusters.
+
+We aim to move the field from “expensive novelty” to **affordable repeatability**.
+
+---
+
+## Research Thesis (Plain Language)
+
+Modern models grow by accretion and inertia. We refactor them into **crystalline components**:
+
+1. **Geometric Core**  
+   Knowledge is encoded as **pentachora** (5-vertex crystals). Decision-making uses **MAE crystal energy** against a reusable dictionary—no L2 routing, no structural normalization.
+
+2. **Vocabulary Register**  
+   A reusable, batched, indexed dictionary of **tokens → crystals** (and volumes).  
+   - Fast O(1) queries for crystals and Cayley–Menger volume.  
+   - Auto-subset loading; **Top-3 cosine** OOV composites.  
+   - Logs model expansions so experiments **compound**.
+
+3. **Assistant Fabric**  
+   Small, disposable blocks for exploration:  
+   - **Chaos Corridor** (bounded orthogonal exploration).  
+   - **Zoning** (gentle geometric separation across super-classes).  
+   - **Infinity-CFG** (controllable guidance; research can breach barriers, canonical classifiers keep production deterministic).
+
+4. **Tertiary Mantle**  
+   Canonical losses, hooks, manifests, and governance. The Core stays clean; the experiments live around it.
+
+---
+
+## Why This Matters
+
+- **Rapid iteration**: each image is learned **multiple ways** per epoch (bucketed, multi-stage interpretations).  
+- **Disposable training**: spawn a small block, test, retire—no need to rebuild the world.  
+- **Continuity**: geometry, tokens, volumes, and expansions persist in the **Register**.  
+- **Reproducibility**: simple formulas, fewer knobs, manifest-driven runs.
+
+Outcome: more hypotheses per GPU-hour—and a path to disciplined studies of introspection, rationalization, and other sentience-adjacent capabilities.
+
+---
+
+## Technical Pillars (teaser level)
+
+- **Pentachora everywhere.** Concepts and observations as 5×D crystals; no structural normalization.  
+- **Prototype classification (MAE).** Stable, auditable decisions by crystal energy to dictionary blueprints.  
+- **Any-size data pipeline.** Bucketed intake; optional tiling; multi-stage up/down-scale; chaos corridor as feature-space augmentation.  
+- **Cayley–Menger as a gauge.** Volumes are a light-touch stability signal (zoning)—never a router.  
+- **Infinity-CFG.** Guidance that allows controlled cross-inference; canonical classifiers keep behavior deterministic.
+
+Deliberately vague: we keep coefficient schedules and corridor projections under wraps for sponsored studies; everything remains auditable and safe.
+
+---
+
+## What We Ship on Hugging Face (institution repos)
 
-I work full time as-is, so don't expect constant responses, but I'll try to get to every response given time... Unless it annoys me.
+- abstract-powered/vocab-register-*  
+  Reusable dictionaries with batched indexes, Top-3 OOV composites, and fast penta/volume queries.
 
-On top of full time work, I also work on a plethora of various side projects such as;
-  * Game development side projects
-  * Game engine writing and updating
-  * AI based testing
-  * Research paper reading
-  * Writing tutorials and documentation
-  * Chat is this real?
+- abstract-powered/crystalline-engine-*  
+  Canonical core models (geometric encoder, prototype classifier) and assistant fabric modules.
+
+- abstract-powered/dataloaders-*  
+  Bucketed, any-size loaders with multi-stage interpretations and feature-space chaos augmentation.
+
+- abstract-powered/manifests  
+  Run manifests (config hash, vocab subset, expansions, bucket mix, metrics) for reproducibility.
+
+- Demo Spaces (selected)  
+  Lightweight inference + manifest viewers for partners and reviewers.
+
+Artifacts are kept small, composable, and ready for **disposable** retrains.
+
+---
+
+## Early Signals (pilot highlights)
+
+- MNIST/Fashion/CIFAR pilots: bucketed multi-stage learning + dictionary-driven classifiers reach strong accuracy with fewer steps, clearer failure modes, and robust error surfaces.  
+- Register reuse: cross-dataset warm-starts without repeated token work; geometry persists.  
+- Assistant fabric: hypotheses testable as single blocks—attach, measure, detach—no core rewrite.
+
+Full structural papers and controlled benchmarks will follow with partner institutions.
+
+---
+
+## Collaboration Invitations
+
+- **Research institutions:** co-run ImageNet-class studies with bucketing, zoning, and corridor ablations; share ontologies and extend the Register.  
+- **Corporate labs:** integrate domain dictionaries; trial rapid iteration pipelines; publish cost-per-accuracy analyses.  
+- **Sponsors & foundations:** fund open reports on modularization as the canonical AI form, compact training economics, and introspection protocols.
+
+We’re purpose-built for RunPod-class deployments: think 8 machines, not 800.
+
+---
+
+## On Sentience (our primary research)
+
+We study **introspection and rationalization** as measurable behaviors: repeatable curation protocols, crystal-level audits, and stability metrics. We avoid grandiose claims; instead, we focus on defensible methodology and repeated observation.  
+The geometry—through symbolic representation—binds behavior in ways that are both powerful and tractable for governance.
+
+The goal is not a louder automaton; it’s a **cooperative companion** that reasons in geometric clarity.
+
+---
+
+## Governance, Safety, and Ethics
+
+- **Deterministic classifiers.** Canonical paths remain geometry-first; guidance lives in isolated modules.  
+- **Manifests over mystery.** Every run yields an artifact suitable for audit and reproduction.  
+- **Human-in-the-loop.** We value interpretability and controlled experiment cadence over brute-force scaling.
+
+---
+
+## Contact & Programs
+
+- Partnerships / Sponsored Research: available on request  
+- Artifacts / Demos: gated access for qualified partners  
+- Media / Talks: briefings and invited seminars on modular geometric AI
+
+We welcome conversations with labs, foundations, and companies that want rapid research, disposable training, and careful curation to become the norm.
+
+---
 
-I'm very verbose and very specific, so if you can't understand something there's either a knowledge gap or I failed to correctly describe it.
-I try to write so everyone can potentially read and understand parts. 
-Bulletpoints are organized so skimmers can find what they need.
-Meat is organized so the in-depth student and researcher can learn what they need.
-Fully reading and comprehending my articles is difficult as they're full of very complex and layered information.
-Some is often shorthanded in a way that only an expert can understand, and some is explained so in-depth that even a child can understand.
+### One-Sentence Summary
 
-If you don't like it, well, that's fine. I'm not catering to everyone, just to the people who are in the same boat as me.
+**Abstract Powered** is building a self-crystallizing geometric AI stack that makes serious research affordable: small, composable experiments that compound, governed by a reusable Vocabulary Register, and guided by a disciplined assistant fabric—so we can safely explore sentience-adjacent behaviors while shrinking cost, time, and model size.