--- title: README emoji: 👀 colorFrom: purple colorTo: pink sdk: static pinned: false --- # Abstract Powered ### Independent AI Research Cooperative — modular, geometric, and ruthlessly efficient > “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. --- ## 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) - abstract-powered/vocab-register-* Reusable dictionaries with batched indexes, Top-3 OOV composites, and fast penta/volume queries. - 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. --- ### One-Sentence Summary **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.