Applications
Papers: Hyperon for AGI⇒ASI Whitepaper (2025), §9
Status: Active pilot efforts and research workstreams in four domains. Each at early stages with near-term milestones defined in the whitepaper.
Hyperon's development philosophy is to build real applications in diverse domains from day one, using each as both a testbed and a source of learning that benefits all others. Four domains are currently active — game AI, humanoid social robotics, bioinformatics, and mathematical reasoning. All connect to the same Hyperon+PRIMUS substrate, meaning advances in perception, reasoning, pattern mining, motivation, and transfer benefit every domain simultaneously.
Architecturally, multiple applications share common "collective knowledge" AtomSpaces while maintaining separate per-application AtomSpaces for task-specific processing. The same mathematical controls (weakness priors, geodesic f·g control) and the same neurosymbolic loop (Pattern Miner → WILLIAM → Symbolic Heads/PLN) operate uniformly across all domains.
Responsible: Ben Goertzel (architecture); Berick Cook (AIRIS integration)
Papers: Hyperon for AGI⇒ASI Whitepaper (2025), §9.1
GitHub: Vereya (Minecraft mod), minecraft-demo (Python API), minecraft-experiments, AIRIS-client, rocca (Rational OpenCog Controlled Agent — OpenAI Gym RL), axiom (AXIOM object-centric game-world agent)
Status: Active pilot. Minecraft integration operational via Vereya mod and tagilmo Python API. AIRIS demonstrated causal learning in Minecraft without pre-training. Sophiaverse and Neoterics playground are under development.
Games provide ideal sandboxes for testing perception, planning, tool use, dialogue, and social norms with rapid iteration and safe failure. Three environments are targeted:
- Minecraft — A structured yet open world for integrating SubRep options with evolutionary program edits. The Vereya Fabric mod (Java 21) exposes game state via a network API; the tagilmo Python library provides high-level agent control.
- Sophiaverse — Adds persistent identity, social contracts, and economic systems to the game AI substrate.
- Neoterics — A deliberately constrained micro-world within Sophiaverse, optimized for baby-AGI development with short feedback loops, dense sensor coverage, and cheap resets.
Technical Architecture
The whitepaper describes the game interface operating through Spaces that mirror game state (voxels, entities, quests, markets) into AtomSpace as typed Atoms. Action affordances appear as SubRep options (navigate, craft, trade, negotiate) with MeTTa rules expressing pre/post-conditions. External LLMs handle narration and quest generation initially, with Symbolic Heads retrieving mined templates like task frames and recipes. PLN factor-graphs encode precondition networks and multi-step plans, with geodesic control scoring candidate steps.
AIRIS (Autonomous Intelligent Reinforcement Inferred Symbolism) has demonstrated causal learning in Minecraft — constructing deterministic environment models through direct interaction without pre-training, and self-correcting via scientific method application.
Additional Repos
- rocca — Rational OpenCog Controlled Agent. Python RL agent using OpenCog AtomSpace for OpenAI Gym environments. Demonstrates planning via PLN and action selection via cognitive schematics. Legacy OpenCog era but conceptually aligned with the PRIMUS game-AI architecture.
- axiom — AXIOM (Adaptive Expansion Object-Centric Models). JAX-based system for rapidly learning to play video games through object-centric representation learning — discovers and tracks objects from raw pixels using hierarchical slot mixture modeling with MPPI-based planning. Developed by VERSES AI. Relevant as a modern object-centric game-world agent architecture, though not directly integrated with MeTTa or AtomSpace.
Key References
- Goertzel, B. (2025). Hyperon for AGI⇒ASI Whitepaper, §9.1: Game AI
- AIRIS — causal machine learning system
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Responsible: Ben Goertzel (architecture)
Partners: Mind Children (education robot); Hanson Robotics (expressive humanoids — Sophia, Desdemona)
Papers: Hyperon for AGI⇒ASI Whitepaper (2025), §9.2
GitHub (legacy OpenCog): ghost_bridge (ROS-GHOST bridge), ros-behavior-scripting (Eva robot control, production 2015–2017), loving-ai (Loving AI dialogue/meditation), loving-ai-ghost (GHOST chatbot scripts for Loving AI)
Status: Partnerships established with Mind Children and Hanson Robotics. Legacy OpenCog ROS integration exists (ghost_bridge, ros-behavior-scripting, loving-ai). Hyperon-native robotics integration is under development; the whitepaper (§9.2) describes the target architecture.
Humanoid social robotics provides a stress-test for PRIMUS: robots must combine perception, dialogue, motor control, and social norms seamlessly — from classroom tutoring to stage performance — without requiring a complete rewrite for each scenario.
Target Architecture (from Whitepaper)
The whitepaper describes a layered architecture: external audio/vision neural networks for initial perception, with progressive embedding of predictive coding layers for latency-critical paths (gaze estimation, lip-sync, gesture segmentation). Pattern Miner would discover conversational templates (adjacency pairs, tutoring frames) for Symbolic Heads to retrieve during speech decoding, while PLN enforces persona and safety constraints. MetaMo would budget the balance between exploration and de-escalation behaviors.
Motor control keeps low-level servo loops on the robot while SubRep options cover higher-level skills ("look at face", "nod", "hand-wave") with formal admission certificates. Geodesic control aligns dialogue steps with motion options so physical gestures support conversational goals. The whitepaper describes capability-gated on-device modification and safety dashboards tracking invariant bands for persona and etiquette.
Current Implementation
The existing repo surface is legacy OpenCog infrastructure:
- ghost_bridge — ROS package connecting Hanson Robotics hardware to the GHOST chatbot system via AtomSpace. Bridges speech, gaze, gestures, emotion recognition.
- ros-behavior-scripting — ROS nodes for Eva robot sensory input (vision, audio) and motor control. Production use with Hanson Robotics 2015–2017.
- loving-ai — Loving AI project for social interaction and guided meditation with the Sophia robot. Demonstrates empathetic dialogue, emotional responsiveness, and therapeutic conversation. Uses OpenCog AtomSpace for dialogue management.
- loving-ai-ghost — GHOST chatbot dialogue scripts for the Loving AI project. Rule-based dialogue trees with emotion-aware branching for meditation guidance and empathetic conversation.
Hyperon-native equivalents of these components are part of the development roadmap.
Key References
- Goertzel, B. (2025). Hyperon for AGI⇒ASI Whitepaper, §9.2: Humanoid Social Robotics
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Responsible: Ben Goertzel (architecture); iCog Labs (data pipelines)
Papers: Hyperon for AGI⇒ASI Whitepaper (2025), §9.3
GitHub: agi-bio (genomics/proteomics), biochatter-metta (NL-to-MeTTa queries), pubchem2metta (PubChem conversion), bio-semantic-parser (biological data parsing), bio-data-semantic-parsing (bio data semantic parsing pipeline)
Status: Active pilot. Data ingestion pipelines operational (agi-bio, biochatter-metta, pubchem2metta). End-to-end hypothesis generation on longevity datasets is a near-term milestone.
Biology is fundamentally structured as graphs: genes connect to proteins, proteins form pathways, pathways influence phenotypes, drugs modulate these relationships. Hyperon's graph-native architecture is well-suited to combining noisy biological graphs, mining meaningful motifs, running uncertain chains of reasoning, and proposing ranked hypotheses with clear rationales.
Data Pipeline
Data flows into AtomSpace through BioSpace adapters that transform omics matrices into node attributes, protein-protein interactions and pathways into edges, literature triples into assertions with provenance, and clinical outcomes into noisy links with confidence scores. Everything receives CIDs, making merges auditable and reproducible. Existing tools include:
- agi-bio — OpenCog-era genomic and proteomic data exploration (C++/Scheme/Python), extended by MOZI.AI as SingularityNET services
- biochatter-metta — Converts natural language biomedical questions into MeTTa queries against the Human BioAtomspace knowledge graph using LLMs with BioCypher schema
- pubchem2metta — Converts PubChem RDF chemical data into MeTTa format via BioCypher adapters
- bio-semantic-parser — iCog Labs biological data parsing tool for extracting structured representations from biological datasets
- bio-data-semantic-parsing — iCog Labs pipeline for semantic parsing of biological data into knowledge graph-compatible formats
Proposed Hypothesis Generation Pipeline
The whitepaper describes a pipeline where Pattern Miner identifies motifs (e.g., "gene A ↔ pathway P ↔ phenotype Y with drug D evidence") ranked by I-surprisingness, WILLIAM promotes frequent subgraphs to reusable templates, PLN factor-graphs propagate graded truth over ontologies and experimental results, and MOSES/GEO-EVO evolves predictive programs. TransWeave would move mechanism components across cohorts or omics platforms when matches hold strong.
The proposed output would be ranked hypothesis packs — auditable CID bundles containing mechanism graphs, predictors, expected biomarkers, and counter-evidence — with experiment selection guided by geodesic f·g control.
Key References
- Goertzel, B. (2025). Hyperon for AGI⇒ASI Whitepaper, §9.3: Bioinformatics
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Responsible: Ben Goertzel (architecture)
Papers: Hyperon for AGI⇒ASI Whitepaper (2025), §9.4
GitHub: chaining (forward/backward chaining, proof synthesis), mm2metta (Metamath→MeTTa converter), mmverify.py (Metamath verifier)
Status: Existing tools include MeTTa forward/backward chaining (chaining repo), Metamath-to-MeTTa conversion (mm2metta), and a Metamath proof verifier (mmverify.py). The whitepaper reports an MM2 proof verifier implemented inside MORK as a fast graph-local proof kernel. The broader automated conjecturing pipeline described below is the proposed research direction.
Most automated theorem proving focuses on proving stated goals, but mathematicians spend much of their time on conjectures — formulating new definitions, lemmas, and "what if" hypotheses that later get proved, refuted, or refined. Hyperon targets automated conjecturing as the primary goal, then uses formal proof for validation.
Proposed Architecture (from Whitepaper)
The whitepaper describes a pipeline built around the MORK/MM2 proof verifier kernel:
- Knowledge base: Definitions, theorems, proofs, and proof tactics stored as Atoms. A MathSpace wrapper would handle import/export to external proof assistants while maintaining CIDs and equivalence classes internally.
- Pattern discovery: Pattern Miner finds proof motifs (induction schemas, commutativity tricks) ranked by surprisingness. WILLIAM promotes frequent motifs to templates for tactic prediction and proof search heuristics.
- Conjecture engine: Conjecturing framed as geodesic search over typed expressions — forward factors from known lemmas, backward factors from gaps to close, weakness bias toward simple statements, TransWeave reuse of isomorphic substructures from neighboring theories.
- Proof verification: Candidate conjectures enter a pipeline — schematic proofs with PLN hints, external provers when helpful, then validation in the MM2 proof verifier on MORK.
Current Tools
- chaining — MeTTa implementations for automated reasoning: forward/backward chaining, program synthesis, PLN integration, Metamath reasoning, and modal logic experiments
- mm2metta — Converts Metamath formal proofs (.mm) to MeTTa (.metta) format, bridging formal verification with Hyperon's symbolic reasoning
- mmverify.py — Pure Python Metamath proof verifier (~700 lines), providing an independent verification baseline
Key References
- Goertzel, B. (2025). Hyperon for AGI⇒ASI Whitepaper, §9.4: Mathematical Theorem Proving with Automated Conjecturing
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