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:
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.
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.
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.
The existing repo surface is legacy OpenCog infrastructure:
Hyperon-native equivalents of these components are part of the development roadmap.
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 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:
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.
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.
The whitepaper describes a pipeline built around the MORK/MM2 proof verifier kernel: