Hyperon Wiki Extensions

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  • Applications: Domain-specific deployments: bioinformatics, social robotics, mathematics, and game AI.
  • Publications: Browsable index of all books, papers, and preprints — from Hyperon-era research (2020–present) through the legacy OpenCog corpus.
  • Reference: Cross-cutting indexes: technical companions, implementation families, publication maps, and a GitHub repositories catalog.
  • Ecosystem: The organizations building on Hyperon — from the ASI Alliance and SingularityNET to research partners, robotics companies, and applied ventures.
  • Glossary: Defines technical terms and internal language used within the ecosystem.
  • MeTTa Variants: Non-Index MeTTa runtime variants and adjacent compiler tooling (e.g., MeTTaTron).

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Hyperon is SingularityNET's open-source platform for Artificial General Intelligence, designed to progress from here to AGI and ultimately to beneficial ASI (Artificial Superintelligence). Building on decades of research from the OpenCog project, Hyperon provides a unified neurosymbolic framework where diverse cognitive processes — symbolic reasoning, probabilistic inference, neural learning, evolutionary search, and attention allocation — interoperate over shared memory to produce emergent general intelligence.

Unlike narrow AI systems optimized for individual tasks, Hyperon is designed as a composable infrastructure in which multiple learning and reasoning algorithms collaborate through a principle called cognitive synergy. Each algorithm addresses a fundamental requirement of general intelligence, but it is their interaction — sharing representations, guiding each other's search, and co-evolving within a common knowledge substrate — that is intended to enable the system to tackle problems none could solve alone.

Architecture at a Glance

Hyperon's architecture spans four layers, each documented in detail within this wiki:

  • AtomSpace — A typed, content-addressed metagraph that serves as shared memory and control plane. Atoms represent symbolic data, relationships, truth values, motives, and executable code in a unified structure where code and data are interchangeable. AtomSpace can be implemented on multiple backends, from the high-performance MORK engine (prefix-tree-based, with large speedups over previous implementations) to the DAS (Distributed AtomSpace) for decentralized operation.
  • MeTTa — Meta-Type Talk, a homoiconic programming language that serves as the native "language of thought." MeTTa operates directly over AtomSpace as graph transformations, enabling reflective self-modification, nondeterministic inference, and seamless interoperation between symbolic and neural components. Multiple implementations exist: PeTTa (high-performance Prolog-based), Hyperon Experimental (reference Rust implementation), JeTTa (JVM), MeTTa-Morph (Scheme), and MeTTaTron (F1R3FLY-native).
  • AI Algorithms — A library of cognitive modules authored in MeTTa: PLN for probabilistic reasoning under uncertainty, ECAN for economic attention allocation, MOSES for evolutionary program synthesis, MetaMo for compositional motivation, NARS-based systems for open-ended reasoning, and integration layers for LLMs and neural networks.
  • PRIMUS Cognitive Architecture — The meta-architecture that orchestrates these components into a unified cognitive system. PRIMUS defines how goal-directed and ambient cognitive loops cooperate, how attention and resources flow between modules, and how the system maintains coherence while self-modifying. Recent theoretical advances include weakness-based simplicity priors, geodesic inference control, TransWeave for cross-domain transfer, and WILLIAM for adaptive compression.

The ASI Chain

For decentralized deployment, Hyperon compiles MeTTa into targets running on ASI Chain — a blockchain runtime designed for AGI workloads. ASI Chain provides cryptographically secured execution, content-addressed provenance, and the ability to scale cognitive processes from a single machine to a distributed network. Its F1R3FLY engine renders concurrent process calculi for scalability, while MeTTaCycle orchestrates AGI workloads. The whitepaper describes ASI Chain as targeting "native inference settlement" — verifying cognitive state transitions rather than just token transfers.

Neural-Symbolic Integration

Hyperon bridges symbolic and neural paradigms through two complementary approaches:

  • Outside integration (current) wraps existing neural models (LLMs, vision systems, embedding models) as Spaces within AtomSpace, exposing their outputs for symbolic reasoning and compositional planning. This is implemented via the MeTTa-Motto library.
  • Inside integration (experimental) via QuantiMORK proposes encoding neural network structures — wavelet-structured tensors, weight matrices, activation patterns — directly into the MORK PathMap, enabling predictive-coding-style local learning updates without backpropagation.

From OpenCog to Hyperon

Hyperon is a ground-up redesign of the earlier OpenCog framework, preserving the core cognitive theories (cognitive synergy, CogPrime architecture, patternist philosophy of mind) while incorporating new ideas at every level: a new type system and language (MeTTa replacing Atomese/Scheme), a new high-performance backend (MORK replacing the C++ AtomSpace), new mathematical controls (quantale-based weakness, geodesic effort), and decentralized execution infrastructure. The transition represents not a departure from OpenCog's vision but its maturation into a system engineered for scalability and composability.

Key Resources

Explore Further

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Approved by Ursula Addison on 2026-05-04

Hyperon inherits its conceptual DNA from OpenCog, an open-source AGI framework that evolved through two decades of research and development. Understanding this lineage illuminates why Hyperon is designed the way it is — and what changed in the transition.

Origins: Novamente to OpenCog (1997–2008)

The intellectual roots trace to the late 1990s. Ben Goertzel's work on formalizing general intelligence — defining it as "the ability to achieve complex goals in complex environments" — led to a series of practical systems: the Webmind AI Engine (1997–2001) at Intelligenesis Corp., followed by the Novamente Cognition Engine (2001–2008) at Novamente LLC. In 2008, the Novamente source code was released publicly as OpenCog, establishing an open-source community around the pursuit of AGI.

The conceptual framework was elaborated in several key publications: The Hidden Pattern (2006) on pattern-based philosophy of mind, Probabilistic Logic Networks (2008) on reasoning under uncertainty, and Building Better Minds (2012, with Cassio Pennachin and Nil Geisweiller) detailing the full CogPrime architecture — the specific configuration of cognitive components believed capable of achieving human-level AGI.

OpenCog Classic Architecture

The original OpenCog system, now sometimes called "OpenCog Classic," was built in C++, Scheme, and Python around several core components:

  • AtomSpace — A hypergraph database storing typed atoms (nodes and links) with associated truth values, attention values, and other metadata. Knowledge was represented in Atomese, a Lisp-like language for constructing and querying graph structures.
  • PLN (Probabilistic Logic Networks) — A comprehensive uncertain inference framework supporting deductive, inductive, and abductive reasoning with graded confidence.
  • MOSES (Meta-Optimizing Semantic Evolutionary Search) — An evolutionary program learning system that breeds compact symbolic programs to solve complex optimization problems.
  • ECAN (Economic Attention Allocation Networks) — An attention economy that dynamically allocates computational resources across atoms based on short-term and long-term importance.
  • OpenPsi — A motivational framework implementing PSI theory for drive-based behavior selection, emotional dynamics, and goal management.
  • URE (Unified Rule Engine) — A general-purpose forward and backward chainer for applying inference rules over AtomSpace.
  • Link Grammar & RelEx — Natural language processing components for parsing English into dependency structures and mapping them to Atomese representations.

These components were deployed in virtual agent control (OpenCogBot in virtual worlds), humanoid robotics (Hanson Robotics integration), and biological knowledge exploration.

What Carried Forward

Hyperon preserves the core intellectual commitments of OpenCog:

  • Cognitive synergy — The conviction that AGI requires multiple interoperating cognitive processes, not a single monolithic algorithm.
  • AtomSpace as shared memory — A typed metagraph serving as the common substrate for all cognitive operations.
  • The CogPrime cognitive model — Now evolved into PRIMUS, retaining the same fundamental architecture of interacting memory systems, attention dynamics, and goal-directed reasoning.
  • The same core algorithms — PLN, MOSES, and ECAN remain central to Hyperon, updated with new mathematical foundations.
  • Patternist philosophy — Intelligence understood as pattern recognition and creation across multiple levels of abstraction.

What Changed

Hyperon is not an incremental update but a ground-up redesign motivated by hard-won lessons from a decade of OpenCog development:

  • Language: Atomese and Scheme were replaced by MeTTa — a purpose-built language with a formal type system, homoiconicity (code-as-data), and native support for nondeterministic inference and self-modification. Where Atomese required manual encoding in Scheme, MeTTa operates directly as graph transformations over AtomSpace.
  • Performance: The C++ AtomSpace was complemented (and in high-performance contexts replaced) by MORK, a prefix-tree-based metagraph engine achieving large speedups through radically different data structures and the Zipper Abstract Machine.
  • Mathematics: New unifying mathematical frameworks — quantale-based weakness theory, geodesic inference control, optimal transport for attention — provide principled controls that the original system lacked.
  • Decentralization: OpenCog ran on single machines or small clusters. Hyperon targets decentralized execution via ASI Chain, with cryptographic provenance, capability-secured processes, and blockchain-based governance.
  • Neural integration: OpenCog treated neural networks as external components. Hyperon offers deeper integration through QuantiMORK (proposed: encoding tensors natively in the graph) alongside pragmatic wrapping of existing models via MeTTa-Motto.
  • Motivation: OpenPsi evolved into MetaMo, a mathematically grounded compositional motivation framework with formal stability guarantees.

Technical Deep Dive: OpenCog Legacy Full — complete timeline, 11-row architectural bridge map, five motivations for transition, component maturity analysis, CogServer criticisms, anti-CYC philosophy, pattern mining evolution, and maintained vs. archived repos.

Key References

  • Hart, D. and Goertzel, B. (2008). OpenCog: A Software Framework for Integrative Artificial General Intelligence — Proceedings of the First Conference on AGI, IOS Press
  • Goertzel, B. (2012). Building Better Minds — comprehensive CogPrime design
  • Goertzel, B. et al. (2023). OpenCog Hyperon: A Framework for AGI at the Human Level and Beyond
  • Goertzel, B. (2025). Hyperon for AGI⇒ASI: Whitepaper 2025
  • OpenCog Wiki — historical documentation

 



Discussion

 

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Approved by Anna on 2026-05-02

Cognitive synergy is the foundational design principle of Hyperon: the idea that general intelligence emerges not from any single algorithm but from the cooperative interaction of multiple specialized cognitive processes sharing a common knowledge substrate. It is both a theory of how minds work and an engineering methodology for building AGI systems.

The Core Insight

Human cognition integrates perception, reasoning, memory, attention, motivation, language, and learning into a unified system where each process continuously informs and strengthens the others. A purely logical reasoner struggles with grounding; a purely statistical learner struggles with compositionality; a purely evolutionary system struggles with directed search. But when these approaches operate over shared representations and guide each other's processing, capabilities emerge that none could achieve independently.

In Goertzel's formulation from Building Better Minds: cognitive synergy occurs when multiple cognitive processes — each handling a different aspect of intelligence — cooperate in a way that their combined capability exceeds the sum of their individual contributions. This is not mere parallelism. It is deep interoperation: one process generating hypotheses that another evaluates, one process identifying attention-worthy patterns that another reasons about, one process learning representations that another uses to plan.

How Synergy Is Designed Into Hyperon

Hyperon's architecture is engineered to enable cognitive synergy through three mechanisms:

  • Shared AtomSpace: All cognitive processes read from and write to the same typed metagraph. When PLN derives a new inference, it becomes available to MOSES for program synthesis, to ECAN for attention reallocation, and to pattern mining for structural analysis. Co-location in shared memory reduces the integration overhead that plagues systems where components communicate through narrow APIs or message buses.
  • Common representational language: MeTTa provides a single language in which reasoning rules, evolutionary fitness functions, attention policies, and motivational goals can all be expressed, examined, and combined. Because code is data in MeTTa, one cognitive process can inspect and modify the programs of another.
  • PRIMUS orchestration: The PRIMUS cognitive architecture defines how cognitive processes cooperate within two meta-dynamics: goal-directed loops (where specific objectives drive coordinated processing) and ambient loops (where background maintenance — attention spreading, pattern discovery, memory consolidation — keeps the system's knowledge fresh and well-organized).

Critical Synergies

Some of the most important synergistic interactions designed into Hyperon include:

  • PLN + MOSES: Logical inference generates candidate hypotheses; evolutionary search breeds programs to test them. PLN can evaluate the logical consistency of MOSES-generated programs, while MOSES can evolve inference control strategies for PLN.
  • ECAN + PLN: Attention allocation guides which inferences are worth pursuing (avoiding combinatorial explosion), while inference results inform which atoms deserve increased attention.
  • Pattern Mining + Neural Networks: Mined patterns from AtomSpace can serve as structural priors for neural architectures, while neural embeddings can guide the pattern mining search.
  • MetaMo + All Processes: The motivational framework evaluates which cognitive goals are most urgent and allocates resources accordingly, creating a self-regulating economy of cognitive effort.

Why Cognitive Synergy Is "Tricky"

As discussed in Building Better Minds (Chapter 8), cognitive synergy may explain a puzzling feature of AGI research: the difficulty of measuring partial progress. If intelligence emerges primarily from the interaction between cognitive processes rather than from any individual process, then a system with three out of five components may show dramatically less capability than one with all five — even though it is only "two components away." This creates a perception of sudden capability jumps that are actually the result of crossing synergy thresholds.

This insight has practical consequences for Hyperon development: the system's true capabilities may only become apparent when enough components are integrated and interoperating, not when individual components are benchmarked in isolation.

Key References

  • Goertzel, B. et al. (2012). Building Better Minds, Chapter 8: Cognitive Synergy
  • Goertzel, B. (2009). OpenCogPrime: A Cognitive Synergy Based Architecture for Embodied AGI
  • Goertzel, B. (2025). Hyperon for AGI⇒ASI Whitepaper, §4: The PRIMUS Cognitive Architecture



Discussion

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Approved by Ursula Addison on 2026-05-04

AtomSpace is the foundational data structure at the heart of Hyperon — a typed, content-addressed metagraph that serves as both shared memory and control plane for all cognitive processes. It is the substrate in which knowledge, code, inference results, attention values, motivational states, and neural representations all coexist and interoperate.

Status: The AtomSpace concept and Space API abstraction are current — implemented in the reference Hyperon codebase (hyperon-experimental) and in MORK. The reference AtomSpace, MORK, and DAS backends are operational. Neural Spaces are experimental; blockchain-backed Spaces via F1R3FLY are proposed.

What Is a Metagraph?

An AtomSpace is a generalization of a hypergraph. Where a traditional graph connects pairs of nodes with edges, and a hypergraph allows edges to connect arbitrary sets of nodes, a metagraph goes further: edges (Links) can themselves be members of other edges, and both nodes and links carry typed values. This recursive structure naturally represents the nested, multi-relational knowledge structures required for general intelligence — logical implications, probabilistic dependencies, procedural programs, and attention metadata can all be expressed as atoms within the same graph.

Every atom in AtomSpace is typed. The type system encodes data categories (symbols, numbers, expressions), function types, and logical connectives. The scope and enforcement of type constraints varies across backends — the reference implementation and MORK each handle types somewhat differently. MeTTa programs can introspect and manipulate the type structure itself.

Code Is Data

A defining feature of AtomSpace is that programs and data share the same representation. A MeTTa function definition, a PLN inference rule, a MOSES-generated program, and a factual knowledge assertion are all atoms in the metagraph. This homoiconicity enables:

  • Reflective self-modification: Programs can inspect, analyze, and rewrite themselves and each other at runtime.
  • Cognitive synergy: Different cognitive processes naturally share representations because they all operate on the same substrate.
  • Inference over code: PLN can reason about the properties of programs just as it reasons about factual knowledge.

The Space API

AtomSpace is accessed through a universal Space API that abstracts over multiple possible backends. A Space supports operations for adding, removing, querying, and pattern-matching atoms. Current and planned backends:

  • Reference AtomSpace (current) — The Rust implementation in hyperon-experimental, providing the canonical semantics for MeTTa execution.
  • MORK (current) — The high-performance core. A prefix-tree (trie-map) based engine achieving large speedups over previous implementations. MORK stores S-expressions as paths in a radix tree, enabling near-constant-time content-addressed lookup. Its Zipper Abstract Machine (ZAM) supports multi-threaded parallel execution. (See MORK for details.)
  • DAS (Distributed AtomSpace) (current) — A distributed backend that decouples persistence (Long-Term Importance) from immediate dynamics (Short-Term Importance via an Attention Broker). DAS enables AtomSpaces spanning multiple machines with Hebbian learning-based resource allocation. (See DAS for details.)
  • Neural Spaces (experimental) — Wrappers that expose neural network models (LLMs, embedding models, vision systems) as queryable Spaces, enabling symbolic processes to interact with neural outputs through the same API.
  • Blockchain-backed Spaces (proposed) — AtomSpaces persisted on ASI Chain via F1R3FLY's RSpaces, providing cryptographic provenance and decentralized access.

Content Addressing

Atoms in AtomSpace are content-addressed — identified by what they contain rather than where they are stored. In MORK, this is implemented through content identifiers (CIDs) derived from the atom's path in the trie structure, enabling efficient verification and deduplication. Content addressing is what makes distributed and decentralized AtomSpaces practical: the same atom resolves to the same identifier regardless of which node stores it.

Truth Values and Metadata

Atoms carry associated values beyond their structural content:

  • Truth Values: Probabilistic confidence measures used by PLN for uncertain reasoning. Hyperon supports multiple truth value types including simple (strength, confidence), distributional, and indefinite truth values.
  • Attention Values: Short-Term Importance (STI) and Long-Term Importance (LTI) scalars managed by ECAN to regulate which atoms receive computational resources.
  • Custom Values: Arbitrary typed metadata can be attached to atoms, including tensors, timestamps, provenance records, and motivational annotations.

Historical Context

The AtomSpace concept originated in the OpenCog project as a C++/Scheme hypergraph database. The original implementation stored atoms in an in-memory graph with optional PostgreSQL persistence. In Hyperon, the concept has been generalized: AtomSpace is now an abstract interface (the Space API) that can be implemented on different backends — from MORK's prefix trees to DAS's distributed storage — while preserving consistent semantics for cognitive processes built on top of it.

Technical Deep Dive: AtomSpace Full — metagraph formal definition, Atom/Value distinction, TruthValue-to-FloatValue transition, Space API abstraction, classical C++ vs. Rust implementations, performance architecture, and historical design decisions.

Key References

  • Goertzel, B. (2025). Hyperon for AGI⇒ASI Whitepaper, §2: Hyperon System Design
  • Goertzel, B. (2012). Building Better Minds, Ch. 19–20: OpenCog Framework and Knowledge Representation
  • opencog/atomspace — Original C++ AtomSpace implementation
  • trueagi-io/hyperon-experimental — Reference Rust AtomSpace in Hyperon

 



Discussion

 

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Approved by Anna on 2026-04-27

A central challenge of AGI is unifying the complementary strengths of neural networks (pattern recognition, generalization from data, continuous optimization) with symbolic systems (compositional reasoning, interpretability, knowledge representation). Hyperon's design addresses this through a dual-path architecture offering both pragmatic interoperability and deep structural unification.

Status: Outside integration is current (implemented via MeTTa-Motto). Inside integration (QuantiMORK) and the advanced techniques below are proposed — described in the 2025 whitepaper as research directions.

Two Paths to Integration

Hyperon provides two complementary modes of neural-symbolic integration, suited to different stages of system maturity:

Outside Integration: Pragmatic Wrapping (Current)

In outside mode, existing neural models — large language models, vision transformers, embedding models, reinforcement learning agents — are wrapped as Spaces within Hyperon's Space API. Symbolic processes can query neural models through the same interface they use to query the AtomSpace:

  • An LLM can be queried for semantic parsing, translating natural language into MeTTa expressions that PLN can reason about.
  • An embedding model can provide similarity-based retrieval, enabling pattern mining to discover structural regularities in neural representations.
  • A vision system can populate AtomSpace with perceived objects and relationships, grounding symbolic reasoning in perceptual data.

The MeTTa-Motto library implements this approach, embedding LLMs (ChatGPT, Claude, open-source models) as programmable MeTTa functions with support for stateless wrappers, stateful dialogue agents, retrieval-augmented generation, and functional calling. (See MeTTa-Motto for details.)

The whitepaper also describes Symbolic Transformer Heads as part of outside integration: mined patterns from AtomSpace serve as structured templates augmenting standard transformer attention heads, using contrastive symbolic alignment to ensure patterns survive memory compression in compressive transformer architectures.

Inside Integration: QuantiMORK (Proposed)

The 2025 whitepaper proposes a more radical inside mode called QuantiMORK, in which neural network structures would be natively encoded within the MORK metagraph rather than wrapped. This would represent neural network components — weight matrices, activation patterns, wavelet-structured tensors — directly as paths and values in MORK's prefix-tree database.

Proposed properties of QuantiMORK:

  • Neural networks as native graph operations: Forward passes, weight updates, and activation propagation expressed as graph traversals within MORK, making them amenable to the same attention allocation and reasoning processes that operate on symbolic content.
  • Predictive coding without backpropagation: Local updates where each layer adjusts based on prediction errors from adjacent layers, compatible with Hyperon's distributed, asynchronous execution model.
  • Unified attention: Neural activations and symbolic knowledge would share the same graph, allowing ECAN's attention allocation to operate uniformly across both.

Weakness-Based Stability (Proposed)

To address the risk that neural learning updates could destabilize symbolic knowledge (and vice versa), the whitepaper explores weakness-based regularization: a quantale-valued simplicity metric applying uniformly to both neural weight updates and symbolic inference steps. The whitepaper investigates conditions under which combined updates would approximately commute — a desirable property for reliable integration, though the extent of this commutativity in practice remains an open research question.

Key References

  • Goertzel, B. (2025). Hyperon for AGI⇒ASI Whitepaper, §7: Neural-Symbolic Synergy in Hyperon
  • Goertzel, B. et al. (2023). OpenCog Hyperon, §3.2: Hyperon's Position in the Era of LLMs



Discussion

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Approved by Anna on 2026-04-27

A system capable of general intelligence must eventually be capable of reflecting on and improving its own cognitive processes. Hyperon's design treats self-modification as a first-class capability governed by formal mathematical guarantees — aiming to ensure that a system improving itself remains aligned with its intended goals and values.

Status: Proposed. The self-modification pipeline described here is a research design from the 2025 whitepaper (§8). It has not yet been implemented end-to-end. The mathematical foundations (weakness theory, geodesic control) are under active development; the deployment pipeline and governance mechanisms remain architectural proposals.

The Challenge

Self-modifying AGI presents a fundamental tension: the same capability that enables a system to improve itself could allow it to alter its goals, remove its safety constraints, or destabilize its own reasoning. Hyperon's proposed approach is to make self-modification transparent, auditable, and mathematically bounded.

The Proposed Five-Stage Self-Modification Pipeline

The whitepaper describes a disciplined pipeline for self-modification:

  1. Proposal: A modification is expressed as a typed metamorphism — a formal transformation with explicit pre-conditions, post-conditions, and type signatures. Because MeTTa is homoiconic (code is data), modifications to cognitive processes would be represented as atoms in AtomSpace, subject to the same reasoning and analysis as any other knowledge.
  2. Analysis: PLN and other reasoning processes would analyze the proposed modification for logical consistency, potential side effects, and alignment with current goal structures. The weakness metric would provide a quantitative bound on how much complexity the modification introduces.
  3. Simulation: The modification would be tested in a twin simulation — a sandboxed copy of the relevant AtomSpace subgraph where the modification can be applied and its effects observed without affecting the running system.
  4. Certification: Formal admission certificates would validate that the modification satisfies safety properties, verified using the same inference machinery (PLN over quantale-annotated factor graphs) that powers general reasoning.
  5. Deployment: Certified modifications would be deployed through staged rollout: shadow mode (running alongside the original), dual-run (both versions active with output comparison), and finally elevation to primary status — with rollback capability at every stage.

Goal Stability (Proposed)

The whitepaper proposes addressing goal stability through supermartingale potentials — Lyapunov-like mathematical functions that provably do not increase under permitted modifications. If a proposed change would increase the potential (indicating goal drift), it would be flagged for additional review or rejected. The aim is to transform goal stability from a philosophical concern into a tractable mathematical problem.

Global Regulators (Proposed)

The design envisions certain principles enforced globally across all cognitive processes:

  • Weakness bounds: All updates must satisfy quantale-valued simplicity constraints.
  • Geodesic effort: All control flow follows cost-aware geodesic paths balancing accuracy against simplicity.
  • Transparency: All modifications are logged with full provenance in content-addressed storage.

Decentralized Governance (Proposed)

When Hyperon operates on ASI Chain, self-modifications could become subject to multi-party governance — requiring approval from multiple stakeholders, community voting, or smart contract constraints encoding organizational policies.

Technical Deep Dive: Self-Modification and Safety Full — typed metamorphism formalism, supermartingale goal stability, five-stage pipeline details, lens laws, drift bounds, and decentralized governance.

Key References

  • Goertzel, B. (2025). Hyperon for AGI⇒ASI Whitepaper, §8: Planning for the AGI→ASI Transition
  • Goertzel, B. (2012). Building Better Minds, Ch. 18: Advanced Self-Modification



Discussion



Discussion

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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.

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Human Approved — by Ursula Addison on 2026-05-21

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



Discussion

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Human Approved — by Ursula Addison on 2026-05-21

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



Discussion



Discussion

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Peer-reviewed publications, books, and foundational research underpinning the Hyperon framework. This section provides a browsable index organized by era and topic — from the current Hyperon-era papers to the legacy OpenCog research corpus. Individual component cards throughout the wiki also link to their relevant papers directly.

For structured reading guides that map source material to wiki content, see Publication Maps under Reference.

Books

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Coverage at a Glance

BucketWhat it meansExamples
Dedicated book-length publication cards already existThese titles already have standalone cards under Publications and are the fastest way to orient a reader.The Hidden Pattern; Probabilistic Logic Networks; Engineering General Intelligence; Real World Reasoning
Important Ben Goertzel books already referenced elsewhere, but not yet broken out hereThese works are already named in +Papers and should be the next priority if book coverage is expanded.The Structure of Intelligence; Chaotic Logic; Artificial General Intelligence; OpenCog: A Software Framework for Integrative AGI; The AGI Revolution

Dedicated Book-Length Publication Cards

Important Ben Goertzel Books Not Yet Broken Out as Standalone Publication Cards

  • Goertzel (1993), The Structure of Intelligence: A New Mathematical Model of Mind — early mathematical formalization of patternist mind theory.
  • Goertzel (1994), Chaotic Logic: Language, Thought, and Reality from the Perspective of Complex Systems Science — logic and cognition as self-organizing dynamical process.
  • Goertzel, Pennachin (eds.) (2007), Artificial General Intelligence — early AGI field survey volume.
  • Goertzel, Hart (eds.) (2008), OpenCog: A Software Framework for Integrative Artificial General Intelligence — book-length OpenCog architectural snapshot.
  • Goertzel, Lian, Arel, de Garis, Chen (eds.) (2012), Theoretical Foundations of Artificial General Intelligence — formal foundations volume.
  • Goertzel (ed.) (2014), AGI: Concept, State of the Art, and Future Prospects — state-of-the-field overview.
  • Goertzel (2016), The AGI Revolution: An Inside View of the Rise of Artificial General Intelligence — strategic and historical overview.

Papers

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Coverage at a Glance

Bucket What it means Representative items
Standalone publication cards already exist These works already have dedicated cards under Publications and are linked throughout the sections below. The Hidden Pattern; Probabilistic Logic Networks; OpenCog Hyperon: A Framework for AGI at the Human Level and Beyond; Meta-MeTTa; MetaMo: A Robust Motivational Framework for Open-Ended AGI
Already represented in RawData or Publication Maps, but not yet promoted to standalone publication cards These papers are already in the wiki's research pipeline and remain good candidates for future publication-card promotion. A General Theory of General Intelligence; What Kind of Programming Language Best Suits Integrative AGI?; Patterns of Quantum Cognition II: Quantum Logic as a Foundation for AGI
Internal or prepublication design-note layer These items are valuable to technical readers, but they are not all conventional public papers and should be treated separately from the peer-reviewed / arXiv publication set. Hyperseed-1: Core Ontology; HyperClaw: Cognitive Orchestration via Attention-Metaprotocol

Investor / Overview Shortlist

Research Trajectory

Era Focus Key Publication
Philosophical (1993–2006) Patternist theory of mind The Hidden Pattern (2006)
Experimental (2006–2014) CogPrime cognitive engine Engineering General Intelligence (2014)
Foundational (2021–2023) MeTTa language and MORK substrate OpenCog Hyperon Whitepaper (2023)
Applied (2024–2025) Multi-domain AGI deployment AGI-25: PRIMUS, MetaMo, PLN/NARS papers
Orchestration (2026–) Hybrid AGI coordination HyperClaw: Cognitive Orchestration (2026)

Books

  • Goertzel (1993), The Structure of Intelligence: A New Mathematical Model of Mind — Springer. First formal treatment of the patternist philosophy of mind that underlies Hyperon's design.
  • Goertzel (1994), Chaotic Logic: Language, Thought, and Reality from the Perspective of Complex Systems Science — Plenum Press. Introduces logic as a dynamic, self-organizing process — a direct precursor to MeTTa's non-deterministic evaluation model.
  • Goertzel (2006), The Hidden Pattern: A Patternist Philosophy of Mind — BrownWalker Press. Foundational philosophy underlying AtomSpace and MeTTa knowledge representation designs.
  • Goertzel, Pennachin (eds.) (2007), Artificial General Intelligence — Springer. First systematic survey of the AGI research field.
  • Goertzel, Hart (eds.) (2008), OpenCog: A Software Framework for Integrative Artificial General Intelligence — Atlantis Press.
  • Goertzel, Lian, Arel, de Garis, Chen (eds.) (2012), Theoretical Foundations of Artificial General Intelligence — Atlantis Press.
  • Goertzel et al. (2009), Probabilistic Logic Networks — Springer. Full PLN formalism and applications.
  • Goertzel, Pennachin, Geisweiller (2014), Engineering General Intelligence, Vols. 1–2 — Atlantis Press. Full CogPrime architecture specification.
  • Goertzel (ed.) (2014), AGI: Concept, State of the Art, and Future Prospects — Collection of AGI direction essays.
  • Goertzel (2016), The AGI Revolution: An Inside View of the Rise of Artificial General Intelligence — Humanity+ Press.

Hyperon Yellow Papers (Formal Specifications)

Technical design documents and formal proofs defining core Hyperon components. Items marked [internal] are not yet publicly available.

  • Potapov (2021), MeTTa Specification — Formal language specification for Meta-Type Talk. [design doc]
  • Meta-MeTTa: an operational semantics for MeTTa — Meredith, Goertzel, Warrell, Vandervorst (2023). Formal semantics and MeTTa-to-Rholang compilation proof. arXiv:2305.17218. See Publication Map.
  • Graphs, Metagraphs, RAM, CPU — Vepstas (2023). AtomSpace metagraph formalism, content addressing, sheaf theory bridge. v2.1.1, 70pp.
  • A Selectivity Theorem and a Hierarchical Corollary — Goertzel (2025). Formal foundations for MORK/ZAM efficiency. See Publication Map.
  • Goertzel (2024), Hyperseed-1: Core Ontology — Ontological seeding specification for Hyperon knowledge bases. [design doc, internal]
  • Goertzel (2026), HyperClaw: Cognitive Orchestration via Attention-Metaprotocol — Design proposal for attention-based meta-layer coordination across Hyperon subsystems. [design doc]

Hyperon Era (2020–Present)

Architecture & Framework

  • OpenCog Hyperon: A Framework for AGI at the Human Level and Beyond — Goertzel et al. (2023). Primary architectural whitepaper. arXiv:2310.18318. See Publication Map.
  • OpenCog Hyperon: A Practical Path to Beneficial AGI and ASI — Goertzel (2025). AGI-25, Springer LNCS vol. 16057. [peer-reviewed]
  • Goertzel (2021), A General Theory of General Intelligence — Metagraph-theoretic AGI foundations. arXiv:2103.15100
  • Goertzel (2021), Reflective Metagraph Rewriting as a Foundation for an AGI "Language of Thought" — arXiv:2112.08272
  • Goertzel (2021), Patterns of Cognition: Cognitive Algorithms as Galois Connections Fulfilled by Chronomorphisms on Probabilistically Typed Metagraphs — arXiv:2102.10581
  • Goertzel (2023), Bridging AGI Theory and Practice with Galois Connections — AGI-23. Springer LNCS 13921. Theoretical sequel to the 2017 synergy paper, providing modern categorical formalization. [peer-reviewed]
  • Goertzel (2017), Toward a Formal Model of Cognitive Synergy — arXiv:1703.04361. The foundational paper defining synergy as a mathematical relationship for overcoming computational stuckness.

MeTTa Language & Formal Semantics

  • Meredith, Goertzel, Warrell, Vandervorst (2023), Meta-MeTTa: an operational semantics for MeTTa — arXiv:2305.17218. See Publication Map.
  • Warrell, Potapov, Vandervorst, Goertzel (2022), A Meta-Probabilistic-Programming Language for Bisimulation of Probabilistic and Non-Well-Founded Type Systems — AGI-22. arXiv:2203.15970 [peer-reviewed]
  • Potapov, Bogdanov (2022), Univalent Foundations of AGI are (not) All You Need — AGI-21. Springer LNCS 13154, pp. 184–195. [peer-reviewed]
  • Goertzel (2020), What Kind of Programming Language Best Suits Integrative AGI? — AGI-20. arXiv:2004.05267
  • Cody (2025), MeTTa-TMPAL: MeTTa-Based Architecture for a Self-writing Process Algebra of Learning — AGI-25. [peer-reviewed]

Knowledge Representation & AtomSpace

  • Vepstas (2023), Graphs, Metagraphs, RAM, CPU — AtomSpace metagraph formalism, content addressing, sheaf theory bridge. v2.1.1, 70pp.
  • Vepstas (2022), Purely Symbolic Induction of Structure — AGI-22. Springer LNCS 13539, pp. 134–144. [peer-reviewed]
  • Goertzel (2025), A Selectivity Theorem and a Hierarchical Corollary — Formal foundations for MORK/ZAM efficiency. See Publication Map.

Predictive Coding & Cognitive Architecture

Reasoning (PLN, NARS, NACE)

  • Geisweiller, Yusuf (2023), Probabilistic Logic Networks for Temporal and Procedural Reasoning — AGI-23. ResearchGate [peer-reviewed]
  • PLN and NARS Often Yield Similar strength × confidence Given Highly Uncertain Term Probabilities — Goertzel (2024). arXiv:2412.19524
  • Hammer, Isaev et al. (2024), Non-Axiomatic Reasoning for an Autonomous Mobile Robot — IEEE ICRA 2024, pp. 17079–17085. [peer-reviewed]
  • Isaev, Hammer (2025), NARS-GPT: An Integrated Reasoning System for Natural Language Interactions — IntelliSys 2025, Springer. [peer-reviewed]
  • Hammer, Isaev et al. (2023), Comparative Reasoning for Intelligent Agents — AGI-23. Springer LNCS 13921, pp. 126–135. [peer-reviewed]
  • Isaev, Hammer (2023), Memory System and Memory Types for Real-Time Reasoning Systems — AGI-23. Springer LNCS 13921, pp. 147–157. [peer-reviewed]
  • Johansson, Hammer, Lofthouse (2025), Arbitrarily Applicable Same/Opposite Relational Responding with NARS — AGI-25. [peer-reviewed]
  • Geisweiller, Yusuf (2023), Rational OpenCog Controlled Agent — AGI-23. Springer LNCS 13921, pp. 95–104. [peer-reviewed]

Motivation & MetaMo

Neural-Symbolic Integration & LLMs

Formal Verification & Mathematics

Evolutionary Learning (MOSES)

  • Looks (2006), Competent Program Evolution — Doctoral dissertation, Washington University in St. Louis.

Applied AGI Roadmap

Papers and proposals positioning Hyperon capabilities in specific real-world domains.

  • Finance: Goertzel (2026), Architecture of Automated Crypto-Finance Agent — Design proposal for ASI Alliance decentralized finance agent stack. [design doc]
  • Robotics: Hammer, Isaev et al. (2024), Non-Axiomatic Reasoning for an Autonomous Mobile Robot — IEEE ICRA 2024, pp. 17079–17085. [peer-reviewed]
  • Longevity: See Rejuve Bio for ongoing biomedical knowledge graph research applying Hyperon to longevity data pipelines.
  • Mathematics: Integrating Formal Verification into an AGI Cognitive Architecture — Z. Goertzel, B. Goertzel et al. (2025), AITP 2025. [peer-reviewed]
  • AI Alignment: Goertzel et al. (2025), The Elowyn: Quest of Time AI Alignment Framework — Internal whitepaper defining the alignment-via-strategic-play thesis for the EARTHwise partnership. See Elowyn. [internal doc]

OpenCog Legacy (2006–2014)

AGI Theory & Architecture

Reasoning (PLN & ECAN)

Motivation & Emotion (OpenPsi/MetaMo)

Natural Language Processing

Pattern Mining & Perception

Embodiment & Virtual Worlds

Evolutionary Learning (MOSES)

Background Resources


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Reference layers for researchers, editors, and AI agents working with the Hyperon Wiki. These cards provide cross-cutting indexes, repository catalogs, and source-to-card mappings that support the editorial process.

  1. Technical Companions
  2. Implementation Families
  3. Publication Maps
  4. GitHub Repositories



Discussion

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The Hyperon ecosystem encompasses the companies, research labs, and open-source projects building products and services on top of the Hyperon framework, MeTTa language, and the broader ASI Alliance infrastructure. Unlike a traditional software ecosystem organized around a single vendor, Hyperon's ecosystem reflects its origins in the OpenCog project — a decentralized research community where multiple independent organizations contribute to a shared AGI infrastructure.

At the organizational apex sits the ASI Alliance, formed in June 2024 through the merger of SingularityNET, Fetch.ai, and Ocean Protocol (which later withdrew). The Alliance provides the governance umbrella, the unified ASI token, and the ASI Chain blockchain runtime. Beneath this, TrueAGI serves as the dedicated engineering arm developing Hyperon's core — the MeTTa language, MORK kernel, and PLN reasoning — while F1R3FLY provides the formal semantics and blockchain execution layer through Rholang and the rho calculus.

Applied ecosystem partners build domain-specific products on this shared infrastructure: Rejuve in longevity and bioinformatics, Magi in AGI tooling and education, EARTHwise in sustainability knowledge systems, SophiaVerse in gamified AI worlds, and Mind Children and Hanson Robotics in social robotics. iCog Labs provides contract R&D across multiple domains, contributing 49+ repositories. NuNet and Singularity Finance handle decentralized compute and DeFi infrastructure respectively.

Contents

  1. ASI Alliance
  2. SingularityNET
  3. TrueAGI
  4. iCog Labs
  5. F1R3FLY
  6. Magi (Full)
  7. EARTHwise (Elowyn)
  8. SophiaVerse
  9. Rejuve
  10. Hanson Robotics
  11. Mind Children
  12. NuNet
  13. Singularity Finance
  14. Fetch.ai
  15. Ocean Protocol



Discussion

MeTTa Variants

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Approved by Anna on 2026-04-30

Responsible: Greg Meredith

GitHub / Documentation: https://github.com/F1R3FLY-io/MeTTa-Compiler

Description:

MeTTaTron is the F1R3FLY-native MeTTa compiler, providing a path from MeTTa into MeTTa-IL and serving as the MeTTa implementation most closely aligned with the F1R3FLY / ASI Chain execution stack. Within the broader Hyperon ecosystem, it represents an important route by which MeTTa programs can move toward lower-level runtime environments designed for concurrency, distributed execution, and blockchain-native settlement.

Where Hyperon Experimental functions as the reference implementation and PeTTa emphasizes high-performance symbolic execution, MeTTaTron is best understood as a compiler-oriented bridge between MeTTa source programs and the F1R3FLY-side execution model. This makes it especially relevant wherever MeTTa code must interoperate with MeTTa-IL, Rholang-adjacent infrastructure, or ASI Chain-facing runtime components. MeTTaTron ensures that MeTTa programs can compile downward into concrete, scalable, decentralized execution environments.



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