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.
Hyperon's architecture spans four layers, each documented in detail within this wiki:
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.
Hyperon bridges symbolic and neural paradigms through two complementary approaches:
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.
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.
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.
The original OpenCog system, now sometimes called "OpenCog Classic," was built in C++, Scheme, and Python around several core components:
These components were deployed in virtual agent control (OpenCogBot in virtual worlds), humanoid robotics (Hanson Robotics integration), and biological knowledge exploration.
Hyperon preserves the core intellectual commitments of OpenCog:
Hyperon is not an incremental update but a ground-up redesign motivated by hard-won lessons from a decade of OpenCog development:
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.
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.
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.
Hyperon's architecture is engineered to enable cognitive synergy through three mechanisms:
Some of the most important synergistic interactions designed into Hyperon include:
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.
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.
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.
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:
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:
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.
Atoms carry associated values beyond their structural content:
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.
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.
Hyperon provides two complementary modes of neural-symbolic integration, suited to different stages of system maturity:
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:
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.
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:
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.
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.
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 whitepaper describes a disciplined pipeline for self-modification:
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.
The design envisions certain principles enforced globally across all cognitive processes:
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.