Knowledge Representations
This section details Atomspace technologies, the representational core of Hyperon’s neural-symbolic approach. In this context, “Atoms” represent symbolic data and formal categories that allow the system to store not just raw data, but the relationships and logic behind it. Systems such as DAS and MORK enable a dynamic knowledge metagraph where code and data are interchangeable, and where neural or sub-symbolic processes can deposit, reshape, and retrieve structured representations through ongoing system dynamics. This allows AI to perform complex queries and self-modifications across a distributed network, treating its own internal logic as a queryable and improvable data structure rather than a static symbolic store.
DAS (Distributed AtomSpace)
DAS is a high-speed, dynamic memory for the Hyperon AGI framework. It operates as a distributed knowledge system for massive, mutable hypergraphs. Unlike conventional relational databases, which organize data into static tables, DAS represents information as nodes and links in a generalized hypergraph. Because links can connect not only nodes but other links, higher-order logic and nested relationships can be expressed directly in the graph itself. In this sense, DAS functions not just as storage, but as an active cognitive memory layer.
DAS serves as a shared cognitive substrate in which perceptions, inferred relations, learned abstractions, goals, and executable structures can all be deposited into a common metagraph and made available to one another.
DAS decouples the persistence of knowledge (Long-Term Importance stored in distributed backends) from the immediate dynamics of attention (Short-Term Importance managed in high-speed RAM). This separation is governed by the Attention Broker, which mitigates combinatorial explosions inherent in graph traversal — critical bottlenecks where exploring dense networks triggers unmanageable, exponential cascades of computation. To prevent this, the Broker implements a resource-allocation mechanism based on Hebbian learning. Before an inference query is executed, the system performs an activation spreading cycle, distributing tokens to “heat up” only the contextually relevant atoms. This dynamically constrains the search space to the most relevant atoms — functionally replicating the efficiencies of limited working memory within biological systems.
Repositories
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MORK (MeTTa Optimized Reduction Kernel)
MORK is a high-performance hypergraph engine for Hyperon. Designed as a specialized, in-RAM processing kernel, it targets the core operations of symbolic AI — pattern matching and graph reduction — through a trie-based data structure that supports efficient intersection of structured symbolic patterns. Its selectivity theorem and hierarchical corollary, formalizing when trie-indexed intersection outperforms WAM-style backtracking, are documented in MORK theory publication map.
The secret to this speed lies in how MORK physically organizes data. While a standard graph database scatters nodes and links across memory like a tangled ball of yarn, MORK organizes them into a highly optimized Trie-Map (Radix Tree) structure. Imagine a dictionary that doesn't store “Apple,” “Apply,” and “Application” separately, but stores “Appl-” once and branches off efficiently. MORK does this for cognitive data: it compresses shared patterns and nested relationships into a structured, crystalline hierarchy. This allows its zipper-based multi-threaded virtual machine to navigate (“zip”) up and down complex reasoning paths with near-instant access, eliminating the slow pointer chasing that plagues traditional graph databases and ensuring that even the most massive knowledge graphs can be traversed in real-time.
MORK’s architecture frames interoperability through a mechanism known as “sinking”: delegating specialized workloads, such as numerical operations, to external optimized libraries. The earlier WebAssembly-specific implementation path has been superseded; current implementation details are covered in Status and Resources.
Architecture (Bottom-Up)
Repositories
Papers & Publications
- Triemaps that Match — Simon Peyton Jones et al.
- CZ2 Scaling Experiments — internal Scala prototype
- Interacting Trie-Maps — internal Scala proof-of-concept
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