QuantiMork

Responsible: Ben Goertzel

Papers: Hyperon for AGI⇒ASI Whitepaper (2025), §7.4

Status: Proposed. Research direction for deep neural-symbolic integration described in the 2025 whitepaper. Not yet implemented. Depends on MORK infrastructure capabilities (ByteFlow, ShardZipper) under active development.

QuantiMORK (stylized; wiki-canonical name: QuantiMork) proposes encoding neural network structures directly within MORK's prefix-tree database, rather than wrapping neural models as external components. The distinctive feature of the proposal is wavelet or multiresolution DAG encoding inside the MORK PathMap — representing neural structures not as flat tensors but as hierarchically decomposed, multiresolution objects native to MORK's trie topology.

Core Idea

Where Hyperon's "outside" neural integration wraps existing models as queryable Spaces, QuantiMORK proposes a more radical "inside" approach: neural network components would become native graph objects in AtomSpace, subject to the same attention allocation, pattern mining, and reasoning processes that operate on symbolic content.

The key technical proposal is encoding weight matrices and activation patterns as wavelet-structured paths in MORK's prefix tree. This multiresolution representation means that coarse features live near the trie root (cheap to access) while fine details live deeper (accessed only when needed), enabling natural hierarchical approximation and progressive refinement during both inference and learning.

Proposed Architecture

• Neural networks as graph operations: Forward passes, weight updates, and activation propagation expressed as graph traversals within MORK, making neural processing transparent to symbolic reasoning.
• 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.
• ByteFlow block packing: Dense numerical data stored in MORK's ByteFlow format — adaptive block packing optimized for GPU/TPU acceleration, living alongside symbolic atoms in the same trie structure.

Relationship to Other Components

The whitepaper describes QuantiMORK interacting with WILLIAM (identifying compression-worthy features within neural representations), weakness theory (regularization ensuring neural updates don't destabilize symbolic knowledge), and TransWeave (transferring learned neural representations to other paradigms).

Key References

• Goertzel, B. (2025). Hyperon for AGI⇒ASI Whitepaper, §7.4: Inside Atomspace — The QuantiMORK Approach