Structured Outputs
-
Updated
Aug 6, 2026 - Python
Structured Outputs
Torchhd is a Python library for Hyperdimensional Computing and Vector Symbolic Architectures
Neuro-symbolic interpretation learning (mostly just language-learning, for now)
Query language for blending SQL and local language models across structured + unstructured data, with type constraints.
Handwritten Equations Decipherment with Abductive Learning
An expert system using logic-based artificial intelligence and symbolic AI.
A simple Lisp written in Go
Kaleidoscope is an experimental cognitive architecture for emergent intelligence. It uses an E8 lattice physics engine and an RL-steered LLM to autonomously generate novel theories about complex systems. Features a visualization hub of its internal thought-space.
A JavaScript implementation of Douglas Hofstadter and Melanie Mitchell's Copycat program.
Wirewright is an experimental symbolic physics environment
Expert system with deductive querying and verification of constraints expressed in natural language
AGI runtime for bounded recursive self-awareness. Attempting machine consciousness at the Gödel–Turing–Hofstadter Nexus.
Meta Optimization Semantic Evolutionary Search
Ein: a tensor logic language unifying neural and symbolic AI
Code and data to the publication "SpikE: spike-based embeddings for multi-relational graph data".
Axiomatic intelligence. Doing the right thing, provably.
Navigate the dungeon, avoid the pits, find the gold, beware of the wumpus. Artificial intelligence based AI game.
A computational foundation for AGI based on hyperdimensional computing and set theory.
Artificial intelligence with a network of connected neurons
Add a description, image, and links to the symbolic-ai topic page so that developers can more easily learn about it.
To associate your repository with the symbolic-ai topic, visit your repo's landing page and select "manage topics."