Paper collection on building and evaluating language model agents via executable language grounding

Reasoning Computers. Lambda Calculus, Fully Differentiable. Also Neural Stacks, Queues, Arrays, Lists, Trees, and Latches.

[ICML 2025] Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale

A curated paper list on neural symbolic and probabilistic logic.

mOWL: Machine Learning library with Ontologies

AIKA (Artificial Intelligence for Knowledge Acquisition) is an innovative approach to neural network design, diverging from traditional architectures that rely heavily on rigid matrix and vector operations. The AIKA Project introduces a flexible, sparse, and non-layered network representation, derived from a type hierarchy.

https://arxiv.org/abs/2312.10807

Neural-Grammar-Symbolic Learning with Back-Search

Explainable complex question answering over RDF files via Llama Index.

OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System.

Symbolic DNN-Tuner is a system to drive the training of a Deep Neural Network, analysing the performance of each training experiment and automatizing the choice of HPs to obtain a network with better performance.

Implementation of a new scenario for the Neural-Symbolic system NEUROLOG

NeuroLog: A Neural-Symbolic System

Jupyter notebooks with examples of Logical Neural Networks (LNN) by IBM

Neural-Symbolic-Superintelligence scaling

Compare CNN and KAN models on symbolic MNIST addition using DeepProbLog

Neural-Symbolic Model

NEural-symbolic Entity Reasoning and Matching in R

From DSL Bootstrap to Universal Reasoning: A framework that captures, evolves, and teaches meta-cognitive reasoning. Started as a simple experiment runner, evolved into a complete system achieving both symbolic rigor (76.5%) and neural adaptability (90%).

NEural-symbolic Entity Reasoning and Matching in Python