arXiv AI
The Emergent Symbolic Structure of Artificial Neural Networks
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Aug 10
Mathematical Principles and Experimental Discoveries of the Emergence of Symbolic Patterns in Artificial Neural Networks
arXiv:2608. 06839v1 Announce Type: new Abstract: Artificial Neural networks (ANNs) are often treated as black-box models, making explainability a central challenge in deep learning.
arXiv AI
Jul 7
The Changing Role of Symbolic Methods in Artificial Intelligence
arXiv:2607. 05168v1 Announce Type: new Abstract: Why do intelligent systems need to perform explicit symbolic reasoning?
arXiv AI
Aug 7
Symbol Grounding in Neuro-Symbolic AI: A Gentle Introduction to Reasoning Shortcuts
arXiv:2510. 14538v3 Announce Type: replace Abstract: Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.
arXiv AI
2d ago
Non-Zero Radius: The Missing Component of Neural Networks
The paper proposes Sphere Neural Networks (SphNNs), which replace the conventional zero‑radius vector representation with spheres of non‑zero radius to better capture biological cognition and enable symbolic reasoning. By relaxing the zero‑radius constraint, the authors argue that concepts can be represented as spatial regions, allowing reasoning through model construction and inspection. SphNNs employ a transition map that achieves Aristotelian syllogistic reasoning without training data, aiming to address the black‑box nature and symbolic reasoning limitations of current neural networks.
arXiv AI
Oct 16
Interpreting Language Model Parameters
The paper introduces adVersarial Parameter Decomposition (VPD), a method that splits a language model’s parameters into simple subcomponents, each responsible for a small part of the model’s algorithm. VPD optimizes these decompositions so that the model’s input‑output behavior remains intact even when many subcomponents are removed, including adversarially chosen ones. This approach enables a new form of circuit analysis by tracing how sequences of parameter subcomponents interact to produce outputs for specific inputs.
arXiv AI
Aug 6
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
arXiv:2608. 04285v1 Announce Type: new Abstract: Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention.