# Analogical Reasoning Within a Conceptual Hyperspace

arXiv: https://arxiv.org/abs/2411.08684 · HTML: https://mlsanity.com/paper/2411.08684/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 8 of its 45 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 8 of 8)

- [A Neuro-vector-symbolic Architecture for Solving Raven's Progressive Matrices](https://mlsanity.com/paper/2203.04571/) (2022)
- [Computing on Functions Using Randomized Vector Representations](https://mlsanity.com/paper/2109.03429/) (2021)
- [Using Counterfactual Tasks to Evaluate the Generality of Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2402.08955/) (2024)
- [Vector Symbolic Architectures as a Computing Framework for Emerging Hardware](https://mlsanity.com/paper/2106.05268/) (2021)
- [Neural Analogical Matching](https://mlsanity.com/paper/2004.03573/) (2020)
- [Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representation](https://mlsanity.com/paper/1701.00464/) (2017)
- [Hyperdimensional Computing with Spiking-Phasor Neurons](https://mlsanity.com/paper/2303.00066/) (2023)
- [Efficient Off-Policy Reinforcement Learning via Brain-Inspired Computing](https://mlsanity.com/paper/2205.06978/) (2022)

## Abstract (arXiv)

We propose an approach to analogical inference that marries the neuro-symbolic computational power of complex-sampled hyperdimensional computing (HDC) with Conceptual Spaces Theory (CST), a promising theory of semantic meaning. CST sketches, at an abstract level, approaches to analogical inference that go beyond the standard predicate-based structure mapping theories. But it does not describe how such an approach can be operationalized. We propose a concrete HDC-based architecture that computes several types of analogy classified by CST. We present preliminary proof-of-concept experimental results within a toy domain and describe how it can perform category-based and property-based analogical reasoning.