# Conceptors: an easy introduction

arXiv: https://arxiv.org/abs/1406.2671 · HTML: https://mlsanity.com/paper/1406.2671/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2014.

## Built on by (most cited first, 4 of 4)

- [Steering Large Language Models using Conceptors: Improving Addition-Based Activation Engineering](https://mlsanity.com/paper/2410.16314/) (2024)
- [Integrating Symbolic Reasoning into Neural Generative Models for Design Generation](https://mlsanity.com/paper/2310.09383/) (2023)
- [Fast Classification Learning with Neural Networks and Conceptors for Speech Recognition and Car Driving Maneuvers](https://mlsanity.com/paper/2102.05588/) (2021)
- [When a Conceptor Complement Becomes Gain: Operator Geometry and Behavioral Attribution in Language Models](https://mlsanity.com/paper/2610.16325/) (2026)

## Abstract (arXiv)

Conceptors provide an elementary neuro-computational mechanism which sheds a fresh and unifying light on a diversity of cognitive phenomena. A number of demanding learning and processing tasks can be solved with unprecedented ease, robustness and accuracy. Some of these tasks were impossible to solve before. This entirely informal paper introduces the basic principles of conceptors and highlights some of their usages.