# Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size

arXiv: https://arxiv.org/abs/2301.08190 · HTML: https://mlsanity.com/paper/2301.08190/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 61 references that are in the map.
- First posted on arXiv 19 Jan 2023.

## Builds on (most cited first, 20 of 20)

- [The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic](https://mlsanity.com/paper/1804.01508/) (2018)
- [The Convolutional Tsetlin Machine](https://mlsanity.com/paper/1905.09688/) (2019)
- [On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators](https://mlsanity.com/paper/2007.14268/) (2020)
- [Explainable Tsetlin Machine framework for fake news detection with credibility score assessment](https://mlsanity.com/paper/2105.09114/) (2021)
- [Coalesced Multi-Output Tsetlin Machines with Clause Sharing](https://mlsanity.com/paper/2108.07594/) (2021)
- [Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling](https://mlsanity.com/paper/2009.04861/) (2020)
- [Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability](https://mlsanity.com/paper/2005.05131/) (2020)
- [On the Convergence of Tsetlin Machines for the XOR Operator](https://mlsanity.com/paper/2101.02547/) (2021)
- [Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier](https://mlsanity.com/paper/2011.08755/) (2020)
- [Tsetlin Machine Embedding: Representing Words Using Logical Expressions](https://mlsanity.com/paper/2301.00709/) (2023)
- [Learning Logistic Circuits](https://mlsanity.com/paper/1902.10798/) (2019)
- [Low-Power Audio Keyword Spotting using Tsetlin Machines](https://mlsanity.com/paper/2101.11336/) (2021)
- [Enhancing Interpretable Clauses Semantically using Pretrained Word Representation](https://mlsanity.com/paper/2104.06901/) (2021)
- [Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines](https://mlsanity.com/paper/2105.04708/) (2021)
- [Tsetlin Machine for Solving Contextual Bandit Problems](https://mlsanity.com/paper/2202.01914/) (2022)
- [A Relational Tsetlin Machine with Applications to Natural Language Understanding](https://mlsanity.com/paper/2102.10952/) (2021)
- [A Novel Multi-Step Finite-State Automaton for Arbitrarily Deterministic Tsetlin Machine Learning](https://mlsanity.com/paper/2007.02114/) (2020)
- [On the Convergence of Tsetlin Machines for the AND and the OR Operators](https://mlsanity.com/paper/2109.09488/) (2021)
- [A Comparison Between Tsetlin Machines and Deep Neural Networks in the Context of Recommendation Systems](https://mlsanity.com/paper/2212.10136/) (2022)
- [Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine](https://mlsanity.com/paper/2203.04378/) (2022)

## Built on by (most cited first, 14 of 14)

- [Adversarial Attacks on AI-Generated Text Detection Models: A Token Probability-Based Approach Using Embeddings](https://mlsanity.com/paper/2501.18998/) (2025)
- [TMComposites: Plug-and-Play Collaboration Between Specialized Tsetlin Machines](https://mlsanity.com/paper/2309.04801/) (2023)
- [An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification Accelerator](https://mlsanity.com/paper/2501.19347/) (2025)
- [Scalable Multi-phase Word Embedding Using Conjunctive Propositional Clauses](https://mlsanity.com/paper/2501.19018/) (2025)
- [Contracting Tsetlin Machine with Absorbing Automata](https://mlsanity.com/paper/2310.11481/) (2023)
- [Runtime Tunable Tsetlin Machines for Edge Inference on eFPGAs](https://mlsanity.com/paper/2502.07823/) (2025)
- [Omni TM-AE: A Scalable and Interpretable Embedding Model Using the Full Tsetlin Machine State Space](https://mlsanity.com/paper/2505.16386/) (2025)
- [Uncertainty Quantification in the Tsetlin Machine](https://mlsanity.com/paper/2507.04175/) (2025)
- [Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning](https://mlsanity.com/paper/2310.02005/) (2023)
- [Green Tsetlin Redefining Efficiency in Tsetlin Machine Frameworks](https://mlsanity.com/paper/2405.04212/) (2024)
- [Fuzzy-Pattern Tsetlin Machine](https://mlsanity.com/paper/2508.08350/) (2025)
- [LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines](https://mlsanity.com/paper/2604.12223/) (2026)
- [FastOmniTMAE: Parallel Clause Learning for Scalable and Hardware-Efficient Tsetlin Embeddings](https://mlsanity.com/paper/2605.06982/) (2026)
- [Clusters are All You Need: Pre-Training the Tsetlin Machine with Semantic Clusters from Language Models for Interpretability](https://mlsanity.com/paper/2606.19815/) (2026)

## Abstract (arXiv)

Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, large clause pools tend to produce clauses with many literals (long clauses). As such, they become less interpretable. Further, longer clauses increase the switching activity of the clause logic in hardware, consuming more power. This paper introduces a novel variant of TM learning - Clause Size Constrained TMs (CSC-TMs) - where one can set a soft constraint on the clause size. As soon as a clause includes more literals than the constraint allows, it starts expelling literals. Accordingly, oversized clauses only appear transiently. To evaluate CSC-TM, we conduct classification, clustering, and regression experiments on tabular data, natural language text, images, and board games. Our results show that CSC-TM maintains accuracy with up to 80 times fewer literals. Indeed, the accuracy increases with shorter clauses for TREC, IMDb, and BBC Sports. After the accuracy peaks, it drops gracefully as the clause size approaches a single literal. We finally analyze CSC-TM power consumption and derive new convergence properties.