Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Turing Completeness of Bounded-Precision Recurrent Neural Networks
Authors: Stephen Chung, Hava Siegelmann
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that a 54-neuron bounded-precision RNN with growing memory modules can simulate a Universal Turing Machine, with time complexity linear in the simulated machine s time and independent of the memory size. The result is extendable to various other stack-augmented RNNs. Furthermore, we analyze the Turing completeness of both unbounded-precision and boundedprecision RNNs, revisiting and extending the theoretical foundations of RNNs. |
| Researcher Affiliation | Academia | Stephen Chung Department of Computer Science University of Massachusetts Amherst Amherst, MA 01003 EMAIL Hava Siegelmann Department of Computer Science University of Massachusetts Amherst Amherst, MA 01003 EMAIL |
| Pseudocode | No | The paper provides mathematical formulations and descriptions of processes, but no structured pseudocode or algorithm blocks are present. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code or provide links to a code repository. The discussion section mentions that 'Since growing memory modules are not differentiable, we cannot train them directly by the frequently used error backpropagation algorithm.' |
| Open Datasets | No | The paper is theoretical and does not describe empirical experiments involving datasets or training. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments involving dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specific hardware. |
| Software Dependencies | No | The paper is theoretical and does not describe any computational experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, hyperparameters, or training configurations. |