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..
Looped Transformers as Programmable Computers
Authors: Angeliki Giannou, Shashank Rajput, Jy-Yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris Papailiopoulos
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | One limitation is that the constructions presented in this paper have not been experimentally validated for efficiency. |
| Researcher Affiliation | Academia | 1University of Wisconsin-Madison 2Yonsei University 3Princeton University. |
| Pseudocode | Yes | Algorithm 1 Looped Transformer |
| Open Source Code | Yes | The code is available at https://github.com/jysohn1108/Looped_TF |
| Open Datasets | No | The paper does not conduct experiments involving datasets, focusing instead on theoretical constructions and emulation capabilities. |
| Dataset Splits | No | The paper does not describe experiments with data splits (train/validation/test), as its focus is on theoretical framework and emulation. |
| Hardware Specification | No | The paper focuses on theoretical constructions and proofs regarding transformer capabilities and does not provide details on specific hardware used for experiments. |
| Software Dependencies | No | The paper describes a theoretical framework and its constructions but does not specify software dependencies with version numbers for reproducibility of experiments. |
| Experiment Setup | No | The paper presents theoretical work on constructing transformer-based computational units and does not include details on experimental setups or hyperparameters. |