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..
Disentangling Reafferent Effects by Doing Nothing
Authors: Benedict Wilkins, Kostas Stathis
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Evaluation Alg. 1 is applied to three different environments. To demonstrate the effectiveness and general applicability of our approach, we perform a number of experiments. Each showcases, or has a parallel with, an important concept or experiment performed in related fields. |
| Researcher Affiliation | Academia | Benedict Wilkins, Kostas Stathis Department of Computer Science, Royal Holloway University of London EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Estimating Effects via SGD |
| Open Source Code | Yes | All code and data is publicly available1. 1https://github.com/Benedict Wilkins/disentangling-reafference |
| Open Datasets | Yes | Alg. 1 is applied to three different environments. ... (i) Cartpole ... (ii) Atari Freeway ... (iii) Artificial Ape ... (Wilkins and Stathis 2022). |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions software components like 'neural network' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | Training and model details for each experiment, as well as additional experiments are presented in the supplementary material. The main text does not provide specific hyperparameters or system-level training settings. |