Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Logic-Based Inductive Synthesis of Efficient Programs
Authors: Andrew Cropper
IJCAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 8,853 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,800 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results agree with the theoretical optimal predictions and show, for instance, that when learning to sort lists, Metagol O learns an efficient quick sort strategy, rather than an inefficient bubble sort strategy. |
| Researcher Affiliation | Academia | Andrew Cropper Imperial College London, United Kingdom |
| Pseudocode | No | The paper includes Prolog program examples in Figure 2, but these are not pseudocode or algorithm blocks for the research methodology itself. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper mentions learning from 'initial/final state examples' and 'a set of positive examples' and 'learning to sort lists', but it does not specify a publicly available dataset by name, provide a link, citation, or repository information for accessing any data used. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or references to standard splits). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions 'Prolog programs' and 'Metagol O', but it does not provide specific version numbers for any software, libraries, or dependencies used in the experiments. |
| Experiment Setup | No | The paper discusses the 'Metagol O' implementation and 'iterative descent' but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |