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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.

Dynamic Tangled Derivative Logic of Metric Spaces

Authors: David Fernández-Duque, Yoàv Montacute

AAAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 17,381 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,678 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.

Reproducibility Variable Result LLM Response
Research Type Theoretical We show that the resulting logics are decidable and have a natural axiomatisation. Moreover, we prove that these logics are complete for interpretations on the Cantor space, the rational numbers, and subspaces thereof.
Researcher Affiliation Academia 1University of Barcelona 2University of Cambridge
Pseudocode No The paper does not contain structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide concrete access to source code for the methodology described.
Open Datasets No The paper is theoretical and does not discuss datasets for training or evaluation.
Dataset Splits No The paper is theoretical and does not provide specific dataset split information.
Hardware Specification No The paper is theoretical and does not provide specific hardware details for running experiments.
Software Dependencies No The paper is theoretical and does not provide specific ancillary software details with version numbers.
Experiment Setup No The paper is theoretical and does not contain specific experimental setup details.