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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.

Realizability of Three-Valued Semantics for Abstract Dialectical Frameworks

Authors: Jörg Pührer

IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 16,388 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,253 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 investigate fundamental properties of threevalued semantics for abstract dialectical frameworks (ADFs). In particular, we deal with realizability, i.e., the question whether there exists an ADF that has a given set of interpretations as its semantics. We provide necessary and sufficient conditions that hold for a set of three-valued interpretations whenever there is an ADF realizing it under admissible, complete, grounded, or preferred semantics. Moreover, we discuss how to construct such an ADF in case of realizability. Our results lay the ground for studying the expressiveness of ADFs under three-valued semantics. As a first application we study implications of our results on the existence of certain join operators on ADFs. Our results are based on constructive proofs and we discuss how to obtain an ADF realizing a set V whenever V is realizable.
Researcher Affiliation Academia J org P uhrer Leipzig University Leipzig, Germany EMAIL
Pseudocode No The paper does not contain any structured pseudocode or algorithm blocks.
Open Source Code No The paper does not contain any statement about releasing source code or providing a link to it.
Open Datasets No This is a theoretical paper focusing on mathematical properties and proofs, and as such, it does not involve the use of datasets for training or experimentation.
Dataset Splits No This is a theoretical paper focusing on mathematical properties and proofs, and as such, it does not involve dataset splits for training, validation, or testing.
Hardware Specification No This is a theoretical paper and does not describe any experimental setup that would require hardware specifications.
Software Dependencies No This is a theoretical paper and does not describe any experimental setup that would require software dependencies with version numbers.
Experiment Setup No This is a theoretical paper and does not describe any experimental setup, hyperparameters, or training configurations.