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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
A Logic for Expressing Log-Precision Transformers
Authors: William Merrill, Ashish Sabharwal
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 17,565 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,048 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 prove any log-precision transformer classifier can be equivalently expressed as a first-order logic sentence that, in addition to standard universal and existential quantifiers, may also contain majority-vote quantifiers. This is the tightest known upper bound and first logical characterization of log-precision transformers. |
| Researcher Affiliation | Collaboration | William Merrill New York University EMAIL Ashish Sabharwal Allen Institute for AI EMAIL |
| Pseudocode | Yes | Algorithm 1 node C(n, i) Return the type of gate i in circuit Cn. Algorithm 2 edge C(n, i, j) If Cn contains an edge i ! j, return the argument number of that edge. Otherwise, return 1. |
| Open Source Code | No | The paper does not mention any open-source code for the methodology described. |
| Open Datasets | No | The paper does not discuss or provide access information for any dataset used for training, as it is a theoretical paper. |
| Dataset Splits | No | The paper does not provide information about training/validation/test dataset splits, as it is a theoretical work without empirical evaluations. |
| Hardware Specification | No | The paper does not provide specific hardware details, as it is a theoretical work without experimental setup. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers, as it is a theoretical work without experimental setup. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (e.g., hyperparameter values), as it is a theoretical work without an empirical setup. |