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..
Analyzing the Power of Chain of Thought through Memorization Capabilities
Authors: Lijia Yu, Xiao-Shan Gao, Lijun Zhang
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our contributions are theoretical research. |
| Researcher Affiliation | Academia | 1Institute of AI for Industries, Chinese Academy of Sciences 2SKLMS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences 3University of Chinese Academy of Sciences 4Key Laboratory of System Software of Chinese Academy of Sciences |
| Pseudocode | No | The paper includes mathematical definitions, theorems, lemmas, and proofs, but no clearly labeled pseudocode or algorithm blocks describing a computational procedure. |
| Open Source Code | No | Our contributions are theoretical research. |
| Open Datasets | No | Our contributions are theoretical research. |
| Dataset Splits | No | The paper does not describe any experimental datasets or their splits, as it is theoretical research. It discusses conceptual 'finite reasoning datasets' and 'infinite reasoning datasets'. |
| Hardware Specification | No | Our contributions are theoretical research. |
| Software Dependencies | No | Our contributions are theoretical research. |
| Experiment Setup | No | Our contributions are theoretical research. |