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.
On Learning Verifiers and Implications to Chain-of-Thought Reasoning
Authors: Maria-Florina Balcan, Avrim Blum, Zhiyuan Li, Dravyansh Sharma
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 29,175 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 1,874 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 | The main contribution is theoretical. |
| Researcher Affiliation | Academia | Maria-Florina Balcan Carnegie Mellon University EMAIL Avrim Blum TTIC EMAIL Zhiyuan Li TTIC EMAIL Dravyansh Sharma TTIC, Northwestern University EMAIL |
| Pseudocode | Yes | Algorithm 1 Intersection of Consistent Verifiers |
| Open Source Code | No | The main contribution is theoretical. |
| Open Datasets | No | The main contribution is theoretical. |
| Dataset Splits | No | The main contribution is theoretical. |
| Hardware Specification | No | The main contribution is theoretical. |
| Software Dependencies | No | The main contribution is theoretical. |
| Experiment Setup | No | The main contribution is theoretical. |