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..
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
| 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. |