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..

An Optimized Franz-Parisi Criterion and its Equivalence with SQ Lower Bounds

Authors: Siyu Chen, Theodor Misiakiewicz, Ilias Zadik, Peiyuan Zhang

NeurIPS 2025 | Venue PDF | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical There are no experimental results, as this is a theory paper.
Researcher Affiliation Academia Siyu Chen Department of Statistics and Data Science Yale University EMAIL Theodor Misiakiewicz Department of Statistics and Data Science Yale University EMAIL Ilias Zadik Department of Statistics and Data Science Yale University EMAIL Peiyuan Zhang Department of Statistics and Data Science Yale University EMAIL
Pseudocode No The paper discusses various algorithms and methods conceptually (e.g., "low-degree polynomial (LDP) lower bounds", "Statistical Query (SQ) lower bounds", "local algorithms such as Langevin or Glauber dynamics"), but it does not present any pseudocode or algorithm blocks with structured steps in the main text or appendices.
Open Source Code No There are no experimental results, as this is a theory paper.
Open Datasets No There are no experimental results, as this is a theory paper.
Dataset Splits No There are no experimental results, as this is a theory paper.
Hardware Specification No There are no experimental results, as this is a theory paper.
Software Dependencies No There are no experimental results, as this is a theory paper.
Experiment Setup No There are no experimental results, as this is a theory paper.