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..
Learning dynamic polynomial proofs
Authors: Alhussein Fawzi, Mateusz Malinowski, Hamza Fawzi, Omar Fawzi
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experimental results. We illustrate our dynamic proving approach on the stable set problem described in Section 2. This problem has been extensively studied in the polynomial optimization literature [Lau03]. We evaluate our method against standard linear programming hierarchies considered in this ๏ฌeld. |
| Researcher Affiliation | Collaboration | Alhussein Fawzi Deep Mind EMAIL Mateusz Malinowski Deep Mind EMAIL Hamza Fawzi University of Cambridge EMAIL Omar Fawzi ENS Lyon EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about open-source code availability or a link to a code repository for the methodology described. |
| Open Datasets | No | We train our prover on randomly generated graphs of size n = 25, where an edge between nodes i and j is created with probability p [0.5, 1]. |
| Dataset Splits | No | The paper mentions training on 'randomly generated graphs' and evaluating on a 'test set', but does not provide specific details about validation data splits or how the training, validation, and test sets are partitioned. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions using DQN and refers to existing proof systems but does not list any specific software dependencies with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | We restrict the number of steps in the dynamic proof to be at most 100 steps and limit the degree of any intermediate lemma to 2. |