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 EXECUTION THROUGH NEURAL CODE FUSION
Authors: Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As an illustration of this, we apply the new model to challenging dynamic tasks (branch prediction and prefetching) from the SPEC CPU benchmark suite, outperforming the state-of-the-art by 26% and 45% respectively. |
| Researcher Affiliation | Collaboration | Zhan Shi The University of Texas at Austin EMAIL Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi Google Research EMAIL |
| Pseudocode | No | The paper does not contain a clearly labeled "Pseudocode" or "Algorithm" block or figure. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We use SPECint 2006 to evaluate our proposal. This is a standard benchmark suite commonly used to evaluate hardware and software system performance. (Sta, 2006) |
| Dataset Splits | Yes | We train the model on each benchmark independently. The ο¬rst 70% of snapshots are used for training, and the last 30% for evaluation. ... These are split into 30 for training, 10 for validation (tuning the linear SVM described below) and 10 for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory amounts, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper mentions software tools like gcc, GNU binary utilities, and Pin, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | The hyperparameters for all models are given in Table 1. input feature size 64 hidden size 64 propagation steps 5 optimizer adam learning rate 0.01 |