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..
Edit Distance Robust Watermarks via Indexing Pseudorandom Codes
Authors: Noah Golowich, Ankur Moitra
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As this paper is theoretical in nature, we work only with this abstraction of an autoregressive model |
| Researcher Affiliation | Academia | Noah Golowich EMAIL MIT Ankur Moitra EMAIL MIT |
| Pseudocode | Yes | Algorithm 1 Indexing PRC: PRCIdx[PRCSub, ρ], Algorithm 2 PRF-PRC[F, p, q]: Generic PRF-based PRC, Algorithm 3 Watermarking from PRCs for general alphabets: W[PRC, Model] |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code. |
| Open Datasets | No | The paper is theoretical and does not use or mention any specific training datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, hyperparameters, or training configurations. |