NeurIPS 2025 Papers with Code & Data

2025-11-13 · Source: Resources | Paper Digest · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing, Computer Vision · Depth: Expert, extended

Summary

This document provides an extensive index of accepted papers for the NeurIPS 2025 conference, scheduled to be held in San Diego starting December 2nd. The compilation focuses on papers that include associated public code or data repositories, generated through an automated extraction process. While efforts were made for completeness, some resources might be missing. The index covers a wide array of machine learning topics, including large language models, diffusion models, reinforcement learning, computer vision, and federated learning. Readers are also encouraged to explore related resources from Paper Digest, such as curated summaries of NeurIPS 2025 highlights and a historical overview of influential NeurIPS papers since 1987.

Key takeaway

For AI Researchers and Scientists seeking to implement or build upon the latest NeurIPS 2025 findings, prioritize papers listed with public code and data. This direct access to implementations can significantly accelerate your research and development cycles, allowing for immediate experimentation and validation of novel techniques. Always verify the public availability of code as some repositories may become accessible only at the conference start.

Key insights

NeurIPS 2025 papers with public code/data span diverse ML areas, emphasizing open science and practical implementation.

Principles

• Open science accelerates community engagement.
• Automated extraction facilitates large-scale indexing.
• Reproducibility is enhanced by shared code and data.

Method

Automated extraction identifies papers with public code/data repositories. Manual reporting is encouraged for missed entries. The index is a living document, with some code becoming public at conference start.

In practice

• Access code and data for NeurIPS 2025 research directly from the provided links.
• Contribute to the index by reporting any missed papers with public resources.
• Explore related curated summaries and historical analyses for broader context.

Topics

• Large Language Models
• Diffusion Models
• Reinforcement Learning
• Multimodal AI
• Model Efficiency

Code references

• facebookresearch/perception_models
• qiuzh20/gated_attention
• showlab/Show-o
• MoonshotAI/MoBA
• facebookresearch/luckmatters

Best for: AI Researcher, AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Resources | Paper Digest.