# Multimodal & Large Language Models

A personal, actively updated reading list of research papers on multimodal and large language models, organized into dozens of topic sections from alignment and reasoning to hallucination and world models.

Multimodal & Large Language Models is a running list of research papers the maintainer reads from the daily arXiv feed in artificial intelligence, computation and language, computer vision, and machine learning, kept for personal reference and shared publicly. Papers are organized into a large table of contents covering topics such as surveys, planning, reasoning, generation, representation learning, LLM safety and evaluation, long-context models, scaling laws, alignment, reward modeling, vision-language foundation models, image and video generation, hallucination and factuality, and world models.

The maintainer states they only record papers believed to offer unique insight, rather than a complete record of the field, and invites others to point out missing important work through an issue.

The material does not describe any accompanying code, tool or software; it is a curated bibliography.

Website: https://github.com/Yangyi-Chen/Multimodal-AND-Large-Language-Models