Is ChatGPT getting a more visual interface, and what can multimodal models do?

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1. 1

   MM-LLMs: Recent Advances in MultiModal Large Language Models

   Research paper (Zhang et al.)Published Jan 1, 2024Checked Oct 12, 2026

   “In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effective training strategies.The resulting models not only preserve the inherent reasoning and decision-making capabilities of LLMs but also empower a diverse range of MM tasks.In this paper, we provide a comprehensive survey aimed at facilitating further research on MM-LLMs.Initially, we outline general design formulations for model architecture and training pipeline.Subsequently, we introduce a taxonomy encompassing 126 MM-LLMs, each characterized by its specific formulations.Furthermore, we review the performance of selected MM-LLMs on mainstream benchmarks and summarize key training recipes to enhance the potency of MM-LLMs.Finally, we explore promising directions for MM-LLMs while concurrently maintaining a real-time tracking website 1 for the latest developments in the field.We hope that this survey contributes to the ongoing advancement of the MM-LLMs domain.”

2. 2

   A survey on multimodal large language models

   National Science Review (Yin et al.)Published Nov 11, 2024Checked Oct 12, 2026

   “The surprising emergent capabilities of the MLLM, such as writing stories based on images and optical character recognition-free math reasoning, are rare in traditional multimodal methods, suggesting a potential path to artificial general intelligence. To this end, both academia and industry have endeavored to develop MLLMs that can compete with or even outperform GPT-4V, pushing the limit of research at a surprising speed. In this paper, we aim to trace and summarize the recent progress of MLLMs. First, we present the basic formulation of the MLLM and delineate its related concepts, including architecture, training strategy and data, as well as evaluation. Then, we introduce research topics about how MLLMs can be extended to support more granularity, modalities, languages and scenarios. We continue with multimodal hallucination and extended techniques, including multimodal in-context learning, multimodal chain of thought and LLM-aided visual reasoning. To conclude the paper, we discuss existing challenges and point out promising research directions.”

3. 3

   Visual cognition in multimodal large language models

   Nature Machine Intelligence (Buschoff et al.)Published Jan 15, 2025Checked Oct 12, 2026

   “Researchers have asserted these models’ limitations in the domains of causal reasoning, intuitive physics and intuitive psychology. Yet recent advancements, namely the rise of large language models, particularly those designed for visual processing, have rekindled interest in the potential to emulate human-like cognitive abilities. This paper evaluates the current state of vision-based large language models in the domains of intuitive physics, causal reasoning and intuitive psychology. Through a series of controlled experiments, we investigate the extent to which these modern models grasp complex physical interactions, causal relationships and intuitive understanding of others’ preferences. Our findings reveal that, while some of these models demonstrate a notable proficiency in processing and interpreting visual data, they still fall short of human capabilities in these areas. Our results emphasize the need for integrating more robust mechanisms for understanding causality, physical dynamics and social cognition into modern-day, vision-based language models, and point out the importance of cognitively inspired benchmarks.”

4. 4

   Efficient multimodal large language models: a survey

   Visual Intelligence (Jin et al.)Published Dec 1, 2025Checked Oct 12, 2026

   “In the past years, multimodal large language models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering and visual understanding and reasoning. However, the extensive model size and high training and inference costs have hindered the widespread application of MLLMs in academia and industry. Thus, studying efficient and lightweight MLLMs has enormous potential, especially in edge computing scenarios. In this survey, we provide a comprehensive and systematic review of the current state of efficient MLLMs. Specifically, this survey summarizes the timeline of representative efficient MLLMs, the current state of research in structures and strategies, and the applications. Finally, the limitations of current efficient MLLM research and promising future directions are discussed.”

5. 5

   The Revolution of Multimodal Large Language Models: A Survey

   Research paper (Caffagni et al.)Published Jan 1, 2024Checked Oct 12, 2026

   “Davide Caffagni, Federico Cocchi, Luca Barsellotti, Nicholas Moratelli, Sara Sarto, Lorenzo Baraldi, Lorenzo Baraldi, Marcella Cornia, Rita Cucchiara. Findings of the Association for Computational Linguistics: ACL 2024. 2024.”