LLMs can see and hear without any training
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Abstract (arXiv)
We present MILS: Multimodal Iterative LLM Solver, a surprisingly simple, training-free approach, to imbue multimodal capabilities into your favorite LLM. Leveraging their innate ability to perform multi-step reasoning, MILS prompts the LLM to generate candidate outputs, each of which are scored and fed back iteratively, eventually generating a solution to the task. This enables various applications that typically require training specialized models on task-specific data. In particular, we establish a new state-of-the-art on emergent zero-shot image, video and audio captioning. MILS seamlessly applies to media generation as well, discovering prompt rewrites to improve text-to-image generation, and even edit prompts for style transfer! Finally, being a gradient-free optimization approach, MILS can invert multimodal embeddings into text, enabling applications like cross-modal arithmetic.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Jan 2025.
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Citing papers per year: 2025: 5; 2026: 3.
8 papers.
- 2025Learning to See Before Seeing: Demystifying LLM Visual Priors from Language Pre-trainingcited by 23
- 2025It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Modelscited by 11
- 2025Words That Make Language Models Perceivecited by 7
- 2025Exploiting Mixture-of-Experts Redundancy Unlocks Multimodal Generative Abilitiescited by 3
- 2026DUALVISION: RGB-Infrared Multimodal Large Language Models for Robust Visual Reasoningcited by 1
- 2025No Concept Left Behind: Test-Time Optimization for Compositional Text-to-Image Generationcited by 0
- 2026Personalizing Text-to-Image Generation to Individual Tastecited by 0
- 2026LINE: LLM-based Iterative Neuron Explanations for Vision Modelscited by 0
- 2026DUALVISION: RGB-Infrared Multimodal Large Language Models for Robust Visual Reasoning20 Apr 2026
- 2026LINE: LLM-based Iterative Neuron Explanations for Vision Models9 Apr 2026
- 2026Personalizing Text-to-Image Generation to Individual Taste8 Apr 2026
- 2025It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Models31 Dec 2025
- 2025Words That Make Language Models Perceive2 Oct 2025
- 2025Learning to See Before Seeing: Demystifying LLM Visual Priors from Language Pre-training30 Sep 2025
- 2025No Concept Left Behind: Test-Time Optimization for Compositional Text-to-Image Generation27 Sep 2025
- 2025Exploiting Mixture-of-Experts Redundancy Unlocks Multimodal Generative Abilities28 Mar 2025
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- 2024LLMs Meet Multimodal Generation and Editing: A Survey
- 2026Bi-Level Prompt Optimization for Multimodal LLM-as-a-Judge
- 2025LLM-I: LLMs are Naturally Interleaved Multimodal Creators
- 2023Prompt Highlighter: Interactive Control for Multi-Modal LLMs
- 2024Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs
- 2024MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
- 2025Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs
- 2025Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey