mlsanityData to 14 Oct 2026

LLMs can see and hear without any training

arXiv 2501.18096 ↗First posted 30 Jan 2025cs.CV cs.AI cs.CLcited by 8

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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.

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  1. 2024LLMs Meet Multimodal Generation and Editing: A Survey
  2. 2026Bi-Level Prompt Optimization for Multimodal LLM-as-a-Judge
  3. 2025LLM-I: LLMs are Naturally Interleaved Multimodal Creators
  4. 2023Prompt Highlighter: Interactive Control for Multi-Modal LLMs
  5. 2024Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs
  6. 2024MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
  7. 2025Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs
  8. 2025Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey