OpenNovelty.org

Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.6Download Report PDF
Multimodal Large Language ModelsMultimodal Prompt OptimizationPrompt Optimization
Abstract:

Large Language Models (LLMs) have shown remarkable success, and their multimodal expansions (MLLMs) further unlock capabilities spanning images, videos, and other modalities beyond text. However, despite this shift, prompt optimization approaches, designed to reduce the burden of manual prompt crafting while maximizing performance, remain confined to text, ultimately limiting the full potential of MLLMs. Motivated by this gap, we introduce the new problem of multimodal prompt optimization, which expands the prior definition of prompt optimization to the multimodal space defined by the pairs of textual and non-textual prompts. To tackle this problem, we then propose the Multimodal Prompt Optimizer (MPO), a unified framework that not only performs the joint optimization of multimodal prompts through alignment-preserving updates but also guides the selection process of candidate prompts by leveraging earlier evaluations as priors in a Bayesian-based selection strategy. Through extensive experiments across diverse modalities that go beyond text, such as images, videos, and even molecules, we demonstrate that MPO outperforms leading text-only optimization methods, establishing multimodal prompt optimization as a crucial step to realizing the potential of MLLMs.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces multimodal prompt optimization as a new problem formulation and proposes the Multimodal Prompt Optimizer (MPO) framework to jointly optimize textual and non-textual prompts. Within the taxonomy, it resides in the Search-Based Prompt Optimization leaf under Prompt Optimization Methods, alongside only two sibling papers: one focused on interpretable optimization and another using LLMs as meta-optimizers. This leaf represents a relatively sparse research direction compared to denser areas like Soft Prompt Tuning for Vision-Language Models, which contains four papers, suggesting the paper enters a less crowded but emerging subfield.

The taxonomy reveals that the broader Prompt Optimization Methods branch includes three distinct approaches: gradient-based soft prompt tuning, adapter-based learning, and search-based optimization. The paper's search-based approach contrasts with neighboring gradient-driven methods in Automated Prompt Learning and Tuning, which focus on continuous embeddings rather than discrete prompt discovery. The taxonomy's scope note clarifies that search-based methods distinguish themselves by avoiding learnable parameters, instead relying on algorithmic exploration. This positioning suggests the work diverges from the dominant gradient-based paradigm while connecting to the broader goal of reducing manual prompt engineering effort.

Among thirty candidates examined through semantic search, none were found to clearly refute any of the three core contributions. The Multimodal Prompt Optimization Problem formulation examined ten candidates with zero refutable overlaps, as did the MPO Framework and the Prior-Inherited Bayesian UCB Selection Strategy. This absence of refutation within the limited search scope suggests that the specific combination of multimodal joint optimization, alignment-preserving updates, and Bayesian-guided selection may represent a novel synthesis. However, the modest search scale means that more extensive prior work could exist beyond the top-thirty semantic matches examined.

Based on the limited literature search covering thirty candidates, the work appears to occupy a relatively unexplored intersection of multimodal prompting and search-based optimization. The sparse population of its taxonomy leaf and the lack of refutable prior work within the examined scope suggest potential novelty, though the analysis does not capture exhaustive coverage of the field. The contribution's distinctiveness may lie in its unified treatment of multiple modalities rather than individual technical components.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: multimodal prompt optimization for large language models. The field has evolved into a rich ecosystem organized around several complementary branches. Prompt Optimization Methods encompasses search-based and gradient-driven techniques that automatically refine prompts, while Prompt Engineering Strategies focuses on manual design principles and structured reasoning chains such as Duty-distinct Chain-of-Thought[5]. Domain-Specific Applications tailor prompts to specialized contexts like medical imaging (Prompt Engineering Glaucoma[19]) and remote sensing (EarthMarker Visual Prompting[10]), whereas Architectural Enhancements introduce learnable adapters (Prompt-aware Adapter[4]) and token-level modifications (Adaptive Visual Tokens[7]). Robustness and Safety addresses adversarial concerns including typographic injection (AgentTypo Typographic Injection[34]) and cross-modal attacks (Cross-modal Prompt Injection[36]), while Cross-Task and Cross-Modal Generalization explores transfer mechanisms (Transfer Visual Prompt[32]) and any-to-any frameworks (NExT-GPT Any-to-Any[20]). Surveys and Theoretical Foundations provide overarching perspectives (Visual Prompting Survey[18], Foundation Models Evolution[37]). Within the search-based optimization branch, a particularly active line of work contrasts automated discovery methods with interpretable, human-in-the-loop approaches. Multimodal Prompt Optimization[0] sits squarely in this automated search space, emphasizing algorithmic strategies to navigate the prompt design landscape efficiently. Nearby, Interpretable Prompt Optimization[1] prioritizes transparency and user control, trading some automation for explainability, while Large Language Models Optimizers[42] leverages LLMs themselves as meta-optimizers to iteratively refine prompts. This cluster reveals a fundamental trade-off: fully automated methods like Multimodal Prompt Optimization[0] can explore vast search spaces rapidly, but interpretable alternatives such as Interpretable Prompt Optimization[1] offer clearer insights into why certain prompts succeed. Open questions remain about balancing exploration efficiency with human oversight, and whether hybrid frameworks can combine the scalability of search-based techniques with the trust and adaptability of interactive refinement.

Claimed Contributions

Multimodal Prompt Optimization Problem

The authors formalize a novel problem that extends automatic prompt optimization from text-only to multimodal settings, where prompts consist of paired textual and non-textual components (e.g., images, videos, molecules). This expansion aims to fully leverage the capabilities of Multimodal Large Language Models.

10 retrieved papers
Multimodal Prompt Optimizer (MPO) Framework

The authors introduce MPO, a unified optimization framework with two key components: alignment-preserving exploration that jointly updates textual and non-textual prompts using cohesive feedback and complementary operators (generation, edit, mix), and a prior-inherited Bayesian-UCB selection strategy that efficiently identifies high-performing prompts by leveraging parent prompt performance as informative priors.

10 retrieved papers
Prior-Inherited Bayesian UCB Selection Strategy

The authors propose a novel candidate selection mechanism that warm-starts the evaluation of child prompts by inheriting performance information from their parent prompts as informative priors in a Bayesian UCB framework, reducing evaluation budget while improving selection accuracy in the enlarged multimodal search space.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Multimodal Prompt Optimization Problem

The authors formalize a novel problem that extends automatic prompt optimization from text-only to multimodal settings, where prompts consist of paired textual and non-textual components (e.g., images, videos, molecules). This expansion aims to fully leverage the capabilities of Multimodal Large Language Models.

Contribution

Multimodal Prompt Optimizer (MPO) Framework

The authors introduce MPO, a unified optimization framework with two key components: alignment-preserving exploration that jointly updates textual and non-textual prompts using cohesive feedback and complementary operators (generation, edit, mix), and a prior-inherited Bayesian-UCB selection strategy that efficiently identifies high-performing prompts by leveraging parent prompt performance as informative priors.

Contribution

Prior-Inherited Bayesian UCB Selection Strategy

The authors propose a novel candidate selection mechanism that warm-starts the evaluation of child prompts by inheriting performance information from their parent prompts as informative priors in a Bayesian UCB framework, reducing evaluation budget while improving selection accuracy in the enlarged multimodal search space.

Table of Contents