Multimodal LLMs Do Not Compose Skills Optimally Across Modalities

• URL: http://arxiv.org/abs/2511.08113v2
• Date: Thu, 13 Nov 2025 01:33:46 GMT
• Title: Multimodal LLMs Do Not Compose Skills Optimally Across Modalities
• Abstract summary: We study the ability of Multimodal Large Language Models to compose skills across modalities.<n>We find that all evaluated MLLMs exhibit a significant cross-modality skill composition gap.
• Score: 7.4508858982186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Skill composition is the ability to combine previously learned skills to solve new tasks. As neural networks acquire increasingly complex skills during their pretraining, it is not clear how successfully they can compose them. In this paper, we focus on Multimodal Large Language Models (MLLM), and study their ability to compose skills across modalities. To this end, we design three evaluation tasks which can be solved sequentially composing two modality-dependent skills, and evaluate several open MLLMs under two main settings: i) prompting the model to directly solve the task, and ii) using a two-step cascaded inference approach, which manually enforces the composition of the two skills for a given task. Even with these straightforward compositions, we find that all evaluated MLLMs exhibit a significant cross-modality skill composition gap. To mitigate the aforementioned gap, we explore two alternatives: i) use chain-of-thought prompting to explicitly instruct MLLMs for skill composition and ii) a specific fine-tuning recipe to promote skill composition. Although those strategies improve model performance, they still exhibit significant skill composition gaps, suggesting that more research is needed to improve cross-modal skill composition in MLLMs.

Related papers

• Generative Skill Composition for LLM Agents [58.47191648555256]
  LLM agents benefit from skills for solving complex tasks.<n>As skill libraries grow, selecting an appropriate skill composition has emerged as a central bottleneck.<n>We propose SkillComposer, which instantiates structured skill composition as task-conditioned skill sequence prediction.
  arXiv  Detail & Related papers  (2026-06-30T17:53:09Z)
• Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agents [9.144810798419975]
  LLM agents increasingly retrieve externally curated skills-procedural instructions retrieved at decision time to improve performance on long-horizon interactive tasks.<n>We propose MASA Model-Aware Skill Alignment, a framework that adapts skills to each target backbone without modifying agent weights.<n> MASA consistently achieves the best overall performance, with gains of up to 25.8 points over the strongest baseline.
  arXiv  Detail & Related papers  (2026-05-29T01:34:42Z)
• SkillGraph: Skill-Augmented Reinforcement Learning for Agents via Evolving Skill Graphs [70.1970574147839]
  We propose SKILLGRAPH, a framework that represents reusable skills as nodes in a directed graph.<n>SKILLGRAPH retrieves not just individual skills, but an ordered skill subgraph that can guide multi-step decision making.<n> Experiments show that SKILLGRAPH achieves state-of-the-art performance against memory-augmented RL methods.
  arXiv  Detail & Related papers  (2026-05-12T12:21:49Z)
• Advancing Compositional LLM Reasoning with Structured Task Relations in Interactive Multimodal Communications [42.945657927971]
  This paper presents a novel paradigm that accomplishes various IMAs using a single compositional LLM over wireless networks.<n>To tackle the first challenge, we propose ContextLoRA, a novel method that guides an LLM to learn the rich structured context among IMAs.<n>Experiments on three benchmarks show the superiority of the proposed ContextLoRA and ContextGear.
  arXiv  Detail & Related papers  (2025-07-28T09:33:12Z)
• LLM2CLIP: Powerful Language Model Unlocks Richer Visual Representation [72.02635550088546]
  This work explores how large language models (LLMs) can enhance CLIP's capability, especially for processing longer and more complex image captions.<n>We introduce a caption-to-caption contrastive fine-tuning framework, significantly enhancing the discriminative quality of LLM outputs.<n>Our approach outperforms LoRA-based methods, achieving nearly fourfold faster training with superior performance.
  arXiv  Detail & Related papers  (2024-11-07T18:59:16Z)
• LLMs Can Evolve Continually on Modality for X-Modal Reasoning [62.2874638875554]
  Existing methods rely heavily on modal-specific pretraining and joint-modal tuning, leading to significant computational burdens when expanding to new modalities. We propose PathWeave, a flexible and scalable framework with modal-Path sWitching and ExpAnsion abilities. PathWeave performs comparably to state-of-the-art MLLMs while concurrently reducing parameter training burdens by 98.73%.
  arXiv  Detail & Related papers  (2024-10-26T13:19:57Z)
• LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks [73.09643674975591]
  Low-Rank Adaptation (LoRA) is a technique for parameter-efficient fine-tuning of Large Language Models (LLMs)<n>We study how different LoRA modules can be merged to achieve skill composition.
  arXiv  Detail & Related papers  (2024-10-16T20:33:06Z)
• Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability [12.349247962800813]
  Large language models (LLMs) have emerged as powerful tools for many AI problems. They exhibit remarkable in-context learning (ICL) capabilities. How they approach composite tasks remains an open and largely underexplored question.
  arXiv  Detail & Related papers  (2024-07-22T15:22:34Z)
• NoteLLM-2: Multimodal Large Representation Models for Recommendation [71.87790090964734]
  Large Language Models (LLMs) have demonstrated exceptional proficiency in text understanding and embedding tasks.<n>Their potential in multimodal representation, particularly for item-to-item (I2I) recommendations, remains underexplored.<n>We propose an end-to-end fine-tuning method that customizes the integration of any existing LLMs and vision encoders for efficient multimodal representation.
  arXiv  Detail & Related papers  (2024-05-27T03:24:01Z)
• Agentic Skill Discovery [19.5703917813767]
  Language-conditioned robotic skills make it possible to apply the high-level reasoning of Large Language Models (LLMs) to low-level robotic control. A remaining challenge is to acquire a diverse set of fundamental skills. We introduce a novel framework for skill discovery that is entirely driven by LLMs.
  arXiv  Detail & Related papers  (2024-05-23T19:44:03Z)
• Small LLMs Are Weak Tool Learners: A Multi-LLM Agent [73.54562551341454]
  Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs. We propose a novel approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer. This modular framework facilitates individual updates and the potential use of smaller LLMs for building each capability.
  arXiv  Detail & Related papers  (2024-01-14T16:17:07Z)
• Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models [68.18370230899102]
  We investigate how to elicit compositional generalization capabilities in large language models (LLMs) We find that demonstrating both foundational skills and compositional examples grounded in these skills within the same prompt context is crucial. We show that fine-tuning LLMs with SKiC-style data can elicit zero-shot weak-to-strong generalization.
  arXiv  Detail & Related papers  (2023-08-01T05:54:12Z)
• CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models [74.22729793816451]
  Large Language Models (LLMs) have made significant progress in utilizing tools, but their ability is limited by API availability. We propose CREATOR, a novel framework that enables LLMs to create their own tools using documentation and code realization. We evaluate CREATOR on MATH and TabMWP benchmarks, respectively consisting of challenging math competition problems.
  arXiv  Detail & Related papers  (2023-05-23T17:51:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.