arXiv:2606.03005v1 Announce Type: cross Abstract: Despite rapid progress, multimodal large language models (MLLMs) still fail on tasks that humans solve effortlessly, such as navigating a grid maze from a screenshot or selecting the correct puzzle piece. Rather than retraining the model, we ask a complementary question: how much capability can be elicited from a frozen MLLM purely by improving the execution scaffold around it? We introduce MUSE, a multimodal unified structured execution harness that wraps any off-the-shelf MLLM with composable modules for task representation, visual processing
The rapid advancement of MLLMs coupled with their current limitations on complex tasks necessitates innovation in execution frameworks to unlock their full potential.
This development represents a significant step towards enabling MLLMs to perform sophisticated agentic tasks, moving beyond mere conversational or generative capabilities.
Instead of focusing solely on model retraining for performance improvement, the emphasis shifts to optimizing the surrounding execution environment, making current MLLMs more practically useful.
- · AI developers and researchers
- · Companies deploying MLLMs
- · Industries requiring complex task automation
- · Framework and tools providers for MLLMs
- · Models reliant solely on internal improvements for performance gains
- · Companies without strategies for agentic AI integration
Existing MLLMs become capable of performing a wider range of challenging, multi-step tasks that previously required human intervention.
This improved capability leads to faster adoption of MLLMs in various industries, automating complex workflows and decision-making processes.
The increased utility and autonomy of MLLMs accelerate the development and deployment of sophisticated AI agents, reshaping white-collar work and numerous service sectors.
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Read at arXiv cs.AI