MMSkills: Towards Multimodal Skills for General Visual Agents
In the rapidly evolving field of artificial intelligence, the development of reusable skills has emerged as a pivotal element in enhancing the capabilities of visual agents. A recent paper, titled “MMSkills: Towards Multimodal Skills for General Visual Agents,” introduces a groundbreaking framework aimed at addressing the complexities associated with multimodal procedural knowledge. The study, available on arXiv under the identifier 2605.13527v1, outlines a comprehensive approach to equipping visual agents with skills that transcend traditional learning methods.
The authors argue that while existing skill packages often rely on textual prompts, executable code, or learned routines, these methods fall short in the context of visual agents. The intricacies of procedural knowledge necessitate a multimodal approach, which involves not only determining the operations to perform but also interpreting visual cues related to the agent’s environment. This multifaceted nature of skills leads to the formalization of multimodal procedural knowledge, which the paper aims to explore through three core challenges:
- What should a multimodal skill package contain?
- Where can these skill packages be derived from public interaction experiences?
- How can agents utilize multimodal evidence during inference without excessive reliance on image context or reference screenshots?
To tackle these challenges, the authors introduce MMSkills, a framework designed for the representation, generation, and application of reusable multimodal procedures that facilitate runtime visual decision-making. Each MMSkill consists of a compact, state-conditioned package that integrates both a textual procedure and essential visual components, including runtime state cards and multi-view keyframes.
The methodology for constructing these skill packages involves an innovative agentic trajectory-to-skill Generator. This generator is capable of transforming publicly available non-evaluation trajectories into reusable multimodal skills. The process includes:
- Workflow Grouping: Organizing tasks into coherent workflows to streamline skill development.
- Procedure Induction: Deriving structured procedures from the grouped workflows.
- Visual Grounding: Anchoring the skills to visual representations of the environment.
- Meta-skill-guided Auditing: Ensuring the quality and applicability of the generated skills.
Once these multimodal skill packages are created, the framework introduces a branch-loaded multimodal skill agent. This agent inspects selected state cards and keyframes within a temporary branch that aligns with the current state of the environment. This real-time inspection allows the agent to distill structured guidance, which enhances the main agent’s decision-making capabilities.
Experimental results from various benchmarks, including graphical user interfaces (GUIs) and game-based environments, indicate that the MMSkills framework consistently improves the performance of both cutting-edge and smaller multimodal agents. These findings suggest that the integration of external multimodal procedural knowledge serves as a valuable complement to the existing internal model priors.
In conclusion, the MMSkills framework presents a significant advancement in the development of visual agents capable of leveraging multimodal skills for enhanced decision-making. By addressing the challenges of procedural knowledge through a structured approach, this research paves the way for more intelligent and adaptable AI systems in various applications.
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