As user experience (UX) increasingly shapes the value and usability of digital products, enabling non-designers to transform their ideas into coherent UX outcomes remains an important yet challenging problem. Despite advances in AI-assisted tools, UX design still requires cross-stage reasoning across strategic, structural, and interface layers, which is often unsupported by existing systems. To address this gap, this study proposes MAUX, a layered multi-agent UX framework that structures and coordinates design reasoning through goal-driven workflows. MAUX operationalizes the five classical UX layers (strategy, scope, structure, skeleton, and surface) via LLM-driven agents grounded in human–computer interaction principles. A blackboard architecture coordinated by a Meta-Agent maintains semantic consistency across stages, enabling systematic progression from initial intent to interface realization. The framework is demonstrated through the design of Urban AirLink, a conceptual low-altitude mobility platform, and evaluated against four LLM-based baselines in a controlled user study ( N = 50 ). Results show that MAUX significantly improves design quality, structural clarity, and cross-layer goal alignment compared to baseline approaches. These findings highlight the potential of MAUX to support reasoning-centered and democratized AI-assisted UX design.

We present ViTaM-D, a novel visual-tactile framework for reconstructing dynamic hand-object interaction with distributed tactile sensing to enhance contact modeling. Existing methods, relying solely on visual inputs, often fail to capture occluded interactions and object deformation. To address this, we introduce DF-Field, a distributed force-aware contact representation leveraging kinetic and potential energy in hand-object interactions. ViTaM-D first reconstructs interactions using a visual network with contact constraint, then refines contact details through force-aware optimization, improving object deformation modeling. To evaluate deformable object reconstruction, we introduce the HOT dataset, featuring 600 hand-object interaction sequences in a high-precision simulation environment. Experiments on DexYCB and HOT datasets show that ViTaM-D outperforms state-of-the-art methods in reconstruction accuracy for both rigid and deformable objects. DF-Field also proves more effective in refining hand poses and enhancing contact modeling than previous refinement methods. The code, models, and datasets are available at https://sites.google.com/view/vitam-d/.