ThinkDiff-SLM proposes an approximately one-billion-parameter model for industrial digital twins: an entropy-aware masking schedule, a continuous latent planner, a SigLIP vision encoder, and two task-specific output heads. The design aims to refine multiple output positions in parallel while keeping simulation and language outputs tied to a shared representation. The supplied revised manuscript describes a prototype built from LLaDA-8B and reports projected accuracy, throughput, memory, and ablation values. It does not establish a fully validated one-billion-parameter Jetson deployment or peer-reviewed publication.

ThinkDiff-SLM · supplied revised manuscript

Masked diffusion and the entropy-aware schedule

Latent planner and its theoretical framing

Vision features and task-selected heads

Complexity, prototype setup, and evidence disclosure

Projected ablations and remaining limitations

Large Language Diffusion Models · LLaDA

InfoDiffusion: Information Entropy Aware Diffusion Process

Reinforcing the Diffusion Chain of Lateral Thought

A proposed masked-diffusion model that connects digital-twin state generation and operator questions through a shared backbone, with a latent planner and visual context. This explanation separates the architecture from its projected edge-performance results.

ThinkDiff-SLM: A Thinking-Capable Diffusion Small Language Model for Unified Digital Twin Simulation and Edge Chatbot Deployment

The method, the evidence, and what remains open.

ThinkDiff-SLM: A Thinking-Capable Diffusion Small Language Model for Unified Digital Twin Simulation and Edge Chatbot Deployment