Multimodal large models are moving from "split understanding and generation" toward "unified architecture." A recent Mamoda2.5 paper on arXiv showcases the latest progress in this direction: a unified framework fusing an autoregressive (AR) understanding module with a Diffusion Transformer (DiT) generation module — for the first time, multimodal understanding and content generation within a single architecture.

Technical core: DiT-MoE architecture

Mamoda2.5's biggest highlight is the introduction of Mixture-of-Experts (MoE) into Diffusion Transformer. It uses a 128-expert Top-8 routing mechanism, with 25B total parameters but only 3B activated per inference — the sparsity design significantly compresses compute cost while preserving enough model capacity. Notably, the team also adopts an upcycling initialization strategy from Dense models to MoE, accelerating convergence.

Video editing: surpassing Kling O1

On the VBench 2.0 benchmark, Mamoda2.5 achieves top-tier generation performance, particularly setting new records on video editing tasks, surpassing Kuaishou's Kling O1 on OpenVE-Bench. Traditional video editing models typically need 30 iterative steps, while Mamoda2.5, through joint few-step distillation + RL, compresses the inference steps to 4, boosting inference speed 95.9×. It has already been deployed in real advertising scenarios, with content review and creative restoration success rates as high as 98%.

Commentary

Mamoda2.5's significance lies in the intersection of three technical threads: MoE's sparse-compression, Diffusion Transformer's unified generation paradigm, and few-step distillation's inference acceleration. This is highly consistent with the industry's pursuit of "stronger capability + lower latency," and signals that the core of multimodal model competition in 2026 won't be parameter scale, but sparse-routing efficiency and distillation compression ratio. With the help of such efficient architectures, small and mid teams are poised to approach closed-source large-model experience in specific vertical scenarios.