Kling team details the multimodal understanding stack behind its video model

The researchers behind Kling have published a technical report on arXiv arguing that video generation quality now hinges more on semantic control than on rendering, and walking through the understanding stack they built around that idea. The report introduces Kling-MLLM, a shared multimodal foundation model that handles image, video, audio, synchronized audio-video and interleaved inputs, with specializations for each downstream module. It covers captioning treated as a dense semantic contract, prompt enhancement refined with reinforcement learning, paired reward models for critique and fast scoring, and automatic evaluation protocols designed to expose scoring shortcuts. The paper is on arXiv; no code or model weights are announced in the abstract.