VDC-Agent: When Video Detailed Captioners Evolve Themselves via Agentic Self-Reflection

Existing Video Detailed Captioning (VDC) methods pre-dominantly rely on costly human annotations or distillation from pow-erful proprietary models, creating a dependency on external supervision.In this paper, we propose VDC-Agent, an autonomous self-evolvingframework that empowers a single Multimodal Large Language Model(MLLM) to generate and refine high-quality captions through principle-guided self-reflection. To overcome the inference latency inherent in it-erative refinement, we further propose to internalize this reflective ca-pability into the model. Specifically, we construct VDC-Agent-19K, apreference dataset derived from the agent’s self-scored trajectories, andintroduce a Curriculum Direct Preference Optimization (DPO) strategy.This strategy leverages the quality gap between generated candidates toprogressively align the model from easy to hard samples. Extensive ex-periments demonstrate that VDC-Agent achieves state-of-the-art perfor-mance on VDC and DREAM-1K benchmarks, generating captions withsuperior detail and faithfulness. Crucially, our internalization strategyretains the inference efficiency of the base model while significantly en-hancing its generalization capabilities, as validated by both quantitativemetrics and human evaluation.