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The paper introduces ZENDAYA, a closed-form bandwidth dial for streaming multimodal decoders that dynamically adjusts input reading to improve real-time text generation performance. It demonstrates that reducing input consumption can enhance quality and efficiency in streaming settings across video and audio benchmarks.
Introduces multi-reference image-grounded video captioning and proposes RefCaptioner, a two-stage post-training framework with mixed-data SFT and hierarchical coverage-discounted GRPO. The paper also presents MRVBench, a benchmark for evaluating caption factuality and multi-reference grounding.
Introduces PEEK, an efficient dynamic frame sampling method that distills caption-conditioned frame relevance rankings from a teacher model into a lightweight temporal model, outperforming state-of-the-art methods in video captioning while maintaining computational efficiency.
NemoStation/Marlin-2B is a fine-tuned model based on Qwen3.5-2B for video-text-to-text tasks, supporting video captioning and temporal grounding.