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The Thinking Reward Model (TRM), a paradigm that explicitly determines what matters for each case before judging how well the candidate performs, is proposed, which formulates case-adaptive rubrics, performs rubric-guided assessment, and produces fine-grained pointwise rewards.
This work introduces HiRAE (Hierarchical Representation Autoencoder), which learns a hierarchical fusion framework over the full encoder hierarchy to improve reconstruction fidelity while maintaining compatibility with generative modeling.
Human evaluation confirms that RefCaptioner achieves the best overall performance among the open-source models while remaining competitive on standard video captioning benchmarks, and human evaluation further confirms that its captions are preferred by annotators and enable more source-faithful video reconstruction wit...
MultiRef-Compass is introduced, a unified benchmark for MR2AV generation that integrates automatic metrics with a rejudging-enhanced MLLM-as-a-Judge framework, enabling scalable and auditable evaluation of both perceptual fidelity and reference-conditioned composition.
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