HYDRA: Unifying Multi-modal Generation and Understanding via Representation-Harmonized Tokenization

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• HYDRA-TOK reformulates the standard backbone into a progressive learner that transitions from a Gen-ViT, which captures structure-preserving primitives, to a Sem-ViT for semantic encoding.method 95%
• HYDRA sets a benchmark in visual reconstruction with rFID 0.08.result 95%
• HYDRA achieves top-tier generation performance on GenEval (0.86), DPG-Bench (86.4), and WISE (0.53).result 95%
• HYDRA outperforms previous native UMMs by an average of 10.0 points across eight challenging understanding benchmarks.result 95%
• Existing approaches typically compromise by employing decoupled encoders, stacking representation encoder atop VAEs, or utilizing discrete quantization, which often disrupt information coherence and lead to optimization conflicts.limitation 90%
• The Generation-Semantic Bottleneck (GSB) compresses features into a low-dimensional space to filter noise for robust synthesis, then restores dimensionality to empower complex semantic comprehension.technical 90%
• HYDRA is a native unified framework integrating perception and generation within a single parameter space.method 90%
• Unified Multimodal Models struggle to bridge the fundamental gap between the abstract representations needed for visual understanding and the detailed primitives required for generation.limitation 85%

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