离散视觉 token 首次追平连续表示
业界一直默认：把图像压成离散 token 必然丢信息，连续表示才是上限。这篇用多模态理解当尺子，发现量化误差大小跟理解好坏根本不挂钩——误差大的反而更聪明。真正决定水平的是 token 的比特预算，而现有量化器加再多码本也会饱和。他们用对称并行码本分别量化互补视角，在大预算下继续涨，最终在九项指标均值上追平了 Qwen3-VL 这类连续表示强模型，还首次在细粒度 OCR 上不落下风。这不是你明天能用的工具，但它把「离散表示天生低人一等」这个行业假设推翻了，给下一代原生多模态模型指了条路。
📄 原文摘要(英文)
Discrete visual tokens represent vision in a discrete form analogous to language, offering a native interface for multimodal modeling under a shared next-token prediction objective. However, quantization error is widely assumed to cause unavoidable semantic loss, fundamentally limiting the semantic fidelity of discrete visual tokens and positioning continuous visual representations as an intrinsic upper bound. Using multimodal understanding as a proxy for semantic fidelity, we revisit this assumption and find quantization error unreliable, as discrete representations with substantially larger errors can achieve better understanding. Instead, the effective bit budget of discrete indices emerges as a key determinant of downstream performance. However, existing quantizers eventually saturate as additional codebooks contribute progressively less semantic information. As one approach to alleviating this saturation, we introduce Factorized Vector Quantization, which quantizes complementary, fixed-dimensional views of the visual feature with symmetric parallel codebooks. It continues to improve at larger bit budgets, ultimately matching continuous visual representations in multimodal understanding. Trained on industrial-scale multimodal data, our model achieves state-of-the-art performance among discrete-representation models, outperforming UniAR and Emu3-Chat by 22.8% and 59.2%, respectively, on a nine-metric average. To our knowledge, it is also the first discrete-representation model competitive with strong continuous-representation models such as Qwen3-VL, including on fine-grained OCR tasks. We further identify the key principles and a practical recipe enabling this competitiveness. These results and insights support the potential of discrete visual representations to underpin large-scale native multimodal foundation models. Code and models are available at https://github.com/hustvl/FVQ