Update README.md

README.md

@@ -1,3 +1,43 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- muiltimodal
+- discrete-flow-matching
+- unifed-model
+---
+
+## 1. Introduction
+The rapid progress of large language models (LLMs) has catalyzed the emergence of multimodal large language models (MLLMs) that unify visual understanding and image generation within a single framework. However, most existing MLLMs rely on autoregressive (AR) architectures, which impose inherent limitations on future development, such as the raster-scan order in image generation and restricted reasoning abilities in causal context modeling. In this work, we challenge the dominance of AR-based approaches by introducing **FUDOKI**, a unified multimodal model purely based on discrete flow matching, as an alternative to conventional AR paradigms. By leveraging metric-induced probability paths with kinetic optimal velocities, our framework goes beyond the previous masking-based corruption process, enabling iterative refinement with self-correction capability and richer bidirectional context integration during generation. To mitigate the high cost of training from scratch, we initialize FUDOKI from pre-trained AR-based MLLMs and adaptively transition to the discrete flow matching paradigm. Experimental results show that FUDOKI achieves performance comparable to state-of-the-art AR-based MLLMs across both visual understanding and image generation tasks, highlighting its potential as a foundation for next-generation unified multimodal models. Furthermore, we show that applying test-time scaling techniques to FUDOKI yields significant performance gains, further underscoring its promise for future enhancement through reinforcement learning.
+
+<table>
+  <tr>
+    <td width="25%"><img src="asset/teaser.png" alt="image"></td>
+    <td width="30%"><img src="asset/understanding.gif" alt="image"></td>
+    <td width="29%"><img src="asset/generation.gif" alt="image"></td>
+  </tr>
+</table>
+
+[FUDOKI: Discrete Flow-based Unified Understanding and Generation via Kinetic-Optimal Velocities](https://arxiv.org/abs/2505.20147v1).
+
+
+## 2. Quick Start
+
+Please refer to [**Github Repository**](https://github.com/fudoki-hku/FUDOKI)
+
+## 3. Citation
+
+```
+@article{wang2025fudokidiscreteflowbasedunified,
+    title={FUDOKI: Discrete Flow-based Unified Understanding and Generation via Kinetic-Optimal Velocities}, 
+    author={Jin Wang and Yao Lai and Aoxue Li and Shifeng Zhang and Jiacheng Sun and Ning Kang and Chengyue Wu and Zhenguo Li and Ping Luo},
+    year={2025},
+    eprint={2505.20147},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV},
+    url={https://arxiv.org/abs/2505.20147}
+}
+```
+
+## 4. Contact
+
+**Point of Contact:** [Jin Wang](mailto:wj0529@connect.hku.hk)