TiDAR: Think in Diffusion, Talk in Autoregression

Diffusion language models hold the promise of fast parallel generation, while autoregressive (AR) models typically excel in quality due to their causal structure aligning naturally with language modeling. This raises a fundamental question: can we achieve a synergy with high throughput, higher GPU utilization, and AR level quality? Existing methods fail to effectively balance these two aspects, either prioritizing AR using a weaker model for sequential drafting (speculative decoding), leading to lower drafting efficiency, or using some form of left-to-right (AR-like) decoding logic for diffusion, which still suffers from quality degradation and forfeits its potential parallelizability. We introduce TIDAR, a sequence-level hybrid architecture that drafts tokens (Thinking) in Diffusion and samples final outputs (Talking) AutoRegressively - all within a single forward pass using specially designed structured attention masks. This design exploits the free compute density on GPUs, achieving a strong balance between drafting and verification capacity. Moreover, we design TIDAR to be serving-friendly as a standalone model. We extensively evaluate TIDAR against AR models, speculative decoding, and diffusion variants across generative and likelihood tasks at both 1.5B and 8B scales. Thanks to parallel drafting and sampling as well as efficient exact KV cache support, TIDAR outperforms speculative decoding in measured throughput and surpasses diffusion models like Dream and Llada in both efficiency and quality. Most notably, TIDAR is the first architecture to close the quality gap with AR models while delivering 4.71× to 5.91× more tokens per second.