Don't Commit Alone: Joint Token Commitment in Diffusion Language Models
Diffusion language models (dLLMs) commit multiple tokens per denoising step by decoding each selected position independently from a shared context. When these positions are dependent, this factorization introduces an error captured by conditional total correlation, which confidence-based selection cannot infer from marginal probabilities alone. We propose CoCommit, a marker-gated coordination pass that delays commitment. After the usual bundle selection, a learned marker identifies the commit set, and the backbone's last n layers are re-applied to coordinate the marked positions before greedy argmax writes the tokens. This approximates joint-mode decoding while reusing existing weights, r...
| Authors | Lin Yao |
|---|---|
| Primary category | cs.CL |
| Published | 5 July 2026 |
| Last updated at source | 24 August 2026 |
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