Paper Overview
Research Area: NLP / Diffusion Models Authors: Mingyu Zhao, Shuhang Lin, Ligong Han, Xiaojing Yu, Zhenting Wang, Yifei Pang, Yang Zhou, Dimitris N. Metaxas Posted: 2026-10-27 arXiv: 2610.37642
Abstract
Diffusion language models offer a promising path to efficient agents through parallel token generation. However, efficient interaction requires adapting both where computation is spent and how much text is generated. We introduce a metacognitive controller that guides diffusion generation according to the needs of the current response and the agent's interaction with the environment. It learns when to finalize tokens and how far generation should extend, reducing overall computation within responses. Our controller, a lightweight plug-in for the diffusion backbone, is trained on agent trajectories to balance correctness and generation cost. This approach connects the agent's generation process to the interaction the agent serves. Experiments across five benchmarks demonstrate that our approach reduces inference FLOPs by 20.6% on average compared with the most efficient baseline, while maintaining competitive task accuracy. Ablation studies further validate the effectiveness of our approach.
Key Takeaways
- A metacognitive controller guides diffusion language model generation based on the current response's needs and the agent's environment interaction.
- The controller learns when to finalize tokens and how far generation should extend, cutting overall computation within responses.
- It is a lightweight plug-in for the diffusion backbone, trained on agent trajectories to balance correctness and generation cost.
- Across five benchmarks, it reduces inference FLOPs by 20.6% on average versus the most efficient baseline while keeping competitive task accuracy.
- Ablation studies further validate the approach's effectiveness.