Metacognitive Decoding: Efficient Inference for Diffusion Language Agents
A lightweight controller for diffusion language agents adapts when to finalize tokens and how far to generate. Across five benchmarks, the paper reports 20.6% lower inference FLOPs on average than its most efficient baseline while keeping task accuracy competitive.
The paper adds a lightweight controller trained on agent trajectories to decide when to finalize tokens and how far generation should continue. Across **five benchmarks**, it reduces inference FLOPs by **20.6% on average** against the most efficient baseline while maintaining competitive task accuracy.
Builders working with diffusion language models can treat generation length and stopping as part of the agent’s interaction policy, then measure compute alongside task accuracy. The controller is presented as a plug-in for the diffusion backbone.
The paper adds a lightweight controller trained on agent trajectories to decide when to finalize tokens and how far generation should continue. Across **five benchmarks**, it reduces inference FLOPs by **20.6% on average** against the most efficient baseline while maintaining competitive task accuracy. Builders working with diffusion language models can treat generation length and stopping as part of the agent’s interaction policy, then measure compute alongside task accuracy. The controller is presented as a plug-in for the diffusion backbone. The material does not provide per-benchmark results or define how close “competitive” accuracy is. The reported compute reduction is relative to the paper’s baseline and may not transfer to other systems.
This adds a concrete inference-time use of metacognition: a learned controller can adjust token finalization and generation length to reduce compute while maintaining competitive accuracy. It extends broad discussion of self-monitoring into a measurable decoding policy, though the reported average and unspecified accuracy gap limit how confidently builders can transfer the result.