DepCap: Adaptive Block-Wise Parallel Decoding for Efficient Diffusion LM Inference

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

DepCap is a training-free framework designed to enhance the efficiency of block-wise Diffusion Language Model (DLM) inference without compromising generation quality. Existing block-wise DLM methods often use fixed block schedules or rely on local signals, leading to suboptimal trade-offs between speed and quality. DepCap addresses this by introducing two core components: DepGA-Block, which adaptively determines block boundaries using cross-step signals derived from the last decoded block's influence and predictive uncertainty, and CAP-Decoding, which identifies and decodes a conflict-free subset of tokens in parallel within each block. This approach enables substantial inference acceleration, achieving up to a 5.63x speedup on benchmarks like MBPP with LLaDA-1.5, while maintaining or even improving accuracy. DepCap is plug-and-play, compatible with existing KV-cache strategies, and supported by an information-theoretic analysis.

Key takeaway

For AI Engineers optimizing Diffusion Language Model inference, DepCap offers a robust, training-free solution to significantly boost decoding speed without sacrificing generation quality. You should consider integrating DepCap's dependency-guided adaptive block partitioning and conflict-aware parallel decoding into your DLM pipelines, especially for tasks like reasoning and coding, to achieve substantial throughput improvements and potentially better accuracy compared to fixed-schedule or confidence-only methods. This framework is compatible with existing cache techniques, making it a practical enhancement for current deployments.

Key insights

DepCap optimizes DLM inference by adaptively partitioning blocks and safely parallelizing token decoding using cross-step and conflict signals.

Principles

• Cross-step signals improve adaptive block partitioning.
• Token-level conflict detection enables aggressive parallel decoding.
• Last-block influence and predictive uncertainty guide block expansion.

Method

DepCap uses DepGA-Block to set block boundaries based on last-block influence (KL divergence) and predictive uncertainty (Shannon entropy). CAP-Decoding then selects a conflict-free token subset for parallel decoding using confidence and a pairwise conflict score $D_{ij}=\log p_{i}(\hat{y}_{j})+\log p_{j}(\hat{y}_{i})$.

In practice

• Implement adaptive block sizing using KL divergence and entropy.
• Detect token conflicts to enable safer, more aggressive parallel decoding.
• Integrate with existing KV-cache techniques for further speedup.

Topics

• Diffusion Language Models
• Block-Wise Decoding
• Adaptive Block Partitioning
• Conflict-Aware Parallel Decoding
• DLM Inference Efficiency

Code references

• X-Xia0828/DepCap

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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• DepCap: Adaptive Block-Wise Parallel Decoding for Efficient Diffusion LM Inference — DepCap optimizes DLM inference by adaptively determining block boundaries and enabling conflict-free parallel decoding.
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• SAID: Accelerating Diffusion-Based Language Models via Scaffold-Aware Iterative Decoding — SAID accelerates DLLMs by prioritizing denoising on structural tokens and adaptively processing uncertain tokens.
• Fast-dLLM++: Fréchet Profile Decoding for Faster Diffusion LLM Inference — Fast-dLLM++ accelerates Diffusion LLM inference by utilizing heterogeneous confidence profiles for parallel token commitment.
• DFlash: Block Diffusion for Flash Speculative Decoding — DFlash uses a block diffusion model for parallel drafting to significantly accelerate LLM inference.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.