Inference-time compute has re-emerged as a practical way to improve LLM reasoning. Most test-time scaling (TTS) algorithms rely on autoregressive decoding, which is ill-suited to discrete diffusion language models (dLLMs) due to their parallel decoding over the entire sequence. As a result, developing effective and efficient TTS methods to unlock dLLMs' full generative potential remains an underexplored challenge. To address this, we propose Prism (Pruning, Remasking, and Integrated Self-verification Method), an efficient TTS framework for dLLMs that (i) performs Hierarchical Trajectory Search (HTS) which dynamically prunes and reallocates compute in an early-to-mid denoising window, (ii) introduces Local branching with partial remasking to explore diverse implementations while preserving high-confidence tokens, and (iii) replaces external verifiers with Self-Verified Feedback (SVF) obtained via self-evaluation prompts on intermediate completions. Across four mathematical reasoning and code generation benchmarks on three dLLMs, including LLaDA 8B Instruct, Dream 7B Instruct, and LLaDA 2.0-mini, our Prism achieves a favorable performance-efficiency trade-off, matching best-of-N performance with substantially fewer function evaluations (NFE). The code is released at https://github.com/viiika/Prism.

翻译：推理时计算已成为提升大语言模型推理能力的实用途径。大多数测试时扩展算法依赖自回归解码，这与离散扩散语言模型的全序列并行解码机制存在根本性冲突，导致针对该类模型开发高效测试时扩展方法以释放其生成潜力仍是一个探索不足的挑战。为此，我们提出棱镜框架（剪枝、重掩码与集成自验证方法），这是一种面向离散扩散语言模型的高效测试时扩展框架，其具备三大核心机制：（1）执行分层轨迹搜索，在去噪过程的前中期窗口动态剪枝并重新分配计算资源；（2）引入局部分支与部分重掩码技术，在保留高置信度标记的同时探索多样化的实现路径；（3）采用自验证反馈机制替代外部验证器，通过对中间生成结果进行自评估提示来获取验证信号。在涵盖数学推理与代码生成的四个基准测试中，基于LLaDA 8B Instruct、Dream 7B Instruct和LLaDA 2.0-mini三个离散扩散模型的实验表明，棱镜框架在性能与效率间取得了优异平衡，能够以显著更少的函数评估次数匹配最优N采样性能。代码已发布于https://github.com/viiika/Prism。