Diffusion Language Models (DLMs) benefit from a flexible decoding mechanism that enables parallelized inference and controllable generations over autoregressive models. Yet such flexibility introduces a critical challenge: inference performance becomes highly sensitive to the decoding order of tokens. Existing heuristic methods, however, focus mainly on local effects while overlooking long-term impacts. To address this limitation, we propose the Foreseeing Decoding Method (FDM), a novel approach that integrates both local and global considerations to unlock the full potential, employing a search-based strategy to enable effective optimization in discrete spaces. Furthermore, by analyzing the consistency of chosen tokens in the full decoding process, we develop a variant, FDM with Acceleration (FDM-A), which restricts deep exploration to critical steps identified as the exploration and balance circumstances. Extensive experiments across diverse benchmarks and model architectures validate the scalability of FDM and demonstrate the superior efficiency-performance trade-off achieved by FDM-A. Our work might potentially provide a principled step toward more powerful decoding methods for DLMs. Our code is available at https://github.com/PKU-ML/FDM.

Diffusion Language Models (DLMs) are emerging as competitive alternatives to auto-regressive models, but their flexible, any-order decoding may introduce new trustworthiness risks. We present TrustDLM, a comprehensive benchmark for evaluating DLM trustworthiness across safety, privacy, and fairness with multiple categories of static post contexts. Our results show that while DLMs are generally trustworthy with user prompts alone, their alignment degrades noticeably when malicious post-contexts are attached to masked responses. We further find that decoding order and generation length affect evaluation outcomes, while longer contexts do not necessarily induce stronger effects. Finally, we introduce TrustDLM-Auto, the first automatic framework for identifying vulnerable DLM configurations, which reveals substantial weaknesses across models and dimensions. Overall, TrustDLM provides the first trustworthiness evaluation framework specifically designed for DLMs, with practical applicability further validated on closed-source DLMs. Our work will help the community enhance the trustworthiness of DLMs. Our code is available at https://github.com/PKU-ML/TrustDLM.