We present CadLLM, a training-free method to accelerate the inference throughput of diffusion-based LLMs (dLLMs). We first investigate on the dynamic nature of the token unmasking confidence across blocks and steps. Based on this observation, we then present a lightweight adaptive approach that can control the generation block size, step size, and threshold based on the average confidence score of the unmasked tokens. We further reduce the softmaxing overhead of token probability generation by dynamically leveraging a subset of vocabulary size to regulate sampling breadth. CadLLM is a plug-and-play model-agnostic with KV caching based dLLMs. Extensive experiments on four popular tasks demonstrate the efficacy of CadLLM to yield throughput improvement of up to 1.1-2.28x over the state-of-the-art baseline with competitive accuracy.

Machine learning practitioners routinely rely on public code repositories accompanying research papers, libraries, and open-source projects to evaluate, baseline, or build upon prior work. Yet getting such repositories to run and reproduce results often involves non-trivial effort, and is especially challenging when the target software and hardware stack differs from the one the original code was developed on. A prominent instance is running repositories originally built for CUDA-enabled hardware on AMD GPUs supporting the ROCm software stack. Here, successful installation alone is not a sufficient signal: a range of subtler issues may cause the code to either fail outright or silently fall back to inefficient execution. We present Repo2ROCm, an agentic system that migrates CUDA-centric ML repositories to ROCm in an automated manner. Repo2ROCm reconstructs a ROCm-compatible environment, adapts hardware-specific assumptions, executes a real workload, and confirms GPU-backed execution emitting auditable artifacts at every step (environment specs, source patches, execution logs, and verification evidence). We evaluate on ResearchBench, 60 repositories belonging to recent papers from top AI conferences. While only 18/60 run out of the box, Repo2ROCm reaches verified functional reproduction for 59/60 (3.3× improvement). Against popular general-purpose coding agents on a curated ten-repository diagnostic set (with all agents using the same Claude Sonnet 4 backbone), Repo2ROCm achieves the highest Portability Fidelity Score (PFS), a measure of how faithfully a run preserves the accelerator-critical execution path, real workload, and verified environment, scoring 77.3 vs. 61.4 (Claude Code), 58.7 (Mini-SWE-Agent), and 40.3 (Cursor Agent).