Large Language Models (LLMs) have shown remarkable capability in knowledge-intensive scenarios, such as electronic design automation (EDA) tool documentation question answering (QA), due to their ability to process and generate contextually rich, domain-specific information. Evaluating LLM outputs is paramount, as it directly impacts their accuracy, effectiveness, and trustworthiness in practical applications. In this paper, we introduce MAEDA, a novel LLM-powered multi-agent evaluation framework that utilizes multiple fine-tuned LLM agents working collaboratively to assess common error types encountered in EDA tool documentation QA. Specifically, we design customized point-to-point alignment and chain-of-thought (CoT) reasoning strategies tailored to specific agents, enhancing both fine-tuning and inference capabilities. Experimental results demonstrate that MAEDA outperforms state-of-the-art (SOTA) general-purpose and cross-domain evaluation frameworks in accurately identifying error types specific to this domain. Our benchmark is publicly available at https://github.com/Rayzzz14/MAEDA-DATE26/.

Pipeline parallelism has emerged as an indispensable technique for training large deep neural networks. While existing asynchronous pipeline systems address the time bubbles inherent in synchronous architectures, they continue to suffer frominefficiencyandsusceptibilitytovolatilehardware environment due to their suboptimal andstaticconfigurations. In this paper, we propose DynPipe, aninterference-awareasynchronous pipeline framework to optimize theend-to-endtraining performance in highlydynamiccomputing environments. By characterizing thenon-overlappedcommunication overheads andconvergencerate conditioned on stage-wise staleness, DynPipe carefully crafts an optimized pipeline partition that harmonizes the hardware speed with statistical convergence. Moreover, DynPipe deploys anon-intrusiverandom forest model that utilizes runtime stage statistics to evaluate the impact of environmental changes, such as task interference and network jitter, on the training efficiency. Following the evaluation guidance, DynPipe adaptivelyadjustspartition plan to restore both intra and inter-stage load balancing, thereby facilitating seamless pipeline reconfiguration in dynamic environments. Extensive experiments show that DynPipe outperforms state-of-the-art systems, accelerating the time-to-accuracy by1.5-3.4×.