John Stephan, Razvan Puscasu, Xu Yong, Ding Jiutao, Guan Zixuan
In expert-parallel mixture-of-experts (MoE) serving, dispatch moves tokens to the accelerators hosting their selected experts, and combine returns expert outputs to each token's owner. Both operations lie on the critical path and can dominate MoE layer latency in multi-node deployments. We present INC-MoE, an expert-parallel MoE layer that offloads dispatch and combine to the network switch and overlaps both collectives with each other and with expert computation. Dispatch uses in-network AllGather through switch-side multicast, while combine uses in-network ReduceScatter through switch-side reduction. Both collectives use message semantics between the end hosts and the switch. Because the in-network collectives load opposite directions of each link, executing them concurrently reduces bandwidth-bound communication time by $(N-2)/(2(N-1))$ relative to concurrent ring-based collectives: 33% for four ranks and 43% for eight. INC-MoE partitions tokens into bands and processes them through a three-stage AllGather-feed-forward network (FFN)-ReduceScatter pipeline inside a single persistent kernel. At steady state, role-specialized thread blocks gather the next band, compute the current band, and reduce the previous band concurrently, overlapping both collectives with each other and with computation using device-side synchronization. INC-MoE integrates as a drop-in replacement for the MoE layer of vLLM. We evaluate it in an emulated four-node scale-out deployment with a dedicated accelerator emulating an in-network computing (INC) switch. On OLMoE prefill with four-way expert parallelism, INC-MoE reduces time to first token (TTFT) by up to 33% and increases throughput by up to 50% relative to vLLM's default collective path, while matching its outputs.
Xueyang Zhou, Weidong Wang, Lin Lu, Jiawen Shi, Guiyao Tie, Xu Yongtian, Lixing Chen Shanghai Jiaotong University, Pan Zhou Huazhong University of Science and Technology 等 10 位作者
LLM-based agents are rapidly being deployed in real-world applications (e.g., digital assistants and customer service), making safety a critical concern. However, in multi-turn, tool-augmented settings, dynamic user interactions, external tool use, and unintended harmful behaviors make robust safety assurance challenging. To address these challenges, we propose SafeAgent, a framework that improves agent safety through fully automated synthetic data generation. SafeAgent introduces (1) an open and extensible threat model OTS that decomposes agent risk into instruction-, context-, and action-induced sources to ground safety analysis and alignment; and (2) an automated pipeline that instantiates OTS to surface scenario-specific failure modes, stress-test agents, and generate self-reflective safe responses—without hazardous real-world data collection. We evaluate SafeAgent on two safety benchmarks and one real-world terminal task. Across four widely used open-source models, SafeAgent improves safety performance by 45% on average and delivers a 28.91% gain on the real-world task, outperforming state-of-the-art closed-source models. These results highlight the practical advancement and scalability of SafeAgent in building safer LLM agents for real-world deployment.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表