TOPAS: Workflow-Aware Prefix-State Scheduling for Multi-Agent LLM Serving

arXiv:2608.25523 · cs.CL · Submitted 2026-08-26 · Read on arXiv

cs.CL

Submitted: 2026-08-26

Updated: 2026-08-26

Terminology

• KV cache — 13× in this paper · explained in 3 episodes
• Decode — 6× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Trace — 3× in this paper · explained in 2 episodes
• Agentic Workflows — 2× in this paper · explained in 1 episode
• Multi-Agent System — 2× in this paper · explained in 5 episodes
• Operating Systems Design and Implementation — 2× in this paper
• Abstraction — 1× in this paper · explained in 1 episode
• Autellix Least-Attained Service — 1× in this paper
• Decoupling — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 1× in this paper
• In International Conference on Machine Learning — 1× in this paper
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Longest Prefix Match — 1× in this paper
• Shortest Remaining Processing Time — 1× in this paper
• Topological Order — 1× in this paper · explained in 2 episodes

Sources

• Self-Resource Allocation in Multi-Agent LLM Systems
• Kairos: Low-latency Multi-Agent Serving with Shared LLMs and Excessive Loads in the Public Cloud
• MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool
• Continuum: Efficient and Robust Multi-Turn LLM Agent Scheduling with KV Cache Time-to-Live
• DroidSpeak: KV Cache Sharing for Cross-LLM Communication and Multi-LLM Serving
• Autellix: An Efficient Serving Engine for LLM Agents as General Programs
• Astraea: A State-Aware Scheduling Engine for LLM-Powered Agents
• Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving
• Batch Query Processing and Optimization for Agentic Workflows
• FlowMesh: A Service Fabric for Composable LLM Workflows
• Preble: Efficient Distributed Prompt Scheduling for LLM Serving
• Teola: Towards End-to-End Optimization of LLM-based Applications
• Efficient LLM Serving for Agentic Workflows: A Data Systems Perspective
• AugServe: Adaptive Request Scheduling for Augmented Large Language Model Inference Serving
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

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