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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
FAME: QoS-aware Async Service Orchestration for Agentic W...
[Submitted on 21 Jan 2026 (v1), last revised 10 Aug 2026 (this v · 2026-01-21 · via cs.DC updates on arXiv.org

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Abstract:Agentic workflows built on Large Language Models (LLMs) increasingly rely on external tools, with Model Context Protocol (MCP) services emerging as a common interface for tool discovery and invocation. These workflows are stateful, bursty and can block for minutes to hours while external tools execute. Always-on virtual machines and managed agent runtimes simplify deployment, but can over-provision capacity, bill idle wait time or encounter execution limits for long-running tool calls. We present FAME, a service-oriented middleware that optimizes MCP-enabled agentic workflows through modular service orchestration, explicit state management, MCP service input/output reduction and timeout-safe async invocation for long-running external services. FAME realizes these composite services using FaaS-based agent roles, persists workflow state through agent memory injection, and reduces MCP service overhead using S3 handle passing and tool-output caching. For long-running external MCP tools, FAME checkpoints agent and orchestrator state, suspends execution and resumes from callbacks without billing idle waits. Across 26 short-running service tasks over 10 MCP servers, FAME improves service-level QoS by reducing infrastructure cost by 8-12x relative to virtual machines and 43-106x relative to managed runtimes, while reducing latency by up to 17x, input tokens by up to 88% and total cost by up to 66%. On two long-running MLZero workloads from MLE-Bench, FAME completes all iterations while synchronous AWS Step Functions time out, and is 3.64-4.24x cheaper than AWS Durable Functions at the agent level and 2.67-8.23x cheaper at the orchestrator level.

Submission history

From: Varad Vinod Kulkarni Mr [view email]
[v1] Wed, 21 Jan 2026 07:46:25 UTC (595 KB)
[v2] Tue, 27 Jan 2026 10:56:16 UTC (594 KB)
[v3] Mon, 10 Aug 2026 02:51:19 UTC (958 KB)