Patchwork is a resource‑sharing system for domain‑randomized embodied simulation that separates a shared template of common resources from patches that record environment‑specific mutations. By sharing compiled model structure and immutable assets while keeping mutable parameters private, Patchwork reduces CPU memory usage dramatically—up to 92% at 32 replicas—compared to running each environment in its own process. The approach was implemented on robosuite and MuJoCo and evaluated on LIBERO‑plus and RoboCasa365 workloads, demonstrating that memory growth shifts toward template diversity rather than instance count.
arXiv:2608.24650v1 Announce Type: cross Abstract: System-level simulation is an essential tool for exploring the rapidly expanding design space of LLM serving systems, where real deployments remain c...
Multi-turn LLM agents interleave model calls with external tool invocations, shifting serving from stateless request processing to stateful program execution. Serving these workloads requires scheduling, KV-cache management, and routing policies that use program-level context, including turn dependencies, tool-induced gaps, and reusable KV state.
arXiv:2606. 09613v1 Announce Type: cross Abstract: Multi-turn LLM agents interleave model calls with external tool invocations, shifting serving from stateless request processing to stateful program execution.
arXiv:2608. 15127v1 Announce Type: cross Abstract: Agentic applications are shifting AI serving from isolated model inference to long-running workloads in which LLMs coordinate tools, environments, and persistent state.
arXiv:2602. 09345v3 Announce Type: replace-cross Abstract: AI agents are increasingly deployed in multi-tenant cloud environments, where they execute diverse tool calls within sandboxed containers, each call with distinct resource demands and rapid fluctuations.
MobileGym is a browser-hosted, lightweight simulation platform designed for mobile GUI agent research. It offers verifiable outcome signals via deterministic, JSON-based state judging and supports scalable online reinforcement learning with hundreds of parallel instances on a single server. The platform includes a declarative task-definition framework, a structured AnswerSheet protocol, and a benchmark of 416 parameterized tasks across 28 apps, demonstrating strong sim-to-real transfer in a case study.
arXiv:2607. 23933v1 Announce Type: cross Abstract: As LLM agents increasingly rely on the Model Context Protocol (MCP) to invoke isolated external sandboxes, disaggregated sandbox deployment introduces a fundamental tension between resource utilization and interactive tail latency.
arXiv:2610.19822v1 Announce Type: cross Abstract: LLM agents are evolving from a single model calling tools to planners that orchestrate multiple expert models whose capabilities live in distinct wei...
The paper proposes DCO, a dynamic cache orchestration scheme for multi-core AI accelerators that uses application-aware policies and dataflow information to guide cache replacement, bypass decisions, and thrashing mitigation. Using a cycle-accurate simulator, the authors demonstrate up to 1.80× speedup over conventional cache architectures and validate the approach with an analytical model and RTL implementation. The design occupies 0.064 mm² on a 15 nm process and operates at 2 GHz, showing that a shared system-level cache can simplify programming while boosting performance for large language model workloads.
arXiv:2606. 09643v1 Announce Type: cross Abstract: Foundation models (FMs) are increasingly used as backbones for downstream tasks across language, vision, time-series, and multimodal applications.
arXiv:2609.34060v2 Announce Type: replace Abstract: Prompt-specialized multi-agent systems enable multiple agents to share a model while performing complementary roles to solve complex tasks. However...
arXiv:2607. 20488v1 Announce Type: new Abstract: Multi-agent LLM frameworks typically fix their team topology at boot time.