arXiv AI
Simthesizer: An Agent-Driven Simulation Framework for LLM Serving Systems
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arXiv AI
Sep 10
AgentServeSim: Serving-System Simulation and Policy Search for LLM Agent Programs
AgentServeSim is a simulation framework designed to model the execution of large language model (LLM) agent programs, capturing cross‑turn key‑value (KV) state retention, successor turn release, and scheduling decisions. Unlike existing simulators that operate on request streams, AgentServeSim treats the entire agent program as a single unit of execution, using a Program Control Block, Program Orchestrator, Retention Plane, and Dispatch Plane to emulate realistic serving dynamics. Validation against real vLLM deployments on two GPU platforms shows mean job completion time errors below 5.5%, and the simulator enables automated policy search that improves mean JCT by up to 2.8% over hand‑written policies. whyItMatters":"The simulator provides a realistic, CPU‑based tool for evaluating and optimizing LLM agent serving policies, achieving high fidelity to real deployments and enabling measurable performance gains."
Hugging Face Trending Papers
Jun 8
AGENTSERVESIM: A Hardware-aware Simulator for Multi-Turn LLM Agent Serving
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 AI
Jun 9
AGENTSERVESIM: A Hardware-aware Simulator for Multi-Turn LLM Agent Serving
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 AI
Aug 26
Beyond Executable Models: The Pufibara Agent Harness and the Modelica Agent Workflow Benchmark for Physical System Modeling
The paper introduces Pufibara, an agent harness designed to maintain engineering state and evidence across revisions in Modelica-based physical system modeling. It also presents a 232-task Modelica Agent Workflow Benchmark covering model repair, generation, and tuning, evaluated by an external benchmark-owned evaluator. Experiments show Pufibara outperforms Claude Code in task success and resource efficiency across two LLM backends.
arXiv AI
Oct 5
Coco: An Agentic Copilot for the Hardware--Software Co-Design Lifecycle
arXiv:2610.02376v1 Announce Type: cross Abstract: Co-designing ML models and the accelerators that run them is an unusual reasoning task: architects must draw confident, high-stakes conclusions about...
arXiv AI
Oct 9
Synthesis Through Simulation: Generating Coherent Enterprise Data via Scalable Agent-System Interaction
The paper introduces Synthesis Through Simulation (STS), a schema‑free data synthesis method that uses a large language model agent to generate enterprise data by interacting with policy‑enforcing APIs in simulated environments. STS ensures structural validity by construction and separates validity enforcement from distribution modeling. The Generalist Populator, an agent within STS, achieves high distributional fidelity (0.88 average marginal fidelity) and full constraint satisfaction across ten environments without needing database schemas, outperforming traditional statistical synthesizers and schema‑privileged agents.