arXiv AI
Silent and Costly: Characterizing and Detecting Prompt-Caching Mis-Integration in LLM Agents
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arXiv Machine Learning
Sep 7
Same Request, Different Answer: Quantization Amplifies Cache-Induced Divergence in LLM Serving
The paper investigates how prefix caching, a default optimization in open‑source LLM serving stacks, affects reproducibility when combined with weight quantization. Experiments on an eighty‑episode multi‑turn agentic tool‑use workload show that enabling the cache causes the agent’s trajectory to change in 36.2 % of episodes at 16‑bit precision and 75.0 % at 4‑bit precision, while disabling the cache yields perfectly reproducible runs. The study identifies specific cache‑related settings that drive run‑to‑run divergence and demonstrates that cached serving is deterministic only when the cache state is preserved, which is not the case in typical deployments.
arXiv AI
Aug 18
From LLM Inference to Agentic Workloads: Characterization and Implications for Serving Systems
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 AI
Aug 18
Learning Agent Execution for KV-Cache Management in Agentic Serving
arXiv:2608. 14624v1 Announce Type: new Abstract: Multi-agent LLM systems have emerged as an important deployment paradigm for AI services, where each user request is decomposed into a sequence of specialized agents.
arXiv Machine Learning
Aug 4
Agentic Coding in the Wild: Characterizing GitHub Copilot Traces at Production Scale
arXiv:2608. 00101v1 Announce Type: cross Abstract: AI coding agents like GitHub Copilot, Claude Code, and Codex interleave multi-step LLM inference with tool execution, creating a workload different from chatbots.
arXiv AI
Oct 12
Co-Modifying Application Code and Deployment Infrastructure: A Causal Study of AI Agent Pull Requests
The study evaluates 26,999 pull requests that modify both application code and deployment infrastructure, using two large datasets and six AI agent architectures. It finds that such dual‑scope changes occur in only about 4.5% of agent‑generated PRs but lead to two distinct integration outcomes: either significant delays due to CI/CD and provisioning checks, or a high rate of silent merges with reduced human review. The results highlight that current AI agents lack deployment awareness, causing unverified dependencies to slip into production pipelines.
arXiv AI
6d ago
WAKMan: Workload Aware KVCache Manager
WAKMan is a workload‑aware KV cache manager designed for Large Language Model servers that selects an eviction policy based on live traffic patterns without needing to know request types or read prompts. By adapting to the mix of single‑turn, multi‑turn, and agentic requests, it improves cache hit rates over the default LRU strategy by an average of 5 percentage points and achieves the lowest mean and worst‑case optimality gap among evaluated policies. The paper demonstrates that no single policy consistently outperforms others across diverse traffic mixes, highlighting the need for dynamic, workload‑aware cache management.