发表机构
NVIDIA（英伟达）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究长期人工智能代理与用户交互薄弱问题，引入JarvisBench基准测试，含代理协作和用户交互两轨道，用模块化原型评估，结果显示贾维斯式调解可提升任务性能，其有效性取决于调解器语言模型大脑。
AI 中文摘要
长期人工智能代理能力日益增强，但与用户的交互仍很薄弱。多数工作流程中，用户给出初始指令后只能收到部分文本更新，对代理行为及介入时机缺乏清晰认知。当前代理生态系统缺少始终在线的贾维斯式调解器。本文引入JarvisBench基准测试，它包含代理协作和用户交互两个互补轨道，用模块化原型评估，初步结果表明贾维斯式调解可提供基于跟踪的用户问题响应并改善任务性能，有效性很大程度取决于调解器的语言模型大脑。
英文摘要
Long-horizon AI agents are becoming increasingly capable, yet their interaction with users remains surprisingly thin. In most workflows, users give an initial instruction, receive only selective textual updates, and lose a clear sense of what the agent is doing or when to step in. This leaves a missing part in the current agent ecosystem: an always-on Jarvis-style mediator that keeps the agent continuously reachable to the user. Such a mediator should support real-time spoken interaction with the user, answer questions without interrupting the worker, proactively report progress or confusion, and inject user guidance back into the agent's execution when useful. In this work, we introduce JarvisBench, a benchmark for measuring the dual value of mediation in long-horizon agent workflows. JarvisBench contains two complementary tracks: an agent-collaboration track that measures whether mediation improves downstream task completion, and a user-interaction track that measures whether mediation makes ongoing execution more understandable, responsive, and accessible to users. We instantiate the benchmark with a modular reference Jarvis prototype and evaluate it on 34 text-only WildClaw tasks executed in OpenClaw. Preliminary results with GPT-5.5, Claude Opus 4.7, Gemini-based, and GPT-based worker agents suggest that Jarvis-style mediation can provide trace-grounded responses to user questions and improve task performance when sparse user guidance is injected at appropriate moments. The results also show that effectiveness depends strongly on the mediator's LLM brain, highlighting both the promise of this missing middle layer and the need for broader community effort. Demo page https://cchen1436.github.io/jarvis
