发表机构
Fujitsu Limited; RIKEN Center for AIP; The University of（富士通有限公司; 理化学研究所人工智能中心; 某大学（原文未完整给出大学名称））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人工智能代理团队与人类共同进化的问题，提出LOGOS这一可插拔层，通过编译多模态输入、转换代理活动并应用验证，实现可验证的人机循环工程，为可问责自动化提供动态逻辑。
AI 中文摘要
人工智能代理正从答案引擎演变为持久团队，能使用工具、分配工作、从经验中学习并修改塑造其未来行为的工件。部署的关键问题不再仅是代理能做什么，而是谁控制其发展。我们引入LOGOS，它是用于自我进化和治理的可插拔层，强化而非取代现有多智能体框架。LOGOS编译异构多模态输入，在运行时转换代理活动并应用验证。每个学习到的提示等在未经许可前都是不可信的候选版本。该架构实现了“可验证的人机循环工程”，为可问责的自动化提供动态逻辑，代理可进化，但需证据和人类权威来闭环。
英文摘要
AI agents are evolving from answer engines into persistent teams that use tools, delegate work, learn from experience, and modify the artifacts that shape their future behavior. The defining question for deployment is no longer merely what agents can do, but who controls what they are allowed to become. We introduce logos, a pluggable layer for self-evolution and governance that strengthens existing multiagent frameworks rather than replacing them. logos compiles heterogeneous multimodal inputs, including documents, images, audio, tables, databases, APIs, and human instructions into versioned agent packs containing agents, tools, knowledge, tests, permissions, and policies. During operation, it transforms agent activity into portable, auditable event traces and applies fail-closed verification across frameworks and backends. Every learned prompt, memory, skill, tool, role, or workflow remains an untrusted release candidate until held-out execution evidence, human-controlled policy, and explicit authorization permit its promotion. This architecture enables "verifiable human-agent loop engineering": agents can act, ask, learn, and propose improvements, while humans can steer objectives, permissions, approvals, and irreversible actions without interrupting continuous operation. logos provides a living logic for accountable automation. Agents may evolve at machine speed, but only evidence and human authority can close the loop.
Comments58 pages, 14 figures
