Distributing Accountability, Not Capability: Phase Separation and the LLM Workflow Quadrant in Autonomous AI Agent Architectures
- 类型：arxiv
- 标识：2210.03629
- 链接：https://arxiv.org/abs/2210.03629
- 主题：rag
- 主分类：agent
- 形态：method
- 被引：10421
- 被引来源：Semantic Scholar
- S2被引：10421
- OpenAlex被引：578
- 影响力被引：1091
- TLDR：The use of LLMs are explored to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with external sources to gather additional information.
- OpenAlex ID：W4304195432
- OpenAlex DOI：10.48550/arxiv.2210.03629
- DOI：10.48550/arxiv.2210.03629
- DOI来源：OpenAlex
- 开放获取：green
- 开放获取链接：https://arxiv.org/pdf/2210.03629
- OpenAlex更新：2026-08-22
- 待LLM分类：否
- 标题中文：Distributing Accountability, Not Capability: Phase Separation and the LLM Workflow Quadrant in Autonomous AI Agent Architectures
- TLDR中文：探索以交错方式使用 LLM 同时生成推理轨迹和任务特定动作，使两者产生更大协同：推理轨迹帮助模型归纳、跟踪和更新动作计划以及处理异常，而动作使其与外部源交互以获取额外信息。
- 来源文件：
- [OpenAlex discover]
- [S2 enrich]