From Intent to Execution Grant: An Execution-Boundary Conformance Profile for High-Risk AI Actions

• 类型：arxiv
• 标识：2609.11596
• 链接：http://arxiv.org/abs/2609.11596v1
• 主分类：agent
• 形态：application
• TLDR：AI agents increasingly propose actions with external consequences, including financial transfers, infrastructure changes, software deployments, disclosures, and physical actuation. Authorization engines, policy languages, runtime monitors, provenance mechanisms, and agent guardrails provide important foundations, but do not necessarily define a common semantic contract for the final transition from a particular candidate action to execution authority. We specify EBL-Core, an execution-boundary conformance profile for deciding whether one canonical, fully materialized AI-generated candidate may
• 待LLM分类：否
• 标题中文：From Intent to Execution Grant：高风险 AI 操作的执行边界合规性规范。
• TLDR中文：AI agents 日益提议具有外部影响的操作，包括资金转移、基础设施变更、软件部署、信息披露与物理执行。授权引擎、策略语言、运行时监控、溯源机制与 agent guardrails 提供了重要基础，但未必为从特定候选操作到执行权限的最终转换定义统一的语义契约。我们提出 EBL-Core，一种执行边界合规性规范，用于判定某条规范化、完整实例化的 AI 生成候选操作是否可被……（原文截断）
• 来源文件：
• /inbox/tom/_candidates/2026-09-11-agent-rag-longcontext-candidates.json
• /inbox/tom/_candidates/2026-09-12-agent-rag-longcontext-candidates.json