发表机构
South Dakota State University（南达科他州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ERRAND 将代理记忆的重新验证视为有定价的差事，通过单峰指数和预算控制，在漂移环境中以更少的动作开销超越激进策略，实现高效且自适应的记忆维护。
AI 中文摘要
已部署的代理依赖于移交的知识：一个冻结的策略会查阅一份在数据流开始之前编写的、由整合条目组成的简报。随后世界在变化而存储保持静止：路径关闭、标志改变、价格区间移动；每个条目在移交时都是真实的，而失败在于过时，而非无知。我们提出 ERRAND，将重新验证视为一项有定价的差事：一次重新检查与它所保护的任务竞争同样稀缺的动作，只有当解决一个疑问的每动作价值超过现行工资时才获得资助。差事指数是单峰的，在信念的两端都趋于零，因此任一方向的确定性都不花费成本；免费的途中收据维持路径上的知识，修复写入一个版本，而非删除。在两个漂移的工具使用世界中，在相同的动作预算下，ERRAND 在预注册的衡量标准上超越了所有非预言机策略，在每个设置中都是首要的，在每个有约束力的预算下都是有条件的，在基础上限下领先于激进的重新验证 10.0 个百分点。克制获胜：在没有上限的情况下，ERRAND 自行停止，花费 11.0% 的步骤，而无上限的激进重新验证花费 70.7%，并且仍然落后于有上限的 ERRAND 4.5 个百分点。差距位于简报覆盖最薄弱之处，即长尾知识的影子价格：一笔小额预算，定价合理，胜过从不重新检查的更大存储。
英文摘要
Deployed agents run on handed-over knowledge: a frozen policy consults a briefing of consolidated items written before the stream begins. The world then moves while the store stands still: paths close, flags change, price bands move; every item was true at handover, and the failure is staleness, not ignorance. We introduce ERRAND, which treats revalidation as a priced errand: a recheck competes with the task it protects for the same scarce actions, funded only when the value per action of resolving a doubt clears a running wage. The errand index is single-peaked, vanishing at both ends of belief, so certainty in either direction costs nothing; free en-route receipts maintain on-path knowledge, and repair writes a version, never a deletion. Under equal action budgets in two drifting tool-use worlds, ERRAND clears every non-oracle policy on the preregistered calibers, primary in every setting and conditional at every binding budget, leading eager revalidation by 10.0pp at the base cap. Restraint wins: given no cap, ERRAND stops on its own, spending 11.0% of steps, while uncapped eager revalidation spends 70.7% and still finishes 4.5pp behind capped ERRAND. The margin sits where the briefing's coverage is thinnest, the shadow price of long-tail knowledge: a small budget, well priced, beats a bigger store that never rechecks.
