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有边界的 Agent 工作流 / 5 OF 5

记忆截断

Only the latest result survives FIFO truncation.

本地计算 / 模拟

本地教学实验,不调用外部 API。合成示例不能替代生产环境测量。

学习指南与本课目录LEARNING CONTRACT

理解 → 实现 → 研究

目标:解释本课机制、复现一个例子、测试一个失败案例。读完不自动代表掌握。

02 · 预测与配置

技术场景说明、参数名称与参考答案目前为英文。

03 · 检查证据

运行实验后查看结果。

方法与参考资料

A deterministic state machine executes your plan against mock tools, not an autonomous language model. Each executed tool costs a synthetic $0.01. The write action never changes external data. Each mock result is truncated to 1,000 characters to bound memory. Guards are illustrative policy rules, not a complete security system.

Text / 原始公式
Plan → allowlist → approval → budget → mock tool → FIFO memory → next step

A tool allowlist alone cannot prevent data exfiltration or prompt injection in a real agent.

ReAct · reasoning and tool actions ↗

练习题

agent-5 / 01What does “Memory truncation” test?

Only the latest result survives FIFO truncation.

Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗

打开实验 →
agent-5 / 02Which computation produces the result in “Memory truncation”?

Plan → allowlist → approval → budget → mock tool → FIFO memory → next step

Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗

打开实验 →
agent-5 / 03What is the baseline configuration for “Memory truncation”?

{"plan":"search,calculator,recall","allowed":"search,calculator,recall,write","steps":6,"budget":0.1,"memory":1,"approval":0}

Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗

打开实验 →
agent-5 / 04What numerical result should you check for “Memory truncation”?

{"Executed / observed steps":3,"Simulated cost (USD)":0.03,"Memory entries":1}

Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗

打开实验 →
agent-5 / 05How would you run a controlled comparison for “Memory truncation”?

Keep the supplied fixture and other settings fixed; change steps. Inspect the trace, stopping reason, simulated spend and retained memory.

Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗

打开实验 →
agent-5 / 06What failure could invalidate a conclusion from “Memory truncation”?

A tool allowlist alone cannot prevent data exfiltration or prompt injection in a real agent.

Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗

打开实验 →
agent-5 / 07Is “Memory truncation” a benchmark of a deployed language model?

No. A deterministic state machine executes your plan against mock tools, not an autonomous language model. Each executed tool costs a synthetic $0.01. The write action never changes external data. Each mock result is truncated to 1,000 characters to bound memory. Guards are illustrative policy rules, not a complete security system.

Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗

打开实验 →
agent-5 / 08Which evidence should you report for “Memory truncation”?

Inspect the trace, stopping reason, simulated spend and retained memory. Export inputs, engine, result and method; distinguish synthetic fixtures from observed production data.

Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗

打开实验 →
agent-5 / 09How can another researcher reproduce “Memory truncation”?

Open agent-5, reset the preset, retain the exact inputs and run. The calculation is deterministic for identical inputs.

Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗

打开实验 →
agent-5 / 10What is the next validation step after “Memory truncation”?

A tool allowlist alone cannot prevent data exfiltration or prompt injection in a real agent. Test the hypothesis on a representative held-out workload and document a baseline, success metric and stopping rule before claiming deployment readiness.

Original teaching answer / local derivation. Method reference: ReAct · reasoning and tool actions ↗

打开实验 →
MATHEMATICAL REVIEW · 2026-10-26

假设、参考实现与验收

工具预算为非负整数。执行前检查白名单,预算耗尽即停止。确定性 mock 测试编排逻辑,不代表 LLM 规划能力。

已复核引擎公式、场景预设及数值保护。固定种子的合成输入不是生产基准。

待执行工具与参数 → 白名单与批准关卡 → 检查步数和成本预算 → 模拟结果 → 有界记忆
机制图:从上到下跟踪每次变换。

运行前:预测一个边界情况

Only the latest result survives FIFO truncation.

提示:隔离假设

固定基线输入,只改变一个参数或边界;同时比较数值与声明单位。单例通过仅验证该样例。

最小可运行算例

将算例及所链接模块保存在同一目录。使用 Node.js 22 或更高版本,然后运行 node agent-5.mjs. 无需第三方运行依赖。

import {calculate,network} from './academy-core.mjs';
const params={
  "plan": "search,calculator,recall",
  "allowed": "search,calculator,recall,write",
  "steps": 6,
  "budget": 0.1,
  "memory": 1,
  "approval": 0
};
const result=calculate('agent',params);
console.log(result.metrics);

测试与迁移

Only the latest result survives FIFO truncation.

在下方笔记记录输入、代码版本、期望值和实测值;再构造违反上述假设的反例,解释原结论为何不再成立。

下载完整代码包(.tar.gz) · 完整参考代码与回归检查

预测、检验与证据记录

展开答案前先预测一个结果;练习后记录差异和迁移测试。记录仅存本地,在不同语言版本间共用。

打开记录/导出/清除 →

数学复核范围逐篇记录,不代表完成论文实证复现;可在笔记中记录疑似错误。 复核范围