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D6 Messages, Context Engineering and Compression, Session Storage/Recovery/Forking (dg M06/M08/M09/M10)

• When a long conversation outgrows the context window, how do you compress it — when do you trigger, what do you drop, and what do you keep?长对话里上下文放不下了，你会怎么压缩？什么时候触发、压掉什么、保留什么？

  Common in ChinaCommon overseasIntermediate#context-engineering#compression#cost

  How to reason about it · think before answering

  1. Saying 'summarize it' earns nothing — everyone says that. The signal is whether you name a trigger point and a keep-list; without those you sound like someone who never ran a long conversation in production.
  2. Split it into three questions before answering: when to compress, what to drop, what to keep. The split itself scores, because it frames compression as a policy rather than a function.
  3. Trigger on a threshold, not a timer, and never on an error. Give a number and justify it: compress at roughly 70% of the history budget, because summarizing is itself a model call that can be slow or fail. Waiting until 90% means one timed-out summary call and the next turn slams into the window limit.
  4. Drop the process: intermediate reasoning, raw tool payloads already consumed, requirements the user later reversed — their value has already settled into later conclusions. Keep the system prompt (it is not history), the most recent turns verbatim, and any constraint or fact the user stated explicitly. Getting that last one wrong makes the model visibly forget.
  5. Add the detail others miss: the cut must land on a turn boundary. Slicing between an assistant tool_calls message and its matching tool result leaves a dangling call, and most providers reject that request with a 400. This is the line that proves hands-on experience.
  6. Two follow-ups to expect. Which model summarizes? A cheap small one — summarization is extraction, not reasoning, which ties back to tiered routing. And what if the summary call fails? Degrade to a plain sliding window that drops the oldest turns, so a failed compression never fails the whole turn.

  分析过程 · 先想清楚再作答

  1. 这题的区分度不在「用摘要」三个字上，几乎人人都答得出。区分度在你有没有说出触发时机和保留清单——只答「让模型总结一下前面的对话」的，面试官会判定你没在长对话上线过。
  2. 先把问题拆成三问再逐个答：什么时候压、压掉什么、保留什么。这个拆法本身就是加分项，因为它说明你把压缩当成一个策略而不是一个函数。
  3. 触发用阈值不用定时器，也不能等报错。给一个具体数字并解释它：历史占用到预算的七成就动手，因为摘要本身是一次模型调用，有延迟也可能失败，卡到九成再压，一旦摘要超时下一轮就直接撞窗口上限了——七成是留给自己的抢救时间。
  4. 压掉的是过程性内容：中间推理、已经被消费完的工具原始返回值、用户后来推翻的需求。它们的共同点是价值已经沉淀进后面的结论里。保留的是系统提示词（它不属于历史）、最近若干条原文、以及用户明确声明过的约束和事实——后者写错了模型会当场失忆。
  5. 再补一条别人不会说的：切口必须对齐到一轮的开头。切在 assistant 的 tool_calls 和对应的 tool 结果中间，下一次请求就有了悬空调用，多数厂商的 API 直接返回 400。这一条最能证明你真的调过。
  6. 可以预期的追问有两个。一是摘要该用哪个模型：用便宜的小模型就行，摘要是抽取任务不是推理任务，这也接上了 D4 的分层路由。二是摘要调用失败了怎么办：降级到不摘要的滑动窗口（直接丢最早的几轮），保证请求发得出去，别让压缩失败连带整轮对话失败。

  Key points

  • Threshold-triggered at about 70% of the history budget, because the summary call itself is a slow, fallible model call that needs headroom
  • Drop process, keep conclusions: discard intermediate reasoning and consumed raw tool payloads; keep the system prompt, the recent turns verbatim, and explicit user constraints and facts
  • Align the cut to a turn boundary — slicing between tool_calls and its tool result makes the next request fail with a 400
  • Compression is lossy and irreversible: keep an append-only original, send the compressed version, and read the original when you need to backtrack or fork
  • Summarize with a cheap small model, and degrade to a sliding window if the summary call fails so compression failure never fails the turn

  答题要点

  • 阈值触发：历史占用到预算七成就压，因为摘要本身是一次会失败、有延迟的模型调用，必须留抢救余量
  • 压过程、留结论：丢中间推理和已消费的工具原始返回，保留系统提示词、最近若干条原文、用户明确声明的约束与事实
  • 切口必须对齐到一轮开头，切在 tool_calls 与 tool 结果之间会让下一次请求返回 400
  • 压缩是有损且不可逆的：原始历史另存一份只追加，发给模型的是压缩版，需要回溯或分叉时读原始版
  • 摘要用便宜的小模型；摘要失败要能降级成滑动窗口，别让压缩失败连累整轮对话