Iter-4360dd15-0167-transcript
第 167 轮执行轨迹
→ call search_memory({"limit":10,"query":"4360dd15 NEXT_FOCUS insertion adjacent swap content words subsequence local insertion rewrite"})【tool:search_memory】
{"ok": true, "results": [{"title": "Iter-4360dd15-0162-transcript", "tags": "iteration transcript", "modified": "20260424231857000", "preview": "!! 第 162 轮执行轨迹
→ call search_memory({\\\"limit\\\":10,\\\"query\\\":\\\"4360dd15 PMC4083033 整句替换 判定模"}, {"title": "Iter-4360dd15-0164-transcript", "tags": "iteration transcript", "modified": "20260424232341000", "preview": "!! 第 164 轮执行轨迹 → call search_memory({\\\"limit\\\":10,\\\"query\\\":\\\"4360dd15 PMC4083033 rewrite local inse"}, {"title": "Iter-4360dd15-0060-transcript", "tags": "iteration transcript", "modified": "20260424200117000", "preview": "!! 第 60 轮执行轨迹 → call list_recent({"n":8})
→ call list_by_tag({"limit":20,"tag":"4360dd15"})
【tool:list_recent】
{"ok": true, "items": [{"title": "Iter-4360dd15-0166-reflection", "modified": "20260424232542000", "tags": "iteration reflection"}, {"title": "NEXT_FOCUS", "modified": "20260424232542000", "tags": "pinned focus"}, {"title": "纯插入与轻微重排要分开判", "modified": "20260424232542000", "tags": "lesson erratum verification subsequence counterexample"}, {"title": "Iter-4360dd15-0166-transcript", "modified": "20260424232537000", "tags": "iteration transcript"}, {"title": "Iter-4360dd15-0166-fact-minimal-insertion-reorder-counterexample", "modified": "20260424232532000", "tags": "fact 4360dd15 erratum verification"}, {"title": "Iter-4360dd15-0165-reflection", "modified": "20260424232439000", "tags": "iteration reflection"}, {"title": "先保骨架再谈编辑量", "modified": "20260424232439000", "tags": "lesson erratum verification rewrite local insertion"}, {"title": "Iter-4360dd15-0165-transcript", "modified": "20260424232436000", "tags": "iteration transcript"}]}
【tool:list_by_tag】
{"ok": true, "items": [{"title": "Iter-4360dd15-0166-fact-minimal-insertion-reorder-counterexample", "modified": "20260424232532000"}, {"title": "Iter-4360dd15-0165-method-content-subsequence-local-insertion", "modified": "20260424232430000"}, {"title": "Iter-4360dd15-0164-lesson-v2-rule-false-positives", "modified": "20260424232336000"}, {"title": "Iter-4360dd15-0160-method-pmc4083033-rewrite-v2", "modified": "20260424231537000"}, {"title": "Iter-4360dd15-0159-lesson-rewrite-threshold-failure", "modified": "20260424231322000"}, {"title": "Iter-4360dd15-0158-method-pmc4083033-sentence-rewrite-template", "modified": "20260424231132000"}, {"title": "Iter-4360dd15-0157-fact-pmc4083033-word-diff", "modified": "20260424231031000"}, {"title": "Iter-4360dd15-0156-fact-pmc4083033-xml-offsets", "modified": "20260424230924000"}, {"title": "Iter-4360dd15-0155-fact-pmc4083033-xml-coordinates", "modified": "20260424230807000"}, {"title": "Iter-4360dd15-0154-fact-pmc4083033-tokenization-robustness", "modified": "20260424230648000"}, {"title": "Iter-4360dd15-0153-fact-pmc4083033-direct-xml-diff", "modified": "20260424230526000"}, {"title": "Iter-4360dd15-0152-fact-pmc4083033-diff-type", "modified": "20260424230323000"}, {"title": "PMC4083033 erratum minimal diff", "modified": "20260424225923000"}, {"title": "Iter-4360dd15-0149-lesson-pmc4083033-not-insertion", "modified": "20260424225824000"}, {"title": "Iter-4360dd15-0145-fact-replacement-erratum-pressure-test", "modified": "20260424224847
→ call read_note({"title":"Iter-4360dd15-0165-method-content-subsequence-local-insertion"})
→ call read_note({"title":"Iter-4360dd15-0166-fact-minimal-insertion-reorder-counterexample"})
→ call read_note({"title":"NEXT_FOCUS"})
【tool:read_note】
{"ok": true, "title": "Iter-4360dd15-0165-method-content-subsequence-local-insertion", "tags": "method fact 4360dd15 erratum verification", "text": "!! 最窄二层判定候选：内容词有序子序列规则\n\n目标：把“局部插入”与“重写/替换”分开，避免继续被短句里的内容词插入误报。\n\n!! 规则草案\n先把句子抽成 content words（去停用词、只保留字母 token），然后：\n- 若较短句子的 content words 能作为 有序子序列 出现在较长句子中，则优先判为 local insertion；\n- 否则判为 rewrite 候选。\n\n这条规则对本轮样例的区分是：\n- A was observed in the sample. → A significant effect was observed in the sample.\n - content words: observed sample vs significant effect observed sample\n - 短句是长句的有序子序列 ⇒ local insertion\n- The result was significant in the sample. → The result was highly significant in the sample.\n - content words: result significant sample vs result highly significant sample\n - 短句是长句的有序子序列 ⇒ local insertion\n- We observed the effect. → We observed a strong effect.\n - content words: we observed effect vs we observed strong effect\n - 短句是长句的有序子序列 ⇒ local insertion\n- PMC4083033\n - female skin thicker males consistent many other previous studies vs\n men have thicker skin women consistent many other previous studies\n - 不是有序子序列：存在词替换和骨架改写 ⇒ rewrite\n\n!! 可复现代码\n
\nimport re\n\nSTOP = set('a an the in on at of to for and or was is are were be been being than do does did with by as from that this these those which'.split())\n\ndef content_words(s):\n return [t for t in re.findall(r'[a-z]+', s.lower()) if t not in STOP]\n\ndef is_subsequence(short,【tool:read_note】
{"ok": true, "title": "Iter-4360dd15-0166-fact-minimal-insertion-reorder-counterexample", "tags": "fact 4360dd15 [[erratum verification]]", "text": "!! 最小“插入 + 轻微重排”反例（抽象 token 级）\n\n本轮用 Python 穷举了一个最小模型：给短序列先做一次插入（插入 token `x`），再允许一次相邻交换。结果显示：\n\n- 当短序列长度 `n=1` 时，没有反例；\n- 当 `n=2` 时，立刻出现反例；\n- 因而 //content words 有序子序列// 规则对“插入 + 轻微重排”并不稳健，最小失效长度就是 2 个内容词。\n\n!! 具体反例\n短句：`a b`\n\n长句（由插入 + 轻微重排得到）：\n- `b a x`\n- `x b a`\n\n这两种都不是 `a b` 的有序子序列，因此规则会把它们误判为 `rewrite`，即使它们在编辑路径上只是“插入外加一次轻微重排”。\n\n!! 可复现代码\n
\n\n!! 含义\n这条规则可以作为“纯插入”候选判据，但一旦允许轻微重排，它就会产生系统性假阴性。下一步需要把“是否允许局部交换”显式纳入判定。"}【tool:read_note】
{"ok": true, "title": "NEXT_FOCUS", "tags": "pinned focus", "text": "!! 下轮焦点（来自 mission `4360dd15` · Iter-0166）\n\n形式化“插入 + 一次相邻交换”的最小判定修补，找出不把局部重排误判为 rewrite 的可计算条件。"}
→ call run_python({"code":"from itertools import product, permutations\n\ndef is_subsequence(short, long):\n it = iter(long)\n try:\n for x in short:\n while next(it) != x:\n pass\n return True\n except StopIteration:\n return False\n\n\ndef subseq_or_one_adj_swap(short, long):\n if is_subsequence(short, long):\n return True\n n = len(long)\n f)
【tool:run_python】
{"ok": true, "stdout": "1 2 0 []\n2 5 0 []\n3 10 0 []\n4 17 0 []\nfalse positives among abc^3 vs generated model: 11 [('a', 'a', 'b'), ('a', 'b', 'a'), ('a', 'b', 'b'), ('a', 'b', 'c'), ('a', 'c', 'b'), ('b', 'a', 'a'), ('b', 'a', 'b'), ('b', 'a', 'c'), ('b', 'b', 'a'), ('c', 'a', 'b'), ('c', 'b', 'a')]\n", "stderr": "", "returncode": 0}
→ call run_python({"code":"from itertools import combinations, product\n\ndef is_subsequence(short, long):\n it = iter(long)\n try:\n for x in short:\n while next(it) != x:\n pass\n return True\n except StopIteration:\n return False\n\n# repair rule candidate: short is a subsequence of long after at most one adjacent swap in long\n\ndef subseq_with_one_adj_swa)
【tool:run_python】
{"ok": true, "stdout": "1 fails 0\n2 fails 0\n3 fails 0\n4 fails 0\n5 fails 0\naccepted among abc^3 for short ab: 11 [('a', 'a', 'b'), ('a', 'b', 'a'), ('a', 'b', 'b'), ('a', 'b', 'c'), ('a', 'c', 'b'), ('b', 'a', 'a'), ('b', 'a', 'b'), ('b', 'a', 'c'), ('b', 'b', 'a'), ('c', 'a', 'b'), ('c', 'b', 'a')]\nfalse positives vs exact generated model: 11 [('a', 'a', 'b'), ('a', 'b', 'a'), ('a', 'b', 'b'), ('a', 'b', 'c'), ('a', 'c', 'b'), ('b', 'a', 'a'), ('b', 'a', 'b'), ('b', 'a', 'c'), ('b', 'b', 'a'), ('c', 'a', 'b'), ('c', 'b', 'a')]\n", "stderr": "", "returncode": 0}
→ call write_note({"tags":["method","fact","4360dd15","erratum verification"],"text":"!! 一次相邻交换修补：把局部重排纳入 subsequence 判定\n\n本轮把上一轮的“content words 有序子序列”规则做了一个最小修补：\n\n- 先对较长句做 //零次或一次相邻交换//；\n- 再检查较短句的 content words 是否为其有序子序列。\n\n这个修补对应的判定式是：\n\n
【tool:write_note】
{"ok": true, "path": "C:\\Users\\yh599\\Documents\\GitHub\\LLM-yh\\Sisyphus\\wiki\\tiddlers\\Iter-4360dd15-0167-method-one-swap-subsequence-repair.tid", "title": "Iter-4360dd15-0167-method-one-swap-subsequence-repair"}
【assistant】
本轮我把上一轮的最小反例边界做了一个可计算修补：**允许较长句先做一次相邻交换，再检查 content words 的有序子序列**。
结果：
- 对抽象 token 模型，我用 Python 穷举验证了 n=1..5 的“插入 + 一次相邻交换”生成样本，修补规则全部通过；
- 这说明它能把“局部重排但本质仍是插入”的情况从 rewrite 误判里救回来；
- 规则实现已写入长期记忆：Iter-4360dd15-0167-method-one-swap-subsequence-repair。
核心判定是：
def subseq_with_one_adj_swap(short, long):
if is_subsequence(short, long):
return True
for i in range(len(long)-1):
l = list(long)
l[i], l[i+1] = l[i+1], l[i]
if is_subsequence(short, l):
return True
return False
不过它也暴露出一个新问题：**一旦把“允许一次相邻交换”放进来，假阳性会明显增加**。所以下一轮最值得做的是把这个修补进一步收紧成“只对局部窗口交换有效”的版本。
<NEXT_FOCUS>把“一次相邻交换”限制在局部编辑窗口内，形式化一个不会把任意重排都误放行的最窄可计算条件</NEXT_FOCUS>
```