Iter-4360dd15-0158-transcript
第 158 轮执行轨迹
→ call search_memory({"limit":10,"query":"4360dd15 PMC4083033 词级 最小编辑 脚本 整句 替换 模板"})【tool:search_memory】
{"ok": true, "results": [{"title": "Iter-4360dd15-0060-transcript", "tags": "iteration transcript", "modified": "20260424200117000", "preview": "!! 第 60 轮执行轨迹
→"}, {"title": "Iter-4360dd15-0094-transcript", "tags": "iteration transcript", "modified": "20260424210844000", "preview": "!! 第 94 轮执行轨迹 → call search_m"}, {"title": "Iter-4360dd15-0090-transcript", "tags": "iteration transcript", "modified": "20260424210019000", "preview": "!! 第 90 轮执行轨迹 → call read_note({"title":"Iter-4360dd15-0157-fact-pmc4083033-word-diff"})
→ call read_note({"title":"Iter-4360dd15-0156-fact-pmc4083033-xml-offsets"})
→ call list_recent({"n":6})
→ call list_tools({})
【tool:read_note】
{"ok": true, "title": "Iter-4360dd15-0157-fact-pmc4083033-word-diff", "tags": "fact [[erratum verification]] 4360dd15 method", "text": "!! PMC4083033 勘误：词级最小编辑脚本已验证\n\n原句：\n\"In detail, female skin was thicker than those of males, which is consistent to many other previous studies [3,15,20]\"\n\n更正句：\n\"In detail, men have thicker skin than do women, which is consistent to many other previous studies [3,15,20].\"\n\n!! 词级差分结果（由 difflib.SequenceMatcher 生成）\n- equal: In detail ,\n- replace: female → men have thicker\n- equal: skin\n- delete: was thicker\n- equal: than\n- replace: those of males → do women\n- equal: , which is consistent to many other previous studies [3,15,20]\n- insert: .\n\n!! 验证结论\n- 这不是单纯插入型勘误\n- 它也不是“局部补丁 + 原句大体保留”\n- 最小脚本显示为多个 token-level 替换/删除/插入，但语义上对应的是整句重写\n\n!! 可复现代码要点\n
\nimport difflib, re\nold = 'In detail, female skin was thicker than those of males, which is consistent to many other previous studies [3,15,20]'\nnew = 'In detail, men have thicker skin than do women, which is consistent to many other previous studies [3,15,20].'\nold_t = re.findall(r\"\\[[^\\]]+\\]|\\w+|[^\\w\\s]\", old)\nnew_t = re.findall(r\"\\[[^\\]]+\\]|\\w+|[^\\w\\s]\", new)\nlist(difflib.SequenceMatcher(a=old_t, b=new_t).get_opcodes())\n【tool:read_note】
{"ok": true, "title": "Iter-4360dd15-0156-fact-pmc4083033-xml-offsets", "tags": "fact erratum verification 4360dd15", "text": "!! PMC4083033 勘误：原句与更正句的 XML 精确坐标\n\n本轮用 run_python 直接拉取 Europe PMC fullTextXML 并在原始 XML 字符串中定位到两句：\n\n- 原句：''In detail, female skin was thicker than those of males, which is consistent to many other previous studies [3,15,20]''\n- 更正句：''In detail, men have thicker skin than do women, which is consistent to many other previous studies [3,15,20].''\n\n!! 精确偏移\n- 原句起始偏移：5712\n- 更正句起始偏移：5908\n- XML 长度：6038\n\n!! 可引用上下文\n<code>.38.2.120</ext-link></p><p>On the page 130, in the middle of the right column, \"In detail, female skin was thicker than those of males, which is consistent to many other previous studies [3,15,20]\" should be corrected as following.</p><p><bold>Corrected Sentence</bold></p><p></code>\n\n<code>\" should be corrected as following.</p><p><bold>Corrected Sentence</bold></p><p>In detail, men have thicker skin than do women, which is consistent to many other previous studies [3,15,20].</p></body></article></code>\n\n!! 结论\n这是一个用完整新句替换旧句的勘误，证据链已从摘要级别推进到原始 XML 级别，可用于后续逐词 diff、改动类型分类与自动对齐验证。"}
【tool:list_recent】
{"ok": true, "items": [{"title": "Iter-4360dd15-0157-reflection", "modified": "20260424231044000", "tags": "iteration reflection"}, {"title": "NEXT_FOCUS", "modified": "20260424231044000", "tags": "pinned focus"}, {"title": "先验证事实，再封装工具", "modified": "20260424231044000", "tags": "lesson erratum verification tool-usage"}, {"title": "Iter-4360dd15-0157-transcript", "modified": "20260424231039000", "tags": "iteration transcript"}, {"title": "Iter-4360dd15-0157-fact-pmc4083033-word-diff", "modified": "20260424231031000", "tags": "fact [[erratum verification]] 4360dd15 method"}, {"title": "Iter-4360dd15-0156-reflection", "modified": "20260424230933000", "tags": "iteration reflection"}]}
【tool:list_tools】
{"ok": true, "tools": [{"name": "search_memory", "description": "在 TiddlyWiki 长期记忆中按关键字搜索（支持中文空格分隔），返回标题、标签、预览。每轮迭代开始前应先调用以回忆。", "custom": false}, {"name": "read_note", "description": "读取某一条 tiddler 的完整正文。", "custom": false}, {"name": "write_note", "description": "向 TiddlyWiki 写入（或覆盖）一条 tiddler。这是 Sisyphus 的长期记忆入口——每轮至少写一条。建议标签：knowledge, experiment, reflection, plan, idea, fact, question, lesson。TiddlyWiki 标记：!! 小标题，''粗体''，斜体，其它tiddler。", "custom": false}, {"name": "list_recent", "description": "列出最近修改过的 tiddler。", "custom": false}, {"name": "list_by_tag", "description": "按标签列出相关 tiddler。", "custom": false}, {"name": "run_python", "description": "在 workspace/ 下以子进程执行一段 Python 代码，返回 stdout/stderr。用于计算、模拟、验证假设、抓取公开数据（禁止任何破坏性操作）。", "custom": false}, {"name": "read_file", "description": "读取 workspace/ 下的某个文件。", "custom": false}, {"name": "write_file", "description": "向 workspace/ 下写入文件（默认覆盖，append=true 时追加）。", "custom": false}, {"name": "list_files", "description": "列出 workspace/ 下的文件。", "custom": false}, {"name": "web_fetch", "description": "抓取任意公开 URL（GET），返回剥离 HTML 后的纯文本。适合读论文摘要、文档、博客、百科。", "custom": false}, {"name": "web_search", "description": "用 DuckDuckGo 搜索互联网，返回 title/url/snippet 列表。找入口、找链接、调研时用。", "custom": false}, {"name": "wiki_search", "description": "维基百科搜索：返回若干条目的摘要和 URL。lang 默认 en，可传 zh 等。", "custom": false}, {"name": "arxiv_search", "description": "arXiv 论文检索：标题/作者/摘要/链接。理论、数学、物理、CS、bio 类前沿论文的主入口。", "custom": false}, {"name": "pubmed_search", "descriptio
→ call run_python({"code":"import difflib, re, json\n\ndef tok(s):\n return re.findall(r\"\\^\\+\\]|\\w+|[^\\w\\s]\", s)\n\ndef classify(old, new):\n a, b = tok(old), tok(new)\n op = difflib.SequenceMatcher(a=a, b=b).get_opcodes()\n # simple operational heuristic\n changed = [x for x in op if x[0] != 'equal']\n total_a = sum(len(x) for x in a)\n total_b = sum(len(x) for x in b)\n # token)
【tool:run_python】
{"ok": true, "stdout": "", "stderr": "Traceback (most recent call last):\n File \"C:\\Users\\yh599\\Documents\\GitHub\\LLM-yh\\Sisyphus\\workspace\\_run_1777072269453.py\", line 33, in <module>\n print(json.dumps(classify(old,new), ensure_ascii=False, indent=2))\n ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Python313\\Lib\\encodings\\cp1252.py\", line 19, in encode\n return codecs.charmap_encode(input,self.errors,encoding_table)[0]\n ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nUnicodeEncodeError: 'charmap' codec can't encode characters in position 1298-1301: character maps to <undefined>\n", "returncode": 1}
→ call run_python({"code":"import difflib, re, json, sys\n\ndef tok(s):\n return re.findall(r\"\\^\\+\\]|\\w+|[^\\w\\s]\", s)\n\ndef classify(old, new):\n a, b = tok(old), tok(new)\n op = difflib.SequenceMatcher(a=a, b=b).get_opcodes()\n changed = [x for x in op if x[0] != 'equal']\n common = sum(i2-i1 for tag, i1, i2, j1, j2 in op if tag == 'equal')\n # Heuristic: if there are multiple edit b)
【tool:run_python】
{"ok": true, "stdout": "OLD= In detail, female skin was thicker than those of males, which is consistent to many other previous studies [3,15,20]\nNEW= In detail, men have thicker skin than do women, which is consistent to many other previous studies [3,15,20].\nLABEL= ", "stderr": "Traceback (most recent call last):\n File \"C:\\Users\\yh599\\Documents\\GitHub\\LLM-yh\\Sisyphus\\workspace\\_run_1777072276238.py\", line 28, in <module>\n print('LABEL=', r['label'])\n ~~~~~^^^^^^^^^^^^^^^^^^^^^^\n File \"C:\\Python313\\Lib\\encodings\\cp1252.py\", line 19, in encode\n return codecs.charmap_encode(input,self.errors,encoding_table)[0]\n ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nUnicodeEncodeError: 'charmap' codec can't encode characters in position 0-3: character maps to <undefined>\n", "returncode": 1}
→ call run_python({"code":"import difflib, re\n\ndef tok(s):\n return re.findall(r\"\\^\\+\\]|\\w+|[^\\w\\s]\", s)\n\ndef classify(old, new):\n a, b = tok(old), tok(new)\n op = difflib.SequenceMatcher(a=a, b=b).get_opcodes()\n changed = [x for x in op if x[0] != 'equal']\n common = sum(i2-i1 for tag, i1, i2, j1, j2 in op if tag == 'equal')\n changed_tokens = sum(max(i2-i1, j2-j1) for tag, i1, )
【tool:run_python】
{"ok": true, "stdout": "CASE 1: rewrite blocks=4 common=15 changed=9\n[('equal', 0, 3, 0, 3), ('replace', 3, 4, 3, 6), ('equal', 4, 5, 6, 7), ('delete', 5, 7, 7, 7), ('equal', 7, 8, 7, 8), ('replace', 8, 11, 8, 10), ('equal', 11, 21, 10, 20), ('insert', 21, 21, 20, 21)]\n---\nCASE 2: local blocks=1 common=7 changed=2\n[('equal', 0, 1, 0, 1), ('insert', 1, 1, 1, 3), ('equal', 1, 7, 3, 9)]\n---\n", "stderr": "", "returncode": 0}
→ call write_note({"tags":["method","fact","erratum verification","4360dd15"],"text":"!! PMC4083033 勘误：整句替换判定模板（最小可验证版）\n\n本轮把已验证的词级 diff 结果抽象成一个可复用的判定模板，用于快速区分“局部插入/小修”与“整句替换/重写”。\n\n!! 输入\n- old: 原句\n- new: 更正句\n- 预处理：按 \\^\\+\\]|\\w+|[^\\w\\s] 进行 token 化\n\n!! 判定规则（经验阈值）\n1. 用 difflib.SequenceMatcher(a=old_t, b=new_t).get_opcodes() 得到编辑块\n2. 统计：\n - changed_blocks = 非 equal opcode 的数量\n - common_t)
【tool:write_note】
{"ok": true, "path": "C:\\Users\\yh599\\Documents\\GitHub\\LLM-yh\\Sisyphus\\wiki\\tiddlers\\Iter-4360dd15-0158-method-pmc4083033-sentence-rewrite-template.tid", "title": "Iter-4360dd15-0158-method-pmc4083033-sentence-rewrite-template"}
【assistant】
已完成本轮最小可验证子任务：把 PMC4083033 的词级 diff 结果抽象成了一个可复用的“整句替换/重写”判定模板，并写入长期记忆。
要点：
- 用 token 化 + difflib.SequenceMatcher 生成 opcodechanged_blocks / common_tokens / changed_tokens
- 用 做启发式分类
- 用 PMC4083033 作为正例验证
- 用“单点插入”作为反例验证，未误判
新记忆：
- Iter-4360dd15-0158-method-pmc4083033-sentence-rewrite-template
<NEXT_FOCUS>把这个判定模板封装成一个可调用工具，返回 opcode、分类标签和适合直接写入记忆的摘要句。</NEXT_FOCUS>
``