{"id":16744,"date":"2026-08-11T23:11:10","date_gmt":"2026-08-11T13:11:10","guid":{"rendered":"https:\/\/geoffreychen.com\/?p=16744"},"modified":"2026-08-11T23:18:06","modified_gmt":"2026-08-11T13:18:06","slug":"what-ai-remembers-and-work-must-preserve","status":"publish","type":"post","link":"https:\/\/geoffreychen.com\/zh\/what-ai-remembers-and-work-must-preserve\/","title":{"rendered":"AI\u4f1a\u8bb0\u4f4f\u4ec0\u4e48\uff0c\u5de5\u4f5c\u7cfb\u7edf\u53c8\u5fc5\u987b\u4fdd\u5b58\u4ec0\u4e48\uff1f"},"content":{"rendered":"<\/p>\n<p><em>\u300aAI\u8fdb\u5165\u5de5\u4f5c\u4e4b\u540e\u300b\u00b7 \u7b2c\u4e00\u5b63\u201c\u4ece\u56de\u7b54\u95ee\u9898\u5230\u53c2\u4e0e\u5de5\u4f5c\u201d\u00b7 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\u5fc6\u201d\uff0c\u5c31\u5f88\u96be\u8bbe\u8ba1\u6b63\u786e\u7684\u7ea0\u9519\u65b9\u6cd5\u3002\u89e3\u51b3\u4e0a\u4e0b\u6587\u8fc7\u957f\uff0c\u9700\u8981\u9009\u62e9\u3001\u6458\u8981\u548c\u68c0\u7d22\uff1b\u89e3\u51b3\u6b63\u5f0f\u8bb0\u5f55\u7f3a\u5931\uff0c\u9700\u8981\u6863\u6848\u548c\u7248\u672c\u7ba1\u7406\uff1b\u89e3\u51b3\u6570\u636e\u9519\u8bef\uff0c\u5219\u9700\u8981\u6765\u6e90\u4fee\u6b63\u548c\u6743\u9650\u63a7\u5236\u3002\u8ba9AI\u201c\u518d\u8bb0\u7262\u4e00\u70b9\u201d\u4e0d\u662f\u540c\u4e00\u4e2a\u6280\u672f\u95ee\u9898\u3002<\/p>\n<h2>\u4e0a\u4e0b\u6587\u5f88\u957f\uff0c\u4e0d\u7b49\u4e8e\u5de5\u4f5c\u8bb0\u5fc6\u53ef\u9760<\/h2>\n<p>\u6a21\u578b\u80fd\u591f\u63a5\u6536\u66f4\u591a\u6587\u5b57\uff0c\u786e\u5b9e\u6269\u5c55\u4e86\u53ef\u5904\u7406\u4efb\u52a1\u7684\u8303\u56f4\u3002\u957f\u62a5\u544a\u3001\u591a\u4e2a\u6587\u4ef6\u548c\u8f83\u957f\u5bf9\u8bdd\u53ef\u4ee5\u540c\u65f6\u8fdb\u5165\u4e00\u6b21\u4efb\u52a1\u3002<\/p>\n<p>\u4f46\u5bb9\u91cf\u4e0d\u662f\u4f7f\u7528\u8d28\u91cf\u3002\u4e00\u9879\u53d1\u8868\u4e8e\u300aTransactions of the Association for Computational Linguistics\u300b\u7684\u7814\u7a76\u53d1\u73b0\uff0c\u5728\u957f\u4e0a\u4e0b\u6587\u95ee\u7b54\u4efb\u52a1\u4e2d\uff0c\u4e00\u4e9b\u6a21\u578b\u66f4\u5584\u4e8e\u5229\u7528\u51fa\u73b0\u5728\u5f00\u5934\u6216\u7ed3\u5c3e\u7684\u4fe1\u606f\uff0c\u800c\u76f8\u5173\u5185\u5bb9\u4f4d\u4e8e\u4e2d\u95f4\u65f6\u8868\u73b0\u4e0b\u964d\u3002<a href=\"https:\/\/direct.mit.edu\/tacl\/article\/doi\/10.1162\/tacl_a_00638\/119630\/Lost-in-the-Middle-How-Language-Models-Use-Long\" target=\"_blank\" rel=\"noopener\">TACL\uff1a\u300aLost in the Middle: How Language Models Use Long Contexts\u300b<\/a><\/p>\n<p>\u6a21\u578b\u80fd\u529b\u6301\u7eed\u53d8\u5316\uff0c\u5177\u4f53\u8868\u73b0\u4e0d\u80fd\u6c38\u4e45\u5957\u7528\u8fd9\u9879\u7814\u7a76\u3002\u4f46\u5b83\u63ed\u793a\u7684\u7ed3\u6784\u95ee\u9898\u4ecd\u7136\u91cd\u8981\uff1a\u4e00\u6761\u4fe1\u606f\u8fdb\u5165\u4e0a\u4e0b\u6587\uff0c\u4e0d\u7b49\u4e8e\u7cfb\u7edf\u5df2\u7ecf\u6b63\u786e\u8bc6\u522b\u5176\u91cd\u8981\u6027\uff0c\u66f4\u4e0d\u7b49\u4e8e\u5b83\u4f1a\u5728\u9700\u8981\u65f6\u7a33\u5b9a\u4f7f\u7528\u3002<\/p>\n<p>\u8fd9\u4e0e\u73b0\u5b9e\u5de5\u4f5c\u5f88\u76f8\u4f3c\u3002\u628a\u6240\u6709\u90ae\u4ef6\u3001\u4f1a\u8bae\u8bb0\u5f55\u548c\u8349\u7a3f\u653e\u8fdb\u4e00\u4e2a\u5171\u4eab\u76d8\uff0c\u5e76\u4e0d\u4f1a\u81ea\u52a8\u5f62\u6210\u7ec4\u7ec7\u8bb0\u5fc6\u3002\u771f\u6b63\u6709\u7528\u7684\u8bb0\u5fc6\u9700\u8981\u5206\u7c7b\u3001\u72b6\u6001\u3001\u5173\u7cfb\u548c\u68c0\u7d22\u8def\u5f84\u3002\u5426\u5219\u8d44\u6599\u8d8a\u591a\uff0c\u9519\u8bef\u7248\u672c\u4e0e\u91cd\u8981\u51b3\u5b9a\u8d8a\u5bb9\u6613\u6df7\u5728\u4e00\u8d77\u3002<\/p>\n<p>Anthropic\u5173\u4e8e\u4e0a\u4e0b\u6587\u5de5\u7a0b\u7684\u8bf4\u660e\u4e5f\u5f3a\u8c03\uff0c\u5e94\u628a\u4e0a\u4e0b\u6587\u89c6\u4e3a\u6709\u9650\u8d44\u6e90\uff0c\u901a\u8fc7\u5373\u65f6\u68c0\u7d22\u3001\u538b\u7f29\u3001\u7ed3\u6784\u5316\u7b14\u8bb0\u548c\u5de5\u5177\u8c03\u7528\uff0c\u628a\u771f\u6b63\u9700\u8981\u7684\u4fe1\u606f\u653e\u5230\u6a21\u578b\u5f53\u524d\u53ef\u4ee5\u6709\u6548\u4f7f\u7528\u7684\u4f4d\u7f6e\u3002<a href=\"https:\/\/www.anthropic.com\/engineering\/effective-context-engineering-for-ai-agents\" target=\"_blank\" rel=\"noopener\">Anthropic\uff1a\u300aEffective context engineering for AI agents\u300b<\/a><\/p>\n<p>\u56e0\u6b64\uff0c\u201c\u6a21\u578b\u652f\u6301\u591a\u5c11\u4e0a\u4e0b\u6587\u201d\u53ea\u662f\u5bb9\u91cf\u95ee\u9898\u3002\u5de5\u4f5c\u7cfb\u7edf\u8fd8\u5fc5\u987b\u56de\u7b54\uff1a\u54ea\u4e9b\u5185\u5bb9\u5e94\u5f53\u8fdb\u5165\uff0c\u8c01\u51b3\u5b9a\u5176\u91cd\u8981\u6027\uff0c\u51b2\u7a81\u65f6\u600e\u6837\u6392\u5e8f\uff0c\u8fc7\u671f\u5185\u5bb9\u4f55\u65f6\u9000\u51fa\u3002<\/p>\n<h2>\u68c0\u7d22\u589e\u5f3a\u5e76\u4e0d\u4f1a\u81ea\u52a8\u5efa\u7acb\u4e8b\u5b9e\u6765\u6e90<\/h2>\n<p>\u68c0\u7d22\u589e\u5f3a\u751f\u6210\u628a\u5916\u90e8\u8d44\u6599\u4e0e\u6a21\u578b\u751f\u6210\u7ed3\u5408\u8d77\u6765\u3002\u7cfb\u7edf\u6536\u5230\u95ee\u9898\u540e\uff0c\u5148\u4ece\u77e5\u8bc6\u5e93\u627e\u51fa\u76f8\u5173\u7247\u6bb5\uff0c\u518d\u8ba9\u6a21\u578b\u6839\u636e\u8fd9\u4e9b\u7247\u6bb5\u4f5c\u7b54\u3002\u5b83\u7684\u4e00\u4e2a\u91cd\u8981\u4ef7\u503c\uff0c\u662f\u77e5\u8bc6\u5e93\u53ef\u4ee5\u66f4\u65b0\uff0c\u4e5f\u53ef\u4ee5\u4fdd\u7559\u6a21\u578b\u56de\u7b54\u6240\u4f9d\u636e\u7684\u6750\u6599\u3002<a href=\"https:\/\/papers.neurips.cc\/paper\/2020\/hash\/6b493230205f780e1bc26945df7481e5-Abstract.html\" target=\"_blank\" rel=\"noopener\">NeurIPS\uff1a\u300aRetrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\u300b<\/a><\/p>\n<p>\u4f46\u201c\u80fd\u68c0\u7d22\u201d\u4e0e\u201c\u6709\u53ef\u9760\u8bb0\u5f55\u201d\u4ecd\u7136\u4e0d\u540c\u3002<\/p>\n<p>\u5047\u8bbe\u77e5\u8bc6\u5e93\u4e2d\u540c\u65f6\u4fdd\u5b58\u4e09\u4efd\u9000\u6b3e\u653f\u7b56\uff1a\u4e00\u4efd2024\u5e74\u7248\u672c\uff0c\u4e00\u4efd2025\u5e74\u8349\u7a3f\uff0c\u4e00\u4efd2026\u5e74\u5df2\u7ecf\u6279\u51c6\u7684\u73b0\u884c\u7248\u672c\u3002\u68c0\u7d22\u7cfb\u7edf\u53ef\u80fd\u56e0\u4e3a\u63aa\u8f9e\u66f4\u63a5\u8fd1\u95ee\u9898\u800c\u8fd4\u56de\u65e7\u6587\u4ef6\u3002\u5982\u679c\u6587\u4ef6\u6ca1\u6709\u7248\u672c\u3001\u6709\u6548\u65e5\u671f\u548c\u6279\u51c6\u72b6\u6001\uff0c\u6a21\u578b\u65e0\u6cd5\u4ec5\u51ed\u8bed\u8a00\u786e\u5b9a\u54ea\u4e00\u4efd\u5177\u6709\u6743\u5a01\u3002<\/p>\n<p>\u53c8\u5982\uff0c\u4f1a\u8bae\u7eaa\u8981\u5199\u7740\u201c\u56e2\u961f\u503e\u5411\u4e8e\u628a\u53d1\u5e03\u65e5\u671f\u5ef6\u540e\u4e00\u5468\u201d\uff0c\u540e\u6765\u8d1f\u8d23\u4eba\u5728\u6b63\u5f0f\u8ba1\u5212\u4e2d\u51b3\u5b9a\u4e0d\u5ef6\u671f\u3002\u5982\u679cAI\u53ea\u68c0\u7d22\u5230\u4f1a\u8bae\u7eaa\u8981\uff0c\u5b83\u53ef\u80fd\u628a\u503e\u5411\u5199\u6210\u51b3\u5b9a\u3002\u9519\u8bef\u4e0d\u662f\u56e0\u4e3a\u6587\u5b57\u4e0d\u5b58\u5728\uff0c\u800c\u662f\u7cfb\u7edf\u6ca1\u6709\u8868\u8fbe\u4e0d\u540c\u8bb0\u5f55\u7684\u5236\u5ea6\u5730\u4f4d\u3002<\/p>\n<p>\u4e00\u4e2a\u5de5\u4f5c\u77e5\u8bc6\u5e93\u81f3\u5c11\u9700\u8981\u4fdd\u5b58\uff1a<\/p>\n<ul>\n<li>\u539f\u59cb\u6587\u4ef6\u53ca\u5176\u6765\u6e90\uff1b<\/li>\n<li>\u521b\u5efa\u3001\u6279\u51c6\u548c\u5931\u6548\u65e5\u671f\uff1b<\/li>\n<li>\u8349\u7a3f\u3001\u5efa\u8bae\u3001\u8bc1\u636e\u4e0e\u6b63\u5f0f\u51b3\u5b9a\u7684\u533a\u522b\uff1b<\/li>\n<li>\u6587\u4ef6\u4e4b\u95f4\u7684\u66ff\u4ee3\u548c\u5f15\u7528\u5173\u7cfb\uff1b<\/li>\n<li>\u8c01\u80fd\u591f\u8bfb\u53d6\u3001\u4fee\u6539\u548c\u6279\u51c6\uff1b<\/li>\n<li>\u53d1\u751f\u53d8\u66f4\u65f6\u7684\u5386\u53f2\u7248\u672c\u3002<\/li>\n<\/ul>\n<p>\u8fd9\u4e9b\u5143\u6570\u636e\u4e0d\u662f\u6280\u672f\u9644\u5c5e\u54c1\u3002\u5b83\u4eec\u544a\u8bc9AI\uff0c\u4e5f\u544a\u8bc9\u540e\u6765\u63a5\u624b\u7684\u4eba\uff0c\u4e00\u6bb5\u6587\u5b57\u5728\u5de5\u4f5c\u4e2d\u7a76\u7adf\u7b97\u4ec0\u4e48\u3002<\/p>\n<h2>\u5de5\u4f5c\u9700\u8981\u4fdd\u5b58\u7684\u662f\u201c\u53ef\u8ffd\u6eaf\u72b6\u6001\u201d<\/h2>\n<p>\u9879\u76ee\u8fde\u7eed\u6027\u4e0d\u5e94\u4f9d\u8d56\u67d0\u4e2a\u4eba\u6216\u67d0\u4e2aAI\u8bb0\u5f97\u6240\u6709\u4e8b\u60c5\u3002\u5b83\u9700\u8981\u4e00\u4e2a\u53ef\u4ee5\u88ab\u5171\u540c\u8bfb\u53d6\u3001\u6838\u5bf9\u548c\u66f4\u65b0\u7684\u72b6\u6001\u3002<\/p>\n<p>\u4f8b\u5982\uff0c\u4e00\u9879\u8f6f\u4ef6\u5f00\u53d1\u4efb\u52a1\u81f3\u5c11\u53ef\u80fd\u5305\u542b\uff1a\u9700\u6c42\u8bf4\u660e\u3001\u5f53\u524d\u5206\u652f\u3001\u5df2\u77e5\u7f3a\u9677\u3001\u6d4b\u8bd5\u7ed3\u679c\u3001\u5c1a\u672a\u51b3\u5b9a\u7684\u95ee\u9898\u548c\u53d1\u5e03\u6279\u51c6\u3002\u804a\u5929\u53ef\u4ee5\u5e2e\u52a9\u8ba8\u8bba\u8fd9\u4e9b\u5185\u5bb9\uff0c\u5374\u4e0d\u5e94\u6210\u4e3a\u5b83\u4eec\u552f\u4e00\u7684\u4fdd\u5b58\u4f4d\u7f6e\u3002\u5426\u5219\u4e00\u65e6\u5bf9\u8bdd\u88ab\u538b\u7f29\u3001\u4eba\u5458\u79bb\u5f00\u3001\u6a21\u578b\u66f4\u6362\u6216\u9879\u76ee\u8fc1\u79fb\uff0c\u5de5\u4f5c\u72b6\u6001\u5c31\u4f1a\u53d8\u5f97\u6a21\u7cca\u3002<\/p>\n<p>\u66f4\u7a33\u59a5\u7684\u7ed3\u6784\u53ef\u4ee5\u5206\u4e3a\u56db\u5c42\uff1a<\/p>\n<ol>\n<li><strong>\u5bf9\u8bdd\u5c42<\/strong>\uff1a\u4fdd\u5b58\u63a2\u7d22\u3001\u63d0\u95ee\u548c\u4e34\u65f6\u8ba8\u8bba\uff1b<\/li>\n<li><strong>\u5de5\u4f5c\u72b6\u6001\u5c42<\/strong>\uff1a\u4fdd\u5b58\u4efb\u52a1\u3001\u8d1f\u8d23\u4eba\u3001\u8fdb\u5ea6\u3001\u963b\u585e\u548c\u4e0b\u4e00\u6b65\uff1b<\/li>\n<li><strong>\u77e5\u8bc6\u5c42<\/strong>\uff1a\u4fdd\u5b58\u7ecf\u6574\u7406\u7684\u4e8b\u5b9e\u3001\u89c4\u5219\u3001\u8bbe\u8ba1\u8bf4\u660e\u548c\u6765\u6e90\uff1b<\/li>\n<li><strong>\u6b63\u5f0f\u8bb0\u5f55\u5c42<\/strong>\uff1a\u4fdd\u5b58\u5df2\u7ecf\u6279\u51c6\u7684\u51b3\u5b9a\u3001\u53d1\u5e03\u7248\u672c\u3001\u5408\u540c\u548c\u5ba1\u8ba1\u8bb0\u5f55\u3002<\/li>\n<\/ol>\n<p>AI\u53ef\u4ee5\u53c2\u4e0e\u6bcf\u4e00\u5c42\uff0c\u4f46\u4e0d\u80fd\u628a\u56db\u5c42\u538b\u6210\u540c\u4e00\u6bb5\u804a\u5929\u3002\u5bf9\u8bdd\u4e2d\u7684\u4e00\u53e5\u201c\u597d\u7684\uff0c\u6211\u4eec\u4ee5\u540e\u90fd\u8fd9\u6837\u505a\u201d\uff0c\u53ea\u6709\u8fdb\u5165\u51b3\u5b9a\u8bb0\u5f55\u5e76\u6807\u660e\u6279\u51c6\u8005\u4e0e\u9002\u7528\u8303\u56f4\uff0c\u624d\u6210\u4e3a\u7ec4\u7ec7\u53ef\u4ee5\u4f9d\u8d56\u7684\u4e8b\u5b9e\u3002<\/p>\n<p>\u7f8e\u56fd\u56fd\u5bb6\u6807\u51c6\u4e0e\u6280\u672f\u7814\u7a76\u9662\u7684AI\u98ce\u9669\u7ba1\u7406\u6846\u67b6\u628a\u6587\u6863\u8bb0\u5f55\u89c6\u4e3a\u900f\u660e\u5ea6\u3001\u4eba\u7c7b\u590d\u6838\u4e0e\u95ee\u8d23\u7684\u91cd\u8981\u57fa\u7840\uff0c\u5e76\u8981\u6c42\u7ec4\u7ec7\u533a\u5206\u4eba\u673a\u914d\u7f6e\u4e2d\u7684\u89d2\u8272\u548c\u8d23\u4efb\u3002<a href=\"https:\/\/airc.nist.gov\/airmf-resources\/airmf\/5-sec-core\/\" target=\"_blank\" rel=\"noopener\">NIST AI Resource Center\uff1a\u300aAI RMF Core\u300b<\/a><\/p>\n<p>\u8fd9\u4e0d\u662f\u56e0\u4e3a\u6240\u6709AI\u4f7f\u7528\u90fd\u8981\u53d8\u6210\u590d\u6742\u5ba1\u8ba1\u3002\u98ce\u9669\u5f88\u4f4e\u7684\u4e2a\u4eba\u8349\u7a3f\u53ef\u4ee5\u4fdd\u6301\u8f7b\u91cf\u3002\u4f46\u53ea\u8981\u7ed3\u679c\u4f1a\u5f71\u54cd\u4ed6\u4eba\u3001\u4ea7\u751f\u8d39\u7528\u3001\u6539\u53d8\u6b63\u5f0f\u7cfb\u7edf\u6216\u88ab\u516c\u5f00\u91c7\u7528\uff0c\u8bb0\u5f55\u5f3a\u5ea6\u5c31\u5e94\u968f\u4e4b\u540e\u679c\u63d0\u9ad8\u3002<\/p>\n<h2>AI\u7684\u4e2a\u6027\u5316\u8bb0\u5fc6\u8fd8\u5e26\u6765\u53e6\u4e00\u7c7b\u95ee\u9898<\/h2>\n<p>\u4e2a\u4eba\u5316AI\u5e0c\u671b\u901a\u8fc7\u4fdd\u5b58\u7528\u6237\u504f\u597d\u51cf\u5c11\u91cd\u590d\u8bf4\u660e\u3002\u8fd9\u5f88\u6709\u4ef7\u503c\uff1a\u8bed\u8a00\u3001\u683c\u5f0f\u3001\u5174\u8da3\u548c\u957f\u671f\u76ee\u6807\u80fd\u591f\u4fdd\u6301\u8fde\u7eed\uff0c\u7cfb\u7edf\u4e5f\u66f4\u5bb9\u6613\u7406\u89e3\u7701\u7565\u8868\u8fbe\u3002<\/p>\n<p>\u4f46\u504f\u597d\u4e0e\u4e8b\u5b9e\u4e0d\u80fd\u6df7\u4e3a\u4e00\u8c08\u3002\u201c\u7528\u6237\u559c\u6b22\u7b80\u6d01\u56de\u7b54\u201d\u662f\u4e00\u9879\u4ea4\u4e92\u504f\u597d\uff1b\u201c\u7528\u6237\u5df2\u7ecf\u6279\u51c6\u9879\u76ee\u9884\u7b97\u201d\u662f\u4e00\u9879\u6b63\u5f0f\u72b6\u6001\u3002\u524d\u8005\u53ef\u4ee5\u7531\u4e2a\u4eba\u8bb0\u5fc6\u6539\u5584\u4f53\u9a8c\uff0c\u540e\u8005\u5fc5\u987b\u7531\u53ef\u6838\u67e5\u8bb0\u5f55\u8bc1\u660e\u3002<\/p>\n<p>\u4e2a\u4eba\u8bb0\u5fc6\u8fd8\u53ef\u80fd\u53d1\u751f\u4e09\u4e2a\u95ee\u9898\u3002<\/p>\n<p>\u4e00\u662f<strong>\u8fc7\u5ea6\u6982\u62ec<\/strong>\u3002\u7528\u6237\u5728\u7279\u5b9a\u4efb\u52a1\u4e2d\u8868\u8fbe\u4e00\u6b21\u504f\u597d\uff0c\u7cfb\u7edf\u5374\u628a\u5b83\u5f53\u6210\u6c38\u4e45\u4e60\u60ef\u3002<\/p>\n<p>\u4e8c\u662f<strong>\u8fc7\u65f6<\/strong>\u3002\u4eba\u7684\u76ee\u6807\u548c\u89c2\u70b9\u4f1a\u6539\u53d8\uff0c\u8fc7\u53bb\u6b63\u786e\u7684\u6982\u62ec\u53ef\u80fd\u5728\u540e\u6765\u8bef\u5bfc\u7cfb\u7edf\u3002<\/p>\n<p>\u4e09\u662f<strong>\u4e0d\u53ef\u89c1\u5f71\u54cd<\/strong>\u3002\u5982\u679c\u7528\u6237\u4e0d\u77e5\u9053\u7cfb\u7edf\u4fdd\u5b58\u4e86\u4ec0\u4e48\uff0c\u5c31\u5f88\u96be\u7406\u89e3\u4e3a\u4ec0\u4e48AI\u6301\u7eed\u4ee5\u67d0\u79cd\u65b9\u5f0f\u56de\u7b54\uff0c\u4e5f\u96be\u4ee5\u4fee\u6b63\u9519\u8bef\u5370\u8c61\u3002<\/p>\n<p>\u56e0\u6b64\uff0c\u4e2a\u6027\u5316\u8bb0\u5fc6\u5e94\u5f53\u5141\u8bb8\u67e5\u770b\u3001\u4fee\u6539\u3001\u5220\u9664\u548c\u533a\u5206\u9002\u7528\u8303\u56f4\u3002\u9690\u79c1\u4fdd\u62a4\u4e5f\u4e0d\u80fd\u53ea\u4f9d\u9760\u6a21\u578b\u201c\u5584\u610f\u4f7f\u7528\u201d\u3002\u7f8e\u56fd\u56fd\u5bb6\u6807\u51c6\u4e0e\u6280\u672f\u7814\u7a76\u9662\u7684\u9690\u79c1\u6846\u67b6\u5f3a\u8c03\uff0c\u7ec4\u7ec7\u9700\u8981\u8bc6\u522b\u5e76\u7ba1\u7406\u6570\u636e\u5904\u7406\u5e26\u6765\u7684\u9690\u79c1\u98ce\u9669\uff0c\u800c\u4e0d\u53ea\u662f\u4fdd\u8bc1\u7cfb\u7edf\u529f\u80fd\u53ef\u7528\u3002<a href=\"https:\/\/www.nist.gov\/privacy-framework\" target=\"_blank\" rel=\"noopener\">NIST\uff1a\u300aPrivacy 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href=\"https:\/\/airc.nist.gov\/airmf-resources\/airmf\/5-sec-core\/\" target=\"_blank\" rel=\"noopener\">NIST AI Resource Center\uff1aAI RMF Core<\/a><\/li>\n<li><a href=\"https:\/\/www.nist.gov\/privacy-framework\" target=\"_blank\" rel=\"noopener\">NIST\uff1aPrivacy Framework<\/a><\/li>\n<li><a href=\"https:\/\/www.anthropic.com\/engineering\/effective-context-engineering-for-ai-agents\" target=\"_blank\" rel=\"noopener\">Anthropic\uff1aEffective context engineering for AI agents<\/a><\/li>\n<li><a href=\"https:\/\/direct.mit.edu\/tacl\/article\/doi\/10.1162\/tacl_a_00638\/119630\/Lost-in-the-Middle-How-Language-Models-Use-Long\" target=\"_blank\" rel=\"noopener\">Nelson F. Liu et al.\uff1aLost in the Middle: How Language Models Use Long Contexts<\/a><\/li>\n<li><a href=\"https:\/\/papers.neurips.cc\/paper\/2020\/hash\/6b493230205f780e1bc26945df7481e5-Abstract.html\" target=\"_blank\" rel=\"noopener\">Patrick Lewis et al.\uff1aRetrieval-Augmented Generation for Knowledge-Intensive NLP Tasks<\/a><\/li>\n<\/ul>\n<p><strong>\u7ee7\u7eed\u9605\u8bfb\uff1a<\/strong><a href=\"https:\/\/geoffreychen.com\/zh\/category\/ai-frontiers\/after-ai-enters-the-workflow\/\">\u6d4f\u89c8\u300aAI\u8fdb\u5165\u5de5\u4f5c\u4e4b\u540e\u300b\u7cfb\u5217\u6587\u7ae0<\/a><\/p>\n<p>","protected":false},"excerpt":{"rendered":"","protected":false},"author":17897162,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_geoff_meta_description_en":"Explains that AI can appear to remember via context, retrieval and personalization, but work systems must preserve authoritative, traceable records and decision provenance.","_geoff_meta_description_zh":"\u6307\u51faAI\u770b\u4f3c\u8bb0\u5f97\u53ef\u80fd\u53ea\u662f\u4e0a\u4e0b\u6587\u3001\u68c0\u7d22\u6216\u4e2a\u6027\u5316\u673a\u5236\uff0c\u4f46\u5de5\u4f5c\u7cfb\u7edf\u5fc5\u987b\u72ec\u7acb\u4fdd\u5b58\u53ef\u8ffd\u6eaf\u7684\u4e8b\u5b9e\u6765\u6e90\u3001\u6279\u51c6\u51b3\u5b9a\u4e0e\u7248\u672c\u72b6\u6001\u3002","_wpcom_ai_launchpad_first_post":false,"_jetpack_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[787296333],"tags":[787296336,787296347,787296345,787296338,787294476,787296334,787294483,787296335],"class_list":["post-16744","post","type-post","status-publish","format-standard","hentry","category-after-ai-enters-the-workflow","tag-after-ai-enters-the-workflow","tag-ai-memory","tag-ai-memory-zh","tag-after-ai-enters-the-workflow-zh","tag-787294476","tag-from-answering-questions-to-participating-in-work","tag-knowledge-management","tag-from-answering-questions-to-participating-in-work-zh",""],"translatepress_titles":{"en_AU":"What Can AI Remember\u2014and What Must a Work System Preserve?","zh_CN":"AI\u4f1a\u8bb0\u4f4f\u4ec0\u4e48\uff0c\u5de5\u4f5c\u7cfb\u7edf\u53c8\u5fc5\u987b\u4fdd\u5b58\u4ec0\u4e48\uff1f"},"jetpack_publicize_connections":[],"jetpack_likes_enabled":false,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p1ar9H-4m4","jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/geoffreychen.com\/zh\/wp-json\/wp\/v2\/posts\/16744","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/geoffreychen.com\/zh\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/geoffreychen.com\/zh\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/geoffreychen.com\/zh\/wp-json\/wp\/v2\/users\/17897162"}],"replies":[{"embeddable":true,"href":"https:\/\/geoffreychen.com\/zh\/wp-json\/wp\/v2\/comments?post=16744"}],"version-history":[{"count":2,"href":"https:\/\/geoffreychen.com\/zh\/wp-json\/wp\/v2\/posts\/16744\/revisions"}],"predecessor-version":[{"id":16769,"href":"https:\/\/geoffreychen.com\/zh\/wp-json\/wp\/v2\/posts\/16744\/revisions\/16769"}],"wp:attachment":[{"href":"https:\/\/geoffreychen.com\/zh\/wp-json\/wp\/v2\/media?parent=16744"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/geoffreychen.com\/zh\/wp-json\/wp\/v2\/categories?post=16744"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/geoffreychen.com\/zh\/wp-json\/wp\/v2\/tags?post=16744"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}