{"id":30020,"date":"2026-08-12T11:51:57","date_gmt":"2026-08-12T03:51:57","guid":{"rendered":"https:\/\/aif.amtbbs.org\/?p=30020"},"modified":"2026-08-12T11:51:57","modified_gmt":"2026-08-12T03:51:57","slug":"%e6%96%87%e7%94%9f%e5%9b%bescaling%e6%96%b0%e5%8f%98%e9%87%8f%ef%bc%8c%e8%a2%ab%e5%ad%97%e8%8a%82seed%e5%9b%a2%e9%98%9f%e5%8f%91%e7%8e%b0%e4%ba%86","status":"publish","type":"post","link":"https:\/\/aif.amtbbs.org\/index.php\/2026\/08\/12\/30020\/","title":{"rendered":"\u6587\u751f\u56feScaling\u65b0\u53d8\u91cf\uff0c\u88ab\u5b57\u8282Seed\u56e2\u961f\u53d1\u73b0\u4e86"},"content":{"rendered":"
\u56e2\u961f\u63d0\u51fa Structured Prompt\uff0c\u5e76\u4ece Diffusability \u4e0e Promptability \u4e24\u4fa7\u5171\u540c\u63d0\u5347\u6587\u672c\u6761\u4ef6\uff0c\u6700\u7ec8\u5728\u590d\u6742\u7ec4\u5408\u3001\u63a8\u7406\u548c\u4e16\u754c\u77e5\u8bc6\u751f\u6210\u4efb\u52a1\u4e0a\u53d6\u5f97\u663e\u8457\u63d0\u5347\u3002<\/div>\n
\n
\n

\u6587\u751f\u56fe\u6a21\u578b\u4e00\u76f4\u5728\u6269\u5c55\u6a21\u578b\u3001\u6570\u636e\u548c\u7b97\u529b\uff0c\u4f46\u8bad\u7ec3\u56fe\u50cf\u6240\u914d Caption \u7684\u4fe1\u606f\u91cf\uff0c\u5374\u5f88\u5c11\u88ab\u4f5c\u4e3a\u4e00\u4e2a\u72ec\u7acb\u53d8\u91cf\u7cfb\u7edf\u7814\u7a76\u3002ByteDance Seed \u56e2\u961f\u53d1\u73b0\uff1a\u81ea\u7136\u8bed\u8a00 Caption \u53d8\u957f\uff0c\u5e76\u4e0d\u610f\u5473\u7740\u6a21\u578b\u83b7\u5f97\u4e86\u66f4\u591a\u53ef\u7528\u7684\u89c6\u89c9\u76d1\u7763\uff1b\u76f8\u6bd4\u957f\u5ea6\uff0cCaption \u4e2d\u4e0e\u56fe\u50cf\u7ed1\u5b9a\u7684\u4fe1\u606f\u91cf\u66f4\u80fd\u9884\u6d4b\u6269\u6563\u6a21\u578b\u6700\u7ec8\u8fbe\u5230\u7684\u8bad\u7ec3\u635f\u5931\u3002<\/p>\n

\u57fa\u4e8e\u8fd9\u4e00\u53d1\u73b0\uff0c\u56e2\u961f\u63d0\u51fa Structured Prompt\uff0c\u5e76\u4ece Diffusability \u4e0e Promptability \u4e24\u4fa7\u5171\u540c\u63d0\u5347\u6587\u672c\u6761\u4ef6\uff0c\u6700\u7ec8\u5728\u590d\u6742\u7ec4\u5408\u3001\u63a8\u7406\u548c\u4e16\u754c\u77e5\u8bc6\u751f\u6210\u4efb\u52a1\u4e0a\u53d6\u5f97\u663e\u8457\u63d0\u5347\u3002<\/p>\n

\"\u56fe\u7247\"<\/p>\n

\u8bba\u6587 Figure 1\uff5c\u81ea\u7136\u8bed\u8a00\u957f\u5ea6\u5f88\u5feb\u9971\u548c\uff1b\u7ed3\u6784\u5316\u6761\u4ef6\u6301\u7eed\u589e\u52a0\u56fe\u50cf\u4fe1\u606f\uff0c\u5e76\u6cbf\u7edf\u4e00\u5173\u7cfb\u964d\u4f4e\u6269\u6563\u8bad\u7ec3\u635f\u5931<\/p>\n

\u8fc7\u53bb\u51e0\u5e74\uff0c\u6587\u751f\u56fe\u6a21\u578b\u7684\u8fdb\u6b65\u51e0\u4e4e\u6cbf\u7740\u4e00\u6761\u719f\u6089\u7684\u8def\u7ebf\u5c55\u5f00\uff1a\u66f4\u5927\u7684\u6a21\u578b\u3001\u66f4\u591a\u7684\u6570\u636e\uff0c\u4ee5\u53ca\u66f4\u9ad8\u7684\u8bad\u7ec3\u7b97\u529b\u3002<\/p>\n

\u4f46\u6587\u751f\u56fe\u4e0e\u8bed\u8a00\u6a21\u578b\u4e4b\u95f4\u5b58\u5728\u4e00\u4e2a\u5bb9\u6613\u88ab\u5ffd\u7565\u7684\u5dee\u5f02\u3002\u8bed\u8a00\u6a21\u578b\u53ef\u4ee5\u76f4\u63a5\u4ece\u6587\u672c\u5e8f\u5217\u4e2d\u8fdb\u884c\u81ea\u76d1\u7763\u5b66\u4e60\uff1b\u6587\u751f\u56fe\u6a21\u578b\u5219\u4f9d\u8d56\u56fe\u50cf\u4e0e Caption \u7684\u914d\u5bf9\uff0c\u5b66\u4e60 \u201c\u4ec0\u4e48\u6837\u7684\u6587\u5b57\u5bf9\u5e94\u4ec0\u4e48\u6837\u7684\u89c6\u89c9\u5185\u5bb9\u201d\u3002\u56fe\u50cf\u4e2d\u53ef\u80fd\u5305\u542b\u5927\u91cf\u7269\u4f53\u3001\u5c5e\u6027\u3001\u4f4d\u7f6e\u3001\u52a8\u4f5c\u548c\u5173\u7cfb\uff0c\u4f46\u53ea\u6709\u88ab Caption \u51c6\u786e\u63cf\u8ff0\u5e76\u6e05\u6670\u7ed1\u5b9a\u7684\u90e8\u5206\uff0c\u624d\u80fd\u4f5c\u4e3a\u6587\u672c\u6761\u4ef6\u76d1\u7763\u4f20\u9012\u7ed9\u6a21\u578b\u3002<\/p>\n

\u4e8e\u662f\uff0c\u4e00\u4e2a\u57fa\u7840\u95ee\u9898\u51fa\u73b0\u4e86\uff1a\u9664\u4e86\u7ee7\u7eed\u6269\u5927\u6a21\u578b\u3001\u6570\u636e\u548c\u7b97\u529b\uff0c\u6211\u4eec\u80fd\u5426\u901a\u8fc7\u589e\u52a0 Caption \u6240\u643a\u5e26\u7684\u56fe\u50cf\u4fe1\u606f\uff0c\u8ba9\u751f\u6210\u6a21\u578b\u5b66\u5f97\u66f4\u597d\uff1f<\/p>\n

\u5728\u8fd9\u9879\u65b0\u5de5\u4f5c\u4e2d\uff0cByteDance Seed \u56e2\u961f\u7814\u7a76\u4e86\u8fd9\u4e00\u95ee\u9898\u3002\u6838\u5fc3\u7ed3\u8bba\u53ef\u4ee5\u6982\u62ec\u4e3a\u4e00\u53e5\u8bdd\uff1a<\/p>\n

\u771f\u6b63\u968f\u6587\u672c\u6761\u4ef6\u6269\u5c55\u7684\uff0c\u4e0d\u662f Caption \u7684 token \u6570\u91cf\uff0c\u800c\u662f\u5176\u4e2d\u53ef\u88ab\u6a21\u578b\u5229\u7528\u7684\u56fe\u50cf\u4fe1\u606f\u3002<\/p>\n

<\/p>\n

    \n
  • \u8bba\u6587\u6807\u9898\uff1a Scaling Properties of Text Conditioning in Visual Generation<\/li>\n
  • \u4f5c\u8005\uff1a Zilong Chen, Chaorui Deng, Kunchang Li, Hongyi Yuan, Haoqi Fan \u673a\u6784\uff1a ByteDance Seed<\/li>\n
  • \u8bba\u6587\uff1ahttps:\/\/arxiv.org\/abs\/2607.29679<\/li>\n
  • \u9879\u76ee\u4e3b\u9875\uff1ahttps:\/\/heheyas.github.io\/context-scaling<\/li>\n
  • \u4ee3\u7801\uff1ahttps:\/\/github.com\/heheyas\/context-scaling<\/li>\n
  • \u6a21\u578b\uff1ahttps:\/\/huggingface.co\/collections\/heheyas\/context-scaling<\/li>\n
  • \u5728\u7ebf Demo\uff1ahttps:\/\/heheyas-context-scaling.hf.space\/<\/li>\n
  • Hugging Face Paper\uff1ahttps:\/\/huggingface.co\/papers\/2607.29679<\/li>\n<\/ul>\n

    Prompt \u53d8\u957f\u4e86\uff0c\u4e3a\u4ec0\u4e48\u6a21\u578b\u6ca1\u6709\u53d8\u5f3a\uff1f<\/h4>\n

    \u4e00\u4e2a\u76f4\u89c9\u505a\u6cd5\uff0c\u662f\u628a\u8bad\u7ec3 Caption \u6216\u7528\u6237 Prompt \u5199\u5f97\u66f4\u957f\u3001\u66f4\u8be6\u7ec6\u3002\u66f4\u591a token \u770b\u8d77\u6765\u5e94\u8be5\u610f\u5473\u7740\u66f4\u591a\u76d1\u7763\uff0c\u4e5f\u5e94\u8be5\u5e2e\u52a9\u6a21\u578b\u751f\u6210\u66f4\u590d\u6742\u7684\u56fe\u50cf\u3002<\/p>\n

    \u5b9e\u9a8c\u5374\u7ed9\u51fa\u4e86\u4e0d\u540c\u7b54\u6848\u3002\u5728\u591a\u79cd\u73b0\u6709\u5f00\u6e90\u6587\u751f\u56fe\u7cfb\u7edf\u4e0a\uff0c\u81ea\u7136\u8bed\u8a00 Prompt \u968f\u957f\u5ea6\u589e\u52a0\u5f88\u5feb\u9971\u548c\uff0c\u6700\u7ec8\u8f93\u51fa\u751a\u81f3\u4f4e\u4e8e\u5404\u81ea\u6700\u77ed Prompt \u7684\u8868\u73b0\u3002\u5373\u4f7f\u4e13\u95e8\u5728\u540c\u4e00\u5957\u957f\u6587\u672c Caption \u4e0a\u8bad\u7ec3\u6269\u6563\u6a21\u578b\uff0c\u6536\u76ca\u4e5f\u5341\u5206\u6709\u9650\u3002<\/p>\n

    \u4e3a\u4e86\u628a\u8fd9\u4e2a\u73b0\u8c61\u770b\u5f97\u66f4\u6e05\u695a\uff0c\u56e2\u961f\u8bbe\u8ba1\u4e86\u4e00\u4e2a\u56fa\u5b9a\u9aa8\u5e72\u7f51\u7edc\u7684\u56fe\u50cf\u91cd\u5efa\u5b9e\u9a8c\u3002\u5bf9\u4e8e\u540c\u4e00\u5f20\u53c2\u8003\u56fe\u50cf\uff0c\u56e2\u961f\u4ece\u540c\u4e00\u4efd\u5b8c\u6574\u6807\u6ce8\u51fa\u53d1\uff0c\u751f\u6210\u56db\u4e2a\u8be6\u7ec6\u7a0b\u5ea6\u9010\u6e10\u589e\u52a0\u7684\u81ea\u7136\u8bed\u8a00 Caption\uff0c\u518d\u4f7f\u7528\u76f8\u540c\u7684 Qwen-Image \u6a21\u578b\u548c\u968f\u673a\u79cd\u5b50\u5c1d\u8bd5\u8fd8\u539f\u8be5\u56fe\u50cf\u3002\u8fd9\u4e9b Caption \u63cf\u8ff0\u76f8\u540c\u7684\u5b9e\u4f53\u548c\u5173\u7cfb\uff0c\u540e\u7eed\u7248\u672c\u4e3b\u8981\u901a\u8fc7\u8865\u5145\u548c\u5c55\u5f00\u81ea\u7136\u8bed\u8a00\u8868\u8fbe\u6765\u589e\u52a0\u957f\u5ea6\u3002<\/p>\n

    \u4ee4\u4eba\u610f\u5916\u7684\u662f\uff0cCaption \u867d\u7136\u663e\u8457\u53d8\u957f\uff0c\u56fe\u50cf\u91cd\u5efa\u8d28\u91cf\u5374\u51e0\u4e4e\u4e0d\u518d\u63d0\u9ad8\u3002\u65b0\u589e\u7684 prose \u66f4\u591a\u662f\u5728\u89e3\u91ca\u3001\u6539\u5199\u6216\u8fde\u63a5\u5df2\u7ecf\u51fa\u73b0\u7684\u5185\u5bb9\uff0c\u5e76\u6ca1\u6709\u6301\u7eed\u589e\u52a0\u65b0\u7684\u3001\u53ef\u7a33\u5b9a\u4f7f\u7528\u7684\u89c6\u89c9\u53d8\u91cf\u3002<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    \u8bba\u6587 Figure 3\uff5c\u56fa\u5b9a\u9aa8\u5e72\u91cd\u5efa\u5b9e\u9a8c\uff1aNL Caption \u7ee7\u7eed\u53d8\u957f\u540e\u91cd\u5efa\u8d8b\u4e8e\u9971\u548c\uff0c\u9010\u6b65\u6062\u590d SP \u5b57\u6bb5\u5219\u6301\u7eed\u6539\u5584<\/p>\n

    \u8fd9\u8bf4\u660e\uff0cCaption \u957f\u5ea6\u53ea\u662f\u4e00\u4e2a\u5f88\u5f31\u7684\u4ee3\u7406\u53d8\u91cf\u3002\u4e00\u4e2a\u6587\u672c\u53ef\u4ee5\u5199\u5f97\u5f88\u957f\uff0c\u5374\u4ecd\u7136\u6ca1\u6709\u6e05\u695a\u8bf4\u660e\uff1a\u67d0\u4e2a\u5c5e\u6027\u5c5e\u4e8e\u54ea\u4e2a\u7269\u4f53\u3001\u4e24\u4e2a\u7269\u4f53\u662f\u4ec0\u4e48\u5173\u7cfb\u3001\u5404\u81ea\u4f4d\u4e8e\u54ea\u91cc\uff0c\u4ee5\u53ca\u5b83\u4eec\u5728\u753b\u9762\u4e2d\u7684\u524d\u540e\u5c42\u6b21\u3002<\/p>\n

    \u5982\u4f55\u8861\u91cf Caption \u91cc\u771f\u6b63\u7684\u56fe\u50cf\u4fe1\u606f\uff1f<\/h4>\n

    \u5982\u679c token \u6570\u91cf\u4e0d\u8db3\u4ee5\u8861\u91cf\u76d1\u7763\u5f3a\u5f31\uff0c\u5c31\u9700\u8981\u76f4\u63a5\u6d4b\u91cf Caption \u4e2d\u4e0e\u56fe\u50cf\u7ed1\u5b9a\u7684\u4fe1\u606f\u3002\u4e3a\u6b64\uff0c\u56e2\u961f\u4ece\u5df2\u6709\u5de5\u4f5c\u4e2d\u6539\u9020\u4e86\u4e24\u4e2a\u4e92\u8865\u6307\u6807\uff1aGrounded Perplexity Gain\uff08GPG\uff09\u548c Effective Detailness\uff08ED\uff09\u3002<\/p>\n

    GPG\uff1a\u56fe\u50cf\u8ba9 Caption \u53d8\u5f97 \u201c\u66f4\u53ef\u9884\u6d4b\u201d \u591a\u5c11\u3002<\/p>\n

    GPG \u662f\u4e00\u4e2a\u9700\u8981\u8bfb\u53d6\u6a21\u578b token \u6982\u7387\u7684\u767d\u76d2\u6307\u6807\u3002\u5bf9\u4e8e\u540c\u4e00\u53e5 Caption\uff0c\u56e2\u961f\u5206\u522b\u8ba9\u4e00\u4e2a\u51bb\u7ed3\u7684\u89c6\u89c9\u8bed\u8a00\u6a21\u578b\u770b\u5230\u548c\u770b\u4e0d\u5230\u914d\u5bf9\u56fe\u50cf\uff0c\u5e76\u8ba1\u7b97\u56fe\u50cf\u51fa\u73b0\u540e Caption \u5185\u5bb9 token \u7684\u5bf9\u6570\u4f3c\u7136\u63d0\u5347\u3002\u82e5 Caption \u4e2d\u5305\u542b\u5927\u91cf\u4e0e\u8be5\u56fe\u50cf\u7d27\u5bc6\u7ed1\u5b9a\u7684\u4fe1\u606f\uff0c\u770b\u5230\u56fe\u50cf\u5e94\u5f53\u663e\u8457\u63d0\u9ad8\u6a21\u578b\u5bf9\u8fd9\u4e9b token \u7684\u9884\u6d4b\u80fd\u529b\u3002<\/p>\n

    ED\uff1aCaption \u8986\u76d6\u4e86\u591a\u5c11\u53ef\u9760\u7684\u56fe\u50cf\u5c5e\u6027\u3002<\/p>\n

    ED \u662f\u4e00\u4e2a\u4e0d\u4f9d\u8d56 token \u6982\u7387\u7684\u9ed1\u76d2\u8bed\u4e49\u6307\u6807\u3002\u5b83\u5206\u522b\u4ece\u56fe\u50cf\u548c Caption \u4e2d\u63d0\u53d6\u5e26\u6709\u5b9e\u4f53\u4e0a\u4e0b\u6587\u7684\u5c5e\u6027\uff0c\u518d\u8ba1\u7b97 Caption \u5c5e\u6027\u7684\u51c6\u786e\u7387\u4e0e\u56fe\u50cf\u5c5e\u6027\u7684\u53ec\u56de\u7387\u3002\u6700\u7ec8\u91c7\u7528\u66f4\u91cd\u89c6\u51c6\u786e\u7387\u7684 F0.5\uff0c\u5bf9 Caption \u4e2d\u6ca1\u6709\u56fe\u50cf\u4f9d\u636e\u7684\u63cf\u8ff0\u65bd\u52a0\u66f4\u5f3a\u60e9\u7f5a\u3002<\/p>\n

    \u4e24\u4e2a\u6307\u6807\u4ece\u4e0d\u540c\u89d2\u5ea6\u523b\u753b\u540c\u4e00\u4e2a\u95ee\u9898\uff1aGPG \u5173\u6ce8\u56fe\u50cf\u4e0e\u6587\u672c\u4e4b\u95f4\u7684\u7edf\u8ba1\u4f9d\u8d56\uff0cED \u5173\u6ce8 Caption \u662f\u5426\u51c6\u786e\u8986\u76d6\u4e86\u53ef\u9a8c\u8bc1\u7684\u89c6\u89c9\u5185\u5bb9\u3002<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    \u8bba\u6587 Figure 6\uff5cGPG \u4e0e ED \u7684\u5b9a\u4e49\u53ca\u6d4b\u91cf\u8d8b\u52bf<\/p>\n

    Caption \u4fe1\u606f\u91cf\u53ef\u4ee5\u9884\u6d4b\u6269\u6563\u6a21\u578b\u7684\u8bad\u7ec3\u635f\u5931<\/h4>\n

    \u63a5\u4e0b\u6765\uff0c\u56e2\u961f\u56fa\u5b9a\u56fe\u50cf\u3001\u6a21\u578b\u67b6\u6784\u3001\u521d\u59cb\u5316\u65b9\u5f0f\u3001\u4f18\u5316\u914d\u7f6e\u548c\u8bad\u7ec3\u9884\u7b97\uff0c\u53ea\u6539\u53d8\u8bad\u7ec3 Caption\u3002\u6574\u4e2a\u5b9e\u9a8c\u5305\u542b 15 \u79cd Caption \u914d\u7f6e\uff1a\u4e09\u4e2a\u4e0d\u540c\u957f\u5ea6\u7684\u81ea\u7136\u8bed\u8a00\u7248\u672c\u3001\u516d\u4e2a\u9010\u6b65\u6062\u590d\u5b57\u6bb5\u7684 Structured Prompt \u7248\u672c\uff0c\u4ee5\u53ca\u516d\u4e2a\u7a7a\u95f4\u8868\u8fbe\u6216\u5b57\u6bb5\u906e\u853d\u53d8\u4f53\u3002\u6bcf\u79cd\u914d\u7f6e\u90fd\u4ece\u540c\u4e00\u4e2a BAGEL continued-training checkpoint \u51fa\u53d1\uff0c\u72ec\u7acb\u8bad\u7ec3\u4e00\u4e2a\u6269\u6563\u6a21\u578b\u3002<\/p>\n

    \u7ed3\u679c\u663e\u793a\uff0c\u81ea\u7136\u8bed\u8a00 Caption \u7684 token \u6570\u4e0e\u8bad\u7ec3\u7ed3\u679c\u5e76\u4e0d\u5b58\u5728\u7edf\u4e00\u5173\u7cfb\uff1b\u4f46\u5f53\u6a2a\u8f74\u6362\u6210 Caption \u4fe1\u606f\u91cf\u540e\uff0c\u4e0d\u540c\u683c\u5f0f\u548c\u8be6\u7ec6\u7a0b\u5ea6\u7684\u914d\u7f6e\u843d\u5728\u4e86\u9ad8\u5ea6\u89c4\u5f8b\u7684\u66f2\u7ebf\u4e0a\uff1a<\/p>\n

      \n
    • \u6536\u655b\u540e\u7684 diffusion loss \u4e0e GPG \u8fd1\u4f3c\u5448\u7ebf\u6027\u5173\u7cfb\uff0cPearson r = -0.984\uff1b<\/li>\n
    • \u6536\u655b\u540e\u7684 diffusion loss \u4e0e ED \u5448\u5e42\u5f8b\u8d8b\u52bf\uff0clog-log \u7a7a\u95f4\u4e2d\u7684 Pearson r = -0.971\uff1b<\/li>\n
    • GPG \u4e0e ED \u5bf9\u4e0d\u540c Caption \u914d\u7f6e\u7684\u6392\u5e8f\u4e5f\u9ad8\u5ea6\u4e00\u81f4\uff0cSpearman \u03c1 = 0.96\u3002<\/li>\n<\/ul>\n

      \u56e2\u961f\u5c06\u5176\u79f0\u4e3a text conditioning \u7684 scaling properties\u3002\u5b83\u4e0d\u662f\u4e00\u4e2a\u5bf9\u6240\u6709\u6a21\u578b\u90fd\u6210\u7acb\u7684\u7406\u8bba\u5b9a\u5f8b\uff0c\u800c\u662f\u5728\u56fa\u5b9a\u67b6\u6784\u4e0e\u8bad\u7ec3 recipe \u4e0b\u5f97\u5230\u7684\u7ecf\u9a8c\u6807\u5b9a\u3002\u4f46\u5b83\u5e26\u6765\u4e86\u4e24\u4e2a\u76f4\u63a5\u4ef7\u503c\u3002<\/p>\n

      \u7b2c\u4e00\uff0c\u5b83\u628a Caption \u4fe1\u606f\u91cf\u4ece\u4e00\u4e2a\u6a21\u7cca\u7684 \u201c\u6570\u636e\u8d28\u91cf\u201d \u6982\u5ff5\uff0c\u53d8\u6210\u4e86\u53ef\u4ee5\u63a7\u5236\u548c\u6d4b\u91cf\u7684\u8bad\u7ec3\u53d8\u91cf\uff1a\u5728\u6a21\u578b\u3001\u56fe\u50cf\u548c\u7b97\u529b\u4e0d\u53d8\u65f6\uff0c\u4fe1\u606f\u91cf\u80fd\u591f\u9884\u6d4b\u6a21\u578b\u6700\u7ec8\u8fbe\u5230\u7684\u8bad\u7ec3\u635f\u5931\u3002<\/p>\n

      \u7b2c\u4e8c\uff0c\u5b8c\u6210\u4e00\u6b21\u6807\u5b9a\u540e\uff0c\u53ef\u4ee5\u5728\u540c\u4e00\u8bad\u7ec3 recipe \u4e0b\u5148\u7528 GPG \u6216 ED \u6bd4\u8f83\u5019\u9009 Caption \u65b9\u6848\uff0c\u518d\u51b3\u5b9a\u662f\u5426\u6295\u5165\u6602\u8d35\u7684\u6269\u6563\u6a21\u578b\u8bad\u7ec3\u3002\u5bf9\u672a\u53c2\u4e0e\u62df\u5408\u7684\u516d\u4e2a Caption \u53d8\u4f53\uff0c\u4e24\u4e2a\u6307\u6807\u4ecd\u80fd\u8f83\u51c6\u786e\u5730\u9884\u6d4b\u5176\u6536\u655b\u635f\u5931\u3002<\/p>\n

      \"\u56fe\u7247\"<\/p>\n

      \u8bba\u6587 Figure 7\uff5c\u5728\u56fa\u5b9a\u8bad\u7ec3 recipe \u4e0b\uff0c\u6536\u655b\u635f\u5931\u968f Caption \u4fe1\u606f\u91cf\u5448\u73b0\u7a33\u5b9a\u5173\u7cfb<\/p>\n

      Structured Prompt\uff1a\u8ba9\u4fe1\u606f\u4e0d\u4ec5\u66f4\u591a\uff0c\u800c\u4e14\u66f4\u5bb9\u6613\u88ab\u6a21\u578b\u4f7f\u7528<\/h4>\n

      \u524d\u9762\u7684\u5b9e\u9a8c\u63ed\u793a\u4e86\u4e00\u4e2a\u5173\u952e\u70b9\uff1a\u4ec5\u4ec5\u589e\u52a0\u81ea\u7136\u8bed\u8a00 prose \u5e76\u4e0d\u591f\uff0c\u65b0\u589e\u4fe1\u606f\u8fd8\u9700\u8981\u4ee5\u7a33\u5b9a\u3001\u660e\u786e\u7684\u65b9\u5f0f\u7ec4\u7ec7\u8d77\u6765\u3002<\/p>\n

      \u56e0\u6b64\uff0c\u56e2\u961f\u63d0\u51fa Structured Prompt\uff08SP\uff09\uff0c\u7528\u7ed3\u6784\u5316 JSON \u8868\u793a\u4e00\u5f20\u56fe\u50cf\u4e2d\u7684\u89c6\u89c9\u53d8\u91cf\u3002\u5b83\u5305\u542b\u4e09\u4e2a\u5c42\u6b21\uff1a<\/p>\n

        \n
      • \u5168\u5c40\u5c42\uff1a\u753b\u9762\u610f\u56fe\u3001\u573a\u666f\u3001\u6c1b\u56f4\u3001\u98ce\u683c\u3001\u5149\u7167\u548c\u6444\u5f71\u4fe1\u606f\uff1b<\/li>\n
      • \u5143\u7d20\u5c42\uff1a\u6bcf\u4e2a\u4e3b\u4f53\u7684\u8eab\u4efd\u3001\u5c5e\u6027\u3001\u52a8\u4f5c\u3001\u4f4d\u7f6e\u3001\u53ef\u9009\u6df1\u5ea6\u548c\u5c40\u90e8\u6444\u5f71\u4fe1\u606f\uff1b<\/li>\n
      • \u5173\u7cfb\u5c42\uff1a\u4e0d\u540c\u5143\u7d20\u4e4b\u95f4\u7684\u4f4d\u7f6e\u3001\u906e\u6321\u3001\u4ea4\u4e92\u548c\u8bed\u4e49\u5173\u7cfb\u3002<\/li>\n<\/ul>\n

        \u4e0e\u81ea\u7531\u6587\u672c\u76f8\u6bd4\uff0cSP \u7684\u5173\u952e\u4e0d\u53ea\u662f \u201cJSON\u201d \u8fd9\u4e00\u5916\u89c2\uff0c\u800c\u662f\u8ba9\u4e0d\u540c\u89c6\u89c9\u53d8\u91cf\u8fdb\u5165\u7a33\u5b9a\u7684\u547d\u540d\u5b57\u6bb5\uff0c\u51cf\u5c11\u5c5e\u6027\u5f52\u5c5e\u3001\u7a7a\u95f4\u5173\u7cfb\u548c\u5bf9\u8c61\u7ed1\u5b9a\u4e0a\u7684\u6b67\u4e49\u3002\u5728\u56fa\u5b9a\u9aa8\u5e72\u91cd\u5efa\u5b9e\u9a8c\u4e2d\uff0c\u968f\u7740 SP \u5b57\u6bb5\u9010\u6b65\u6062\u590d\uff0c\u91cd\u5efa\u8d28\u91cf\u6301\u7eed\u63d0\u9ad8\uff1b\u5728\u5b8c\u6574\u8bad\u7ec3 sweep \u4e2d\uff0c\u5b57\u6bb5\u8986\u76d6\u589e\u52a0\u4e5f\u540c\u6b65\u63d0\u9ad8 GPG\u3001ED\uff0c\u5e76\u964d\u4f4e\u6536\u655b\u540e\u7684 diffusion loss\u3002<\/p>\n

        \"\u56fe\u7247\"<\/p>\n

        \u8bba\u6587 Figure 5\uff5cStructured Prompt \u5c06\u5168\u5c40\u3001\u5143\u7d20\u7ea7\u548c\u8de8\u5143\u7d20\u89c6\u89c9\u53d8\u91cf\u7ec4\u7ec7\u5230\u547d\u540d\u5b57\u6bb5\u4e2d<\/p>\n

        \u4e3a\u4e86\u5728\u5927\u89c4\u6a21\u8bad\u7ec3\u6570\u636e\u4e0a\u751f\u6210\u5b8c\u6574 SP\uff0c\u56e2\u961f\u6784\u5efa\u4e86\u4e00\u5957 image-to-SP \u6807\u6ce8\u6d41\u6c34\u7ebf\u3002\u901a\u7528 VLM \u8d1f\u8d23\u5168\u5c40\u8bed\u4e49\u548c\u5c40\u90e8\u5185\u5bb9\uff0cSapiens \u8865\u5145\u4eba\u4f53\u59ff\u6001\u8bc1\u636e\uff0cDepthAnything V2 \u63d0\u4f9b\u76f8\u5bf9\u6df1\u5ea6\uff0cSAM 2.1 \u63d0\u4f9b\u63a9\u7801\u4e0e\u906e\u6321\u7ebf\u7d22\uff0c\u6700\u540e\u518d\u7531 VLM \u5c06\u8fd9\u4e9b\u4fe1\u606f\u7edf\u4e00\u6574\u7406\u4e3a\u4e00\u81f4\u7684\u5b8c\u6574 SP\u3002<\/p>\n

        <\/p>\n

        \u8bba\u6587 Figure 8\uff5cVLM \u4e0e\u59ff\u6001\u3001\u6df1\u5ea6\u3001\u5206\u5272\u4e13\u5bb6\u5171\u540c\u6784\u5efa\u5b8c\u6574 Structured Prompt<\/p>\n

        \u56e2\u961f\u5c06\u4e00\u4e2a Caption \u8868\u793a\u80fd\u591f\u5411\u6269\u6563\u6a21\u578b\u66b4\u9732\u5e76\u7ec4\u7ec7\u56fe\u50cf\u76d1\u7763\u7684\u80fd\u529b\u79f0\u4e3a Diffusability\u3002SP \u63d0\u5347\u7684\u6b63\u662f\u8fd9\u4e00\u4fa7\uff1a\u5728\u4e0d\u6539\u53d8\u6269\u6563\u6a21\u578b\u67b6\u6784\u7684\u60c5\u51b5\u4e0b\uff0c\u8ba9\u6a21\u578b\u4ece\u6587\u672c\u6761\u4ef6\u4e2d\u5b66\u5230\u66f4\u591a\u3001\u66f4\u660e\u786e\u7684\u89c6\u89c9\u53d8\u91cf\u3002<\/p>\n

        Promptability\uff1a\u6709\u4e86\u597d\u7684\u7ed3\u6784\uff0c\u8fd8\u9700\u8981 LLM \u628a\u5b83\u586b\u597d<\/h4>\n

        \u8bad\u7ec3\u65f6\u53ef\u4ee5\u4ece\u914d\u5bf9\u56fe\u50cf\u4e2d\u63d0\u53d6\u5b8c\u6574 SP\uff0c\u4f46\u5b9e\u9645\u751f\u6210\u65f6\u53ea\u6709\u7528\u6237\u7684\u4e00\u53e5\u8bdd\uff0c\u6ca1\u6709\u53c2\u8003\u56fe\u50cf\u6216 oracle \u6807\u6ce8\u3002\u7cfb\u7edf\u8fd8\u9700\u8981\u4e00\u4e2a LLM prompter\uff0c\u628a\u7528\u6237\u8bf7\u6c42\u6269\u5c55\u6210\u8be6\u7ec6\u3001\u8fde\u8d2f\u3001\u4e14\u4e0d\u8fdd\u53cd\u539f\u59cb\u7ea6\u675f\u7684 SP\u3002<\/p>\n

        \u56e2\u961f\u628a\u8fd9\u79cd\u4ece\u7528\u6237\u8bf7\u6c42\u5b9e\u4f8b\u5316\u7ed3\u6784\u5316\u6761\u4ef6\u7684\u80fd\u529b\u79f0\u4e3a Promptability\u3002\u7aef\u5230\u7aef\u751f\u6210\u8d28\u91cf\u53d6\u51b3\u4e8e\u4e24\u4fa7\u5171\u540c\u4f5c\u7528\uff1a<\/p>\n

        Generation Quality = Diffusability \u00d7 Promptability<\/strong><\/p>\n

        \u8fd9\u91cc\u7684\u4e58\u53f7\u662f\u4e00\u79cd\u7ec4\u7ec7\u89c6\u89d2\uff0c\u800c\u4e0d\u662f\u62df\u5408\u5f97\u5230\u7684\u6570\u5b66\u4e58\u6cd5\u516c\u5f0f\uff1aDiffusability \u63cf\u8ff0\u6269\u6563\u6a21\u578b\u80fd\u591f\u4ece Caption \u8868\u793a\u4e2d\u5b66\u5230\u4ec0\u4e48\uff0cPromptability \u63cf\u8ff0 LLM \u5728\u63a8\u7406\u65f6\u80fd\u5426\u771f\u6b63\u586b\u51fa\u9ad8\u8d28\u91cf\u7684 Caption \u5b9e\u4f8b\u3002<\/p>\n

        \"\u56fe\u7247\"<\/p>\n

        \u8bba\u6587 Figure 4\uff5cStructured Prompt \u8fde\u63a5\u6807\u6ce8\u3001\u6d4b\u91cf\u3001diffuser \u8bad\u7ec3\u3001prompter \u8bad\u7ec3\u4e0e\u6700\u7ec8\u751f\u6210<\/p>\n

        \u9996\u5148\uff0c\u56e2\u961f\u56fa\u5b9a SP schema \u548c Qwen-Image diffuser\uff0c\u53ea\u66ff\u6362\u96f6\u6837\u672c LLM prompter\u3002\u968f\u7740 Qwen3.5 \u4ece 0.8B \u6269\u5c55\u5230 397B\uff0cthinking \u6a21\u5f0f\u4e0b\u7684 GenEval++ \u4ece 46.4% \u63d0\u5347\u5230 86.8%\u3002\u9664\u6700\u5c0f\u6a21\u578b\u5bb9\u6613\u5728\u601d\u8003\u8fc7\u7a0b\u4e2d\u91cd\u590d\u5e76\u65e0\u6cd5\u8f93\u51fa\u6709\u6548 JSON \u5916\uff0cchain-of-thought \u5728\u5176\u4ed6\u5c3a\u5ea6\u4e0a\u90fd\u5e26\u6765\u8fdb\u4e00\u6b65\u63d0\u5347\u3002\u8fd9\u8bf4\u660e\uff0c\u901a\u7528 LLM \u7684\u6a21\u578b\u80fd\u529b\u548c\u63a8\u7406\u80fd\u529b\uff0c\u53ef\u4ee5\u901a\u8fc7 Caption \u63a5\u53e3\u76f4\u63a5\u8f6c\u5316\u4e3a\u66f4\u597d\u7684\u56fe\u50cf\u751f\u6210\u7ed3\u679c\u3002<\/p>\n

        \u4f46\u96f6\u6837\u672c LLM \u4ecd\u7136\u503e\u5411\u4e8e\u751f\u6210\u4fe1\u606f\u4e0d\u8db3\u3001\u6784\u56fe\u8f83\u7b80\u5355\u7684 SP\u3002\u4e3a\u8fdb\u4e00\u6b65\u63d0\u9ad8 Promptability\uff0c\u56e2\u961f\u91c7\u7528\u4e09\u9636\u6bb5\u8bad\u7ec3\uff1a<\/p>\n

          \n
        1. SFT \u5b66\u4e60\u6269\u6563\u6a21\u578b\u6240\u671f\u671b\u7684 SP \u5185\u5bb9\u5206\u5e03\uff0c\u800c\u4e0d\u53ea\u662f JSON \u683c\u5f0f\uff1b<\/li>\n
        2. Cold-start \u4ece\u5e26\u6709\u914d\u5bf9\u56fe\u50cf\u7684 privileged reasoning traces \u4e2d\u84b8\u998f \u201c\u5982\u4f55\u4ec5\u6839\u636e\u7528\u6237\u8bf7\u6c42\u63a8\u5bfc SP\u201d\uff1b<\/li>\n
        3. RFT \u5728 prompter \u81ea\u5df1\u751f\u6210\u5e76\u6e32\u67d3\u7684 rollout \u4e0a\u7ee7\u7eed\u4f18\u5316\uff0c\u7531 verifier \u7b5b\u9009\u9ad8\u7f6e\u4fe1\u8f68\u8ff9\uff0c\u518d\u901a\u8fc7 image-conditioned teacher \u7684 on-policy self-distillation \u63d0\u4f9b\u7a20\u5bc6 token \u76d1\u7763\u3002<\/li>\n<\/ol>\n

          \u6d88\u878d\u5b9e\u9a8c\u8868\u660e\uff0c\u4e09\u9636\u6bb5\u627f\u62c5\u4e0d\u540c\u4f5c\u7528\uff1aSFT \u5e26\u6765\u6700\u5927\u7684\u5355\u9636\u6bb5\u7ed3\u6784\u63d0\u5347\uff0ccold-start \u52a0\u5f3a\u4ece\u7528\u6237\u8bf7\u6c42\u5230 SP \u7684\u63a8\u5bfc\uff0c\u800c verifier-gated OPSD \u5728 prompter \u81ea\u8eab\u5206\u5e03\u4e0a\u53d6\u5f97\u6700\u5f3a\u7ed3\u679c\u3002<\/p>\n

          \"\u56fe\u7247\"<\/p>\n

          \u8bba\u6587 Figure 9\uff5c\u56fa\u5b9a schema \u4e0e diffuser \u540e\uff0c\u751f\u6210\u8d28\u91cf\u968f LLM prompter \u5c3a\u5ea6\u548c reasoning mode \u63d0\u5347<\/p>\n

          \"\u56fe\u7247\"<\/p>\n

          \u8bba\u6587 Figure 10\uff5cSFT\u3001cold-start \u4e0e verifier-gated RFT \u4e09\u9636\u6bb5 prompter \u8bad\u7ec3<\/p>\n

          \u7ed3\u6784\u5316\u8868\u793a\uff0c\u4e5f\u8ba9\u751f\u6210\u8fc7\u7a0b\u66f4\u5bb9\u6613\u8fed\u4ee3\u4fee\u6b63<\/h4>\n

          SP \u7684\u5b57\u6bb5\u5316\u8868\u793a\u8fd8\u6709\u4e00\u4e2a\u81ea\u7136\u4f18\u52bf\uff1a\u5f53\u751f\u6210\u56fe\u50cf\u51fa\u73b0\u9519\u8bef\u65f6\uff0c\u7cfb\u7edf\u53ef\u4ee5\u5b9a\u4f4d\u5e76\u4fee\u6539\u76f8\u5e94\u7684\u5bf9\u8c61\u3001\u5c5e\u6027\u3001\u5173\u7cfb\u6216\u5e03\u5c40\u5b57\u6bb5\uff0c\u800c\u4e0d\u662f\u91cd\u65b0\u6539\u5199\u6574\u6bb5\u81ea\u7136\u8bed\u8a00 Prompt\u3002<\/p>\n

          \u57fa\u4e8e\u6b64\uff0c\u56e2\u961f\u6784\u5efa\u4e86 refine-render-judge \u5faa\u73af\u3002\u6bcf\u4e00\u8f6e\u4e2d\uff0cprompter \u6839\u636e\u7528\u6237\u8bf7\u6c42\u548c\u5386\u53f2\u53cd\u9988\u751f\u6210\u6216\u4fee\u8ba2 SP\uff0c\u56fa\u5b9a diffuser \u6e32\u67d3\u56fe\u50cf\uff0c\u5728\u7ebf judge \u518d\u9488\u5bf9 Prompt \u9075\u5faa\u3001\u7ed3\u6784\u548c\u89c6\u89c9\u8d28\u91cf\u7ed9\u51fa PASS\/FAIL \u4e0e\u5177\u4f53\u95ee\u9898\u3002\u82e5\u5931\u8d25\uff0c\u4e0b\u4e00\u8f6e\u53ea\u9700\u56f4\u7ed5\u76f8\u5173\u5b57\u6bb5\u8fdb\u884c\u8c03\u6574\u3002<\/p>\n

          \u5b9e\u9a8c\u663e\u793a\uff0c\u589e\u52a0\u8fed\u4ee3\u9884\u7b97\u80fd\u591f\u7ee7\u7eed\u63d0\u9ad8\u7ed3\u6784\u3001\u5bf9\u9f50\u548c GSB \u8868\u73b0\uff1b\u4f46\u6709\u6548\u63a8\u7406\u957f\u5ea6\u5e76\u4e0d\u957f\u3002\u5bf9\u4e8e\u8bad\u7ec3\u540e\u7684 prompter\uff0c\u5373\u4f7f\u5141\u8bb8\u6700\u591a 8 \u8f6e\uff0c\u5e73\u5747\u4e5f\u53ea\u4f7f\u7528 2.31 \u8f6e\uff1b\u4ece Tmax = 4 \u589e\u52a0\u5230 8\uff0c\u6536\u76ca\u5df2\u7ecf\u5f88\u5c0f\u3002\u8fd9\u8868\u660e\uff0c\u6587\u751f\u56fe\u786e\u5b9e\u53d7\u76ca\u4e8e\u8fed\u4ee3\u7ea0\u9519\uff0c\u4f46\u5728\u5f53\u524d\u8bbe\u7f6e\u4e0b\u5e76\u4e0d\u9700\u8981\u5f88\u957f\u7684 prompt-side reasoning trajectory\uff1a\u4fee\u590d\u4e3b\u8981\u89c4\u683c\u9519\u8bef\u540e\uff0c\u989d\u5916\u8f6e\u6b21\u4f1a\u8fc5\u901f\u9971\u548c\u3002<\/p>\n

          \"\u56fe\u7247\"<\/p>\n

          \u8bba\u6587 Figure 14\uff5cRefine-render-judge \u7684 agentic \u63a8\u7406\u5faa\u73af<\/p>\n

          \"\u56fe\u7247\"<\/p>\n

          \u8bba\u6587 Figure 15\uff5c\u5faa\u73af\u80fd\u591f\u4fee\u6b63\u5bf9\u8c61\u62c6\u5206\u3001\u5173\u7cfb\u548c\u6574\u4f53\u5e03\u5c40\u95ee\u9898<\/p>\n

          \u540c\u4e00\u5957 Qwen-Image\uff0c\u7ed3\u6784\u5316\u63a5\u53e3\u5e26\u6765\u4e86\u591a\u5927\u63d0\u5347\uff1f<\/h4>\n

          \u6700\u7ec8\u7cfb\u7edf\u7531 SP-trained diffuser \u4e0e\u8bad\u7ec3\u540e\u7684 LLM prompter \u7ec4\u6210\u3002\u5b83\u5728\u51e0\u4e4e\u6240\u6709\u62a5\u544a\u6307\u6807\u4e0a\u9886\u5148\u6240\u6bd4\u8f83\u7684\u5f00\u6e90\u6743\u91cd\u6a21\u578b\uff0c\u5e76\u5728\u5927\u591a\u6570\u8bc4\u6d4b\u4e0a\u8fbe\u5230\u6216\u8d85\u8fc7\u6240\u6bd4\u8f83\u7684\u95ed\u6e90\u7cfb\u7edf\uff0c\u4f18\u52bf\u5728\u7ec4\u5408\u3001\u63a8\u7406\u548c\u4e16\u754c\u77e5\u8bc6\u4efb\u52a1\u4e0a\u5c24\u5176\u660e\u663e\u3002<\/p>\n

          \u66f4\u5173\u952e\u7684\u662f matched control\u3002\u4e3a\u4e86\u6392\u9664 \u201c\u53ea\u662f\u591a\u8bad\u7ec3\u4e86\u4e00\u4e9b\u201d \u7684\u89e3\u91ca\uff0c\u56e2\u961f\u4f7f\u7528\u5b8c\u5168\u76f8\u540c\u7684 Qwen-Image \u67b6\u6784\u3001\u8bad\u7ec3\u56fe\u50cf\u3001\u8bad\u7ec3\u9636\u6bb5\u548c\u9884\u7b97\uff0c\u989d\u5916\u8bad\u7ec3\u4e86\u4e00\u5957\u59cb\u7ec8\u4f7f\u7528\u81ea\u7531\u81ea\u7136\u8bed\u8a00 Caption \u7684\u7cfb\u7edf\u3002\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n

          \"\u56fe\u7247\"<\/p>\n

          \u8bba\u6587 Table 2\uff5c\u4e0e\u4ee3\u8868\u6027\u6587\u751f\u56fe\u7cfb\u7edf\u7684\u5b8c\u6574\u5bf9\u6bd4\u3002\u622a\u56fe\u4fdd\u7559\u8bba\u6587\u4e2d\u7684\u8bc4\u6d4b\u5b9a\u4e49\u3001\u52a0\u7c97\u4e0e\u811a\u6ce8<\/p>\n

          Matched NL \u7684\u989d\u5916\u8bad\u7ec3\u786e\u5b9e\u5e26\u6765\u4e86\u4e00\u4e9b\u63d0\u5347\uff0c\u4f46\u8fdc\u4e0d\u8db3\u4ee5\u590d\u73b0 SP \u7cfb\u7edf\u7684\u7ed3\u679c\u3002\u8fd9\u8bf4\u660e\uff0c\u6536\u76ca\u4e0d\u80fd\u7b80\u5355\u5f52\u56e0\u4e8e\u66f4\u5927\u7684 backbone \u6216\u66f4\u591a\u8bad\u7ec3\uff0c\u800c\u4e0e prompter \u548c diffuser \u4e4b\u95f4\u6240\u4f7f\u7528\u7684\u7ed3\u6784\u5316 Caption \u63a5\u53e3\u5bc6\u5207\u76f8\u5173\u3002<\/p>\n

          \"\u56fe\u7247\"<\/p>\n

          \u8bba\u6587 Figure 11\uff5c\u590d\u6742\u7a7a\u95f4\u5173\u7cfb\u3001\u6570\u91cf\u548c\u5c5e\u6027\u7ed1\u5b9a\u7684\u5b9a\u6027\u5bf9\u6bd4<\/p>\n

          \u4e0b\u4e00\u6b65\u4e0d\u53ea\u662f scale \u6a21\u578b\uff0c\u4e5f\u8981 scale \u6761\u4ef6\u672c\u8eab<\/h4>\n

          \u8fd9\u9879\u5de5\u4f5c\u7684\u51fa\u53d1\u70b9\u5f88\u7b80\u5355\uff1a\u5bf9\u4e8e\u6587\u751f\u56fe\u6a21\u578b\uff0cCaption \u4e0d\u662f\u65e0\u5173\u7d27\u8981\u7684\u5143\u6570\u636e\uff0c\u800c\u662f\u56fe\u50cf\u5185\u5bb9\u8fdb\u5165\u6587\u672c\u6761\u4ef6\u5b66\u4e60\u7684\u4e3b\u8981\u63a5\u53e3\u3002<\/p>\n

          \u5f53 Caption \u53ea\u662f\u53d8\u957f\u65f6\uff0c\u65b0\u589e token \u53ef\u80fd\u53ea\u662f\u6539\u5199\u548c\u94fa\u9648\uff1b\u5f53\u56fe\u50cf\u4fe1\u606f\u88ab\u51c6\u786e\u63d0\u53d6\u3001\u6e05\u6670\u7ed1\u5b9a\u5e76\u7a33\u5b9a\u7ec4\u7ec7\u65f6\uff0c\u540c\u4e00\u4e2a\u751f\u6210\u6a21\u578b\u624d\u80fd\u4ece\u4e2d\u5b66\u4e60\u66f4\u591a\u3002GPG \u548c ED \u8ba9\u8fd9\u79cd\u4fe1\u606f\u6210\u4e3a\u53ef\u6d4b\u91cf\u7684\u53d8\u91cf\uff0cStructured Prompt \u63d0\u9ad8 Diffusability\uff0cLLM scaling\u3001post-training \u548c\u77ed\u7a0b agentic refinement \u5219\u63d0\u9ad8 Promptability\u3002<\/p>\n

          \u56e0\u6b64\uff0c\u6587\u751f\u56fe\u7684\u4e0b\u4e00\u6b65 scaling \u4e0d\u5e94\u53ea\u5173\u6ce8 \u201c\u8d1f\u8d23\u6e32\u67d3\u7684\u6a21\u578b\u6709\u591a\u5927\u201d\uff0c\u8fd8\u5e94\u5173\u6ce8\uff1a<\/p>\n

          \u4f20\u9012\u7ed9\u6a21\u578b\u7684\u6587\u672c\u6761\u4ef6\uff0c\u7a76\u7adf\u643a\u5e26\u4e86\u591a\u5c11\u5b83\u771f\u6b63\u80fd\u591f\u5b66\u4e60\u548c\u4f7f\u7528\u7684\u56fe\u50cf\u4fe1\u606f\uff1f<\/p>\n

          \u6587\u7ae0\u6765\u81ea\uff1a51CTO<\/p>\n<\/div>\n<\/div>\n

          <\/div>\n

          <\/g><\/svg><\/i> \"Loading\"<\/p>\n

          <\/div>\n","protected":false},"excerpt":{"rendered":"

          \u56e2\u961f\u63d0\u51fa Structured Prompt\uff0c\u5e76\u4ece Diffusability \u4e0e Promptability […]<\/p>\n

          <\/div>\n

          <\/g><\/svg><\/i> \"Loading\"<\/p>\n

          <\/div>\n","protected":false},"author":56,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[3,23,20,80],"tags":[2276],"class_list":["post-30020","post","type-post","status-publish","format-standard","hentry","category-ai","category-23","category-20","category-80","tag-scalingseed"],"_links":{"self":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/30020","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/users\/56"}],"replies":[{"embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/comments?post=30020"}],"version-history":[{"count":1,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/30020\/revisions"}],"predecessor-version":[{"id":30021,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/30020\/revisions\/30021"}],"wp:attachment":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/media?parent=30020"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/categories?post=30020"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/tags?post=30020"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}