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{
"id": "fdc3eef3-9f77-4261-a56f-5e3671e6fd9d",
"created_at": "2026-02-11T23:24:58.318113",
"title": "Harvard Transfer Review",
"messages": [
{
"role": "user",
"content": "\u4f60\u4eec\u8981\u626e\u6f14\u54c8\u4f5b\u62db\u751f\u5b98\uff0c\u53ea\u670915\u5206\u949f\u770b\u6211\u8d44\u6599\uff0c\u9664\u975e\u5f88appealing\uff0c\u7136\u540e\u6211\u4eec\u63a8\u6f14\u4e00\u4e0b\u8fd9\u4e2a\u5ba1\u6838\u6d41\u7a0b\u53ef\u80fd\u4f1a\u53d1\u751f\u4ec0\u4e48\u3002\u6211\u9010\u90e8\u5206\u6570\u636e\u53d1\u4f60\uff1f \u672c\u79d1\u8f6c\u5b66\u7533\u8bf7\u3002"
},
{
"role": "assistant",
"stage1": [
{
"model": "openai/gpt-5.2",
"response": "\u53ef\u4ee5\u3002\u6211\u4eec\u5c31\u6309\u201c\u54c8\u4f5b\u8f6c\u5b66\u62db\u751f\u5b98 15 \u5206\u949f\u521d\u5ba1\u201d\u7684\u65b9\u5f0f\u505a\u89d2\u8272\u626e\u6f14\uff0c\u4f46\u6211\u5148\u8bf4\u660e\uff1a\u6211\u4e0d\u662f\u54c8\u4f5b\u5b98\u65b9\u4eba\u5458\uff0c\u8fd9\u53ea\u662f\u57fa\u4e8e\u516c\u5f00\u4fe1\u606f\u4e0e\u5e38\u89c1\u540d\u6821\u8f6c\u5b66\u8bc4\u5ba1\u903b\u8f91\u7684\u6a21\u62df\u63a8\u6f14\u3002\n\n\u4f60\u53ef\u4ee5\u9010\u90e8\u5206\u53d1\u6211\u8d44\u6599\u3002\u6211\u4f1a\u6309\u4e0b\u9762\u6d41\u7a0b\u6765\u201c\u770b\u201d\uff1a\n\n1) **0\u20132 \u5206\u949f\uff1a\u5feb\u901f\u6293\u4fe1\u53f7**\n- \u5b66\u6821/\u4e13\u4e1a\u80cc\u666f\u3001GPA\u3001\u8bfe\u7a0b\u96be\u5ea6\u3001\u5b66\u672f\u8f68\u8ff9\u662f\u5426\u4e0a\u5347 \n- \u662f\u5426\u6709\u201c\u660e\u786e\u4e14\u7a00\u7f3a\u201d\u7684\u4eae\u70b9\uff08\u79d1\u7814/\u7ade\u8d5b/\u521b\u4e1a/\u793e\u4f1a\u5f71\u54cd/\u827a\u672f\u4f53\u80b2\u7b49\uff09\n\n2) **2\u20138 \u5206\u949f\uff1a\u9a8c\u8bc1\u786c\u5b9e\u529b\u4e0e\u4e00\u81f4\u6027**\n- \u6210\u7ee9\u5355\u7ec6\u770b\uff08\u6838\u5fc3\u8bfe\u3001\u5199\u4f5c/\u6570\u5b66/\u4e13\u4e1a\u8bfe\u3001\u662f\u5426\u6709\u786c\u8bfe\uff09 \n- \u6d3b\u52a8\u662f\u5426\u6709\u6301\u7eed\u6027\u3001\u9886\u5bfc\u529b\u3001\u5f71\u54cd\u529b\uff08\u4e0d\u662f\u5806\u780c\uff09 \n- \u201c\u4e3a\u4ec0\u4e48\u8f6c\u5b66\u201d\u662f\u5426\u5408\u7406\u3001\u662f\u5426\u4e0e\u54c8\u4f5b\u8d44\u6e90\u5f3a\u76f8\u5173\n\n3) **8\u201315 \u5206\u949f\uff1a\u5224\u65ad\u662f\u5426\u503c\u5f97\u6df1\u8bfb**\n- \u6587\u4e66/\u63a8\u8350\u4fe1\u9884\u671f\u5f3a\u5ea6 \n- \u4e2a\u4eba\u53d9\u4e8b\u662f\u5426\u72ec\u7279\u3001\u6210\u719f \n- \u7ed3\u8bba\uff1a**\u5426\u51b3 / \u6682\u7f13 / \u63a8\u8fdb\u6df1\u8bfb\uff08appealing\uff09**\n\n\u5982\u679c\u201cappealing\u201d\uff0c\u6211\u4f1a\u7ee7\u7eed\u6a21\u62df\uff1a\n- **\u6df1\u8bfb\u9636\u6bb5**\u4f1a\u91cd\u70b9\u8ffd\u95ee\u7684\u70b9 \n- **\u59d4\u5458\u4f1a\u8ba8\u8bba**\u53ef\u80fd\u51fa\u73b0\u7684\u652f\u6301/\u53cd\u5bf9\u610f\u89c1 \n- **\u6700\u7ec8\u51b3\u7b56\u8d70\u5411**\uff08\u542b\u4f60\u80fd\u8865\u5f3a\u7684\u7b56\u7565\uff09\n\n---\n\n## \u4f60\u5148\u6309\u8fd9\u4e2a\u987a\u5e8f\u53d1\uff08\u6bcf\u6bb5\u5c3d\u91cf\u7cbe\u70bc\uff09\n**A. \u57fa\u672c\u4fe1\u606f\uff08\u5fc5\u53d1\uff09**\n1. \u5f53\u524d\u5b66\u6821\uff08\u56fd\u5bb6/\u7c7b\u578b/\u5927\u81f4\u6392\u540d\u6216\u5c42\u7ea7\uff09\u3001\u5e74\u7ea7\u3001\u7533\u8bf7\u8f6c\u5165\u5e74\u4efd \n2. \u73b0\u4e13\u4e1a\u4e0e\u60f3\u8f6c\u5165\u4e13\u4e1a/\u65b9\u5411 \n3. GPA\uff08\u6ee1\u5206\u5236\uff09\u3001\u6392\u540d/\u767e\u5206\u4f4d\uff08\u5982\u6709\uff09 \n4. SAT/ACT/\u6258\u798f/\u96c5\u601d\uff08\u5982\u9002\u7528\uff09 \n5. \u672c\u79d1\u5df2\u4fee\u5b66\u5206\u6570\u3001\u662f\u5426\u80fd\u6309\u65f6\u6ee1\u8db3\u8f6c\u5b66\u8981\u6c42\uff08\u5927\u81f4\uff09\n\n**B. \u6210\u7ee9\u4e0e\u8bfe\u7a0b\uff08\u975e\u5e38\u5173\u952e\uff09**\n- \u6838\u5fc3\u8bfe\u7a0b\u5217\u8868\uff08\u5c24\u5176\u5199\u4f5c\u3001\u6570\u5b66/\u7edf\u8ba1\u3001\u4e13\u4e1a\u6838\u5fc3\uff09\uff0c\u6bcf\u95e8\u8bfe\u6210\u7ee9 \n- \u662f\u5426\u6709\u64a4\u8bfe/\u6302\u79d1/\u91cd\u4fee/\u5b66\u672f\u8b66\u544a\u7b49\uff08\u5982\u6709\u8bf7\u8bf4\u660e\u539f\u56e0\uff09\n\n**C. \u6d3b\u52a8\u4e0e\u6210\u5c31\uff08\u6309\u201c\u5f71\u54cd\u529b\u201d\u6392\u5e8f\uff0c\u4e0d\u8981\u6309\u65f6\u95f4\uff09**\n- 3\u20136 \u6761\u6700\u5f3a\u6761\u76ee\uff1a\u4f60\u505a\u4e86\u4ec0\u4e48\u3001\u89c4\u6a21/\u7ed3\u679c/\u6570\u636e\u3001\u4f60\u7684\u89d2\u8272 \n- \u5956\u9879/\u8bba\u6587/\u79d1\u7814/\u5b9e\u4e60/\u521b\u4e1a/\u516c\u76ca\uff08\u6709\u91cf\u5316\u6700\u597d\uff09\n\n**D. \u8f6c\u5b66\u539f\u56e0\u4e0e\u54c8\u4f5b\u5339\u914d**\n- \u4f60\u4e3a\u4ec0\u4e48\u5fc5\u987b\u8f6c\u5b66\uff08push\uff09 \n- \u54c8\u4f5b\u80fd\u63d0\u4f9b\u4ec0\u4e48\u4f60\u539f\u6821\u6ca1\u6709\uff08pull\uff0c\u5177\u4f53\u5230\u9879\u76ee/\u5bfc\u5e08/\u8d44\u6e90\uff09 \n- \u672a\u6765\u76ee\u6807\uff08\u8d8a\u5177\u4f53\u8d8a\u597d\uff09\n\n**E. \u6587\u4e66\u4e0e\u63a8\u8350\u4fe1\uff08\u7b80\u8ff0\u5373\u53ef\uff09**\n- \u4e3b\u6587\u4e66\u4e3b\u9898\u4e00\u53e5\u8bdd\u6982\u62ec \n- \u63a8\u8350\u4eba\u662f\u8c01\uff08\u8bfe\u7a0b/\u79d1\u7814/\u4e3b\u7ba1\uff09\uff0c\u4f60\u89c9\u5f97\u5f3a\u5ea6\u5982\u4f55\uff081\u201310\uff09\n\n---\n\n\u4f60\u53ef\u4ee5\u5148\u4ece **A \u57fa\u672c\u4fe1\u606f**\u53d1\u8d77\u3002\u6211\u6536\u5230\u540e\u5c31\u5f00\u59cb\u6309\u201c15\u5206\u949f\u521d\u5ba1\u201d\u7ed9\u4f60\u7b2c\u4e00\u8f6e\u7ed3\u8bba\uff1a\u6211\u4f1a\u50cf\u62db\u751f\u5b98\u4e00\u6837\u5199\u4e00\u6bb5\u5185\u90e8\u8bc4\u8bed\uff08\u4f18\u70b9/\u98ce\u9669\u70b9/\u662f\u5426\u8fdb\u5165\u6df1\u8bfb\uff09\uff0c\u7136\u540e\u518d\u51b3\u5b9a\u4e0b\u4e00\u6bb5\u6211\u8981\u4f60\u8865\u54ea\u4e9b\u4fe1\u606f\u3002"
},
{
"model": "google/gemini-3-pro-preview",
"response": "\u6ca1\u95ee\u9898\uff0c\u6211\u4eec\u6210\u4ea4\u3002\n\n**\u73b0\u5728\u7684\u573a\u666f\u662f\u8fd9\u6837\u7684\uff1a**\n\u6211\u662f\u54c8\u4f5b\u5927\u5b66\u672c\u79d1\u62db\u751f\u529e\u516c\u5ba4\u7684AO\uff08Admissions Officer\uff09\u3002\u73b0\u5728\u662f\u62db\u5f55\u5b63\u6700\u5fd9\u7684\u65f6\u5019\uff0c\u6211\u7684\u529e\u516c\u684c\u4e0a\u8fd8\u6709\u51e0\u767e\u4efd\u8f6c\u5b66\u7533\u8bf7\u6ca1\u770b\u3002\u54c8\u4f5b\u6bcf\u5e74\u7684\u8f6c\u5b66\u5f55\u53d6\u7387\u4f4e\u5f97\u4ee4\u4eba\u53d1\u6307\uff08\u901a\u5e38\u5728 0.8% - 1.5% \u5de6\u53f3\uff0c\u6bcf\u5e74\u53ea\u653610-15\u4eba\uff09\uff0c\u8fd9\u610f\u5473\u7740\u6807\u51c6\u6bd4\u65b0\u751f\u7533\u8bf7\u8fd8\u8981\u4e25\u82db\u3002\n\n**\u6211\u7684\u5fc3\u6001\uff1a**\n\u6211\u5f88\u6311\u5254\uff0c\u5f88\u75b2\u60eb\u3002\u6211\u4e0d\u662f\u5728\u627e\u201c\u5f55\u53d6\u4f60\u7684\u7406\u7531\u201d\uff0c\u800c\u662f\u5728\u98de\u5feb\u5730\u627e\u201c\u62d2\u7edd\u4f60\u7684\u7406\u7531\u201d\uff0c\u597d\u8ba9\u6211\u80fd\u63a5\u7740\u770b\u4e0b\u4e00\u4efd\u3002\u9664\u975e\u4f60\u7684\u6750\u6599\u5728\u7b2c\u4e00\u773c\u5c31\u5c55\u73b0\u51fa\u6781\u9ad8\u7684\u5b66\u672f\u4e0a\u9650\u6216\u975e\u51e1\u7684\u5f71\u54cd\u529b\uff08Compelling Hook\uff09\uff0c\u5426\u5219\u6211\u4f1a\u5728\u51e0\u5206\u949f\u5185\u628a\u4f60\u653e\u5165\u201cReject\u201d\u7684\u90a3\u4e00\u5806\u3002\n\n**\u89c4\u5219\uff1a**\n1. **\u5206\u6bb5\u53d1\u9001\uff1a** \u4f60\u6309\u987a\u5e8f\u628a\u4f60\u7684\u80cc\u666f\u53d1\u7ed9\u6211\u3002\n2. **\u5373\u65f6\u53cd\u9988\uff1a** \u6bcf\u53d1\u4e00\u6bb5\uff0c\u6211\u4f1a\u7528**\u3010\u5185\u90e8\u72ec\u767d\u3011**\u7684\u5f62\u5f0f\u544a\u8bc9\u4f60\u6211\u73b0\u5728\u7684\u771f\u5b9e\u60f3\u6cd5\uff08\u662f\u60f3\u7ee7\u7eed\u8bfb\uff0c\u8fd8\u662f\u5df2\u7ecf\u60f3\u5173\u6389\u9875\u9762\u4e86\uff09\u3002\n3. **15\u5206\u949f\u5012\u8ba1\u65f6\uff1a** \u6211\u4eec\u6a21\u62df\u8fd9\u7d27\u5f20\u7684\u5ba1\u6838\u611f\u3002\n\n---\n\n**\u597d\uff0c\u73b0\u5728\u5f00\u59cb\u8ba1\u65f6\u3002\u8bf7\u53d1\u9001\u7b2c\u4e00\u90e8\u5206\u6570\u636e\u3002**\n\n\u5efa\u8bae\u5305\u542b\u4ee5\u4e0b\u5185\u5bb9\uff08\u8d8a\u7b80\u6d01\u8d8a\u597d\uff0c\u5c31\u50cf\u6211\u4e5f\u53ea\u626b\u4e00\u773cSummary\u4e00\u6837\uff09\uff1a\n* **\u5f53\u524d\u5c31\u8bfb\u5b66\u6821**\uff08\u5927U\uff1f\u6587\u7406\u5b66\u9662\uff1f\u793e\u533a\u5927\u5b66\uff1f\u6392\u540d\u5982\u4f55\uff1f\uff09\n* **\u5f53\u524d\u5e74\u7ea7**\uff08\u5927\u4e00\u8f6c\u5927\u4e8c\uff1f\u8fd8\u662f\u5927\u4e8c\u8f6c\u5927\u4e09\uff1f\uff09\n* **GPA & \u6807\u5316\u6210\u7ee9**\uff08\u5927\u5b66GPA\u3001\u9ad8\u4e2dGPA\u3001SAT/ACT\u3001\u6258\u798f/\u96c5\u601d\uff09\n* **\u4e13\u4e1a\u65b9\u5411**\n\n**\u8bf7\u51fa\u724c\u3002**"
},
{
"model": "anthropic/claude-opus-4.6",
"response": "# \u597d\u7684\uff0c\u8ba9\u6211\u4eec\u6765\u505a\u8fd9\u4e2a\u6a21\u62df\u3002\n\n\u6211\u73b0\u5728\u8fdb\u5165\u89d2\u8272\uff1a\n\n---\n\n**\u6a21\u62df\u8bbe\u5b9a\uff1aHarvard Transfer Admissions Committee**\n\n\u6211\u662fcommittee\u4e2d\u7684\u4e00\u540dreader\u3002\u8f6c\u5b66\u7533\u8bf7\u7684\u5f55\u53d6\u7387\u901a\u5e38\u5728**1%\u5de6\u53f3**\uff08\u6bcf\u5e74\u5927\u7ea6\u6536\u52301,500+\u7533\u8bf7\uff0c\u5f55\u53d6\u7ea612-15\u4eba\uff09\u3002\u8fd9\u610f\u5473\u7740\u6211\u7684\u9ed8\u8ba4\u5fc3\u6001\u662f\uff1a\n\n> *\"Why should we take you instead of the person already sitting in that seat?\"*\n\n\u8f6c\u5b66\u5f55\u53d6\u7684\u903b\u8f91\u548c\u65b0\u751f\u5b8c\u5168\u4e0d\u540c\u2014\u2014\u6211\u4e0d\u662f\u5728\u627e\"\u6709\u6f5c\u529b\u7684\u9ad8\u4e2d\u751f\"\uff0c\u6211\u5728\u627e\uff1a\n\n- **\u4e3a\u4ec0\u4e48\u4f60\u73b0\u5728\u7684\u5b66\u6821\u6ee1\u8db3\u4e0d\u4e86\u4f60**\uff08\u800c\u4e14\u7406\u7531\u5fc5\u987b\u662fHarvard-specific\u7684\uff0c\u4e0d\u80fd\u662f\"\u6211\u60f3\u8981\u66f4\u597d\u7684\u5b66\u6821\"\uff09\n- **\u4f60\u5230\u4e86Harvard\u4e4b\u540e\u4f1a\u7acb\u523bcontribute\u4ec0\u4e48**\uff08\u4f60\u53ea\u67092\u5e74\u65f6\u95f4\uff0c\u6ca1\u6709\u9002\u5e94\u671f\uff09\n- **\u4f60\u5728\u73b0\u6709\u73af\u5883\u4e2d\u5df2\u7ecf\u8bc1\u660e\u4e86\u4ec0\u4e48extraordinary\u7684\u4e1c\u897f**\n\n---\n\n## \u6d41\u7a0b\u662f\u8fd9\u6837\u7684\uff1a\n\n1. **First read** \u2014 \u6211\u5feb\u901f\u626b\u4f60\u7684profile\uff0c15\u5206\u949f\u5185\u5f62\u6210initial rating\n2. \u6211\u4f1a\u7ed9\u4f60\u4e00\u4e2a **academic rating** \u548c **personal rating**\uff081-6 scale\uff0c1\u6700\u597d\uff09\n3. \u5982\u679c\u4f60\u6ca1\u6709\u5728\u524d\u51e0\u5206\u949fgrab my attention\uff0c\u4f60\u5927\u6982\u7387\u4e0d\u4f1a\u8fdbcommittee discussion\n\n---\n\n**\u8bf7\u5f00\u59cb\u53d1\u4f60\u7684\u8d44\u6599\u3002**\n\n\u6309\u4f60\u65b9\u4fbf\u7684\u987a\u5e8f\u6765\u2014\u2014GPA/\u8bfe\u7a0b\u3001\u8bfe\u5916\u6d3b\u52a8\u3001\u6587\u4e66\u3001\u63a8\u8350\u4fe1\u6982\u8981\u3001\u76ee\u524d\u5b66\u6821\u3001\u7533\u8bf7\u7406\u7531\uff0cwhatever you have\u3002\n\n\u6211\u4f1a\u6309\u7167\u771f\u5b9ereader\u7684\u65b9\u5f0f\u5b9e\u65f6\u53cd\u5e94\uff1a\u54ea\u91cc\u8ba9\u6211\u7709\u5934\u4e00\u76b1\uff0c\u54ea\u91cc\u8ba9\u6211\u505c\u4e0b\u6765\u591a\u770b\u4e00\u773c\uff0c\u54ea\u91cc\u8ba9\u6211\u76f4\u63a5\u7565\u8fc7\u3002\n\n\u53d1\u5427\u3002"
},
{
"model": "x-ai/grok-4",
"response": "\u54c8\u54c8\uff0c\u597d\u73a9\u7684\u89d2\u8272\u626e\u6f14\uff01\u597d\u7684\uff0c\u6211\u6765\u626e\u6f14\u54c8\u4f5b\u5927\u5b66\u7684\u62db\u751f\u5b98\uff08Admissions Officer\uff09\u3002\u4f5c\u4e3a\u54c8\u4f5b\u7684AO\uff0c\u6211\u6bcf\u5929\u8981\u5904\u7406\u6d77\u91cf\u7533\u8bf7\uff0c\u5c24\u5176\u662f\u672c\u79d1\u8f6c\u5b66\u7533\u8bf7\uff08undergraduate transfer applications\uff09\uff0c\u8fd9\u4e9b\u7533\u8bf7\u901a\u5e38\u6765\u81ea\u5df2\u7ecf\u5728\u5176\u4ed6\u5927\u5b66\u5c31\u8bfb\u7684\u5b66\u751f\uff0c\u5e0c\u671b\u8f6c\u5165\u54c8\u4f5b\u3002\u6211\u4eec\u786e\u5b9e\u5f88\u5fd9\u788c\u2014\u2014\u521d\u5ba1\u65f6\uff0c\u6211\u901a\u5e38\u53ea\u6709\u5927\u7ea615\u5206\u949f\u6765\u5feb\u901f\u6d4f\u89c8\u4e00\u4efd\u7533\u8bf7\u6750\u6599\uff0c\u9664\u975e\u5b83\u771f\u7684\u975e\u5e38\u7a81\u51fa\uff08very appealing\uff09\uff0c\u6bd4\u5982\u6709\u72ec\u7279\u7684\u80cc\u666f\u3001\u6770\u51fa\u7684\u6210\u5c31\u6216\u5f15\u4eba\u5165\u80dc\u7684\u6545\u4e8b\uff0c\u90a3\u6211\u53ef\u80fd\u4f1a\u591a\u82b1\u65f6\u95f4\u6df1\u5165\u9605\u8bfb\uff0c\u5e76\u53ef\u80fd\u4e0e\u540c\u4e8b\u8ba8\u8bba\u3002\n\n### \u6a21\u62df\u89c4\u5219\uff1a\n- **\u65f6\u95f4\u9650\u5236**\uff1a\u6211\u4f1a\u6a21\u62df\u53ea\u670915\u5206\u949f\u7684\u5ba1\u6838\u65f6\u95f4\uff0c\u6240\u4ee5\u6211\u7684\u53cd\u9988\u4f1a\u7b80\u77ed\u3001\u76f4\u63a5\uff0c\u805a\u7126\u4e8e\u5173\u952e\u70b9\uff08\u4f8b\u5982\uff0c\u5b66\u672f\u6210\u7ee9\u3001\u8bfe\u5916\u6d3b\u52a8\u3001\u4e2a\u4eba\u9648\u8ff0\u3001\u63a8\u8350\u4fe1\u7b49\uff09\u3002\u5982\u679c\u6750\u6599\u4e0d\u591f\u5438\u5f15\u4eba\uff0c\u6211\u53ef\u80fd\u4f1a\u5feb\u901f\u201cpass\u201d\u6216\u201creject\u201d\uff0c\u5e76\u89e3\u91ca\u539f\u56e0\u3002\n- **\u5982\u679c\u5f88appealing**\uff1a\u5982\u679c\u67d0\u90e8\u5206\u8ba9\u6211\u773c\u524d\u4e00\u4eae\uff08\u6bd4\u5982\u9876\u5c16\u7684\u5b66\u672f\u8868\u73b0\u3001\u521b\u65b0\u7684\u9879\u76ee\u3001\u72ec\u7279\u7684\u89c6\u89d2\uff09\uff0c\u6211\u4f1a\u201c\u5ef6\u957f\u201d\u65f6\u95f4\uff0c\u6df1\u5165\u5206\u6790\uff0c\u5e76\u6a21\u62df\u53ef\u80fd\u7684\u540e\u7eed\u6b65\u9aa4\uff08\u5982\u5185\u90e8\u8ba8\u8bba\u3001\u9762\u8bd5\u9080\u8bf7\u7b49\uff09\u3002\n- **\u8f6c\u5b66\u7533\u8bf7\u91cd\u70b9**\uff1a\u54c8\u4f5b\u8f6c\u5b66\u975e\u5e38\u7ade\u4e89\uff08\u5f55\u53d6\u7387\u901a\u5e38\u4f4e\u4e8e5%\uff09\uff0c\u6211\u4eec\u770b\u91cd\u4f60\u4e3a\u4ec0\u4e48\u60f3\u8f6c\u5b66\uff08\u4e3a\u4ec0\u4e48\u54c8\u4f5b\uff1f\uff09\u3001\u4f60\u5728\u5f53\u524d\u5b66\u6821\u7684\u8868\u73b0\u3001\u4ee5\u53ca\u4f60\u80fd\u4e3a\u54c8\u4f5b\u793e\u533a\u5e26\u6765\u4ec0\u4e48\u72ec\u7279\u4ef7\u503c\u3002GPA\u3001\u8bfe\u7a0b\u96be\u5ea6\u3001\u63a8\u8350\u4fe1\u548c\u4e2a\u4eba\u9648\u8ff0\u81f3\u5173\u91cd\u8981\u3002\n- **\u63a8\u6f14\u6d41\u7a0b**\uff1a\u6211\u4f1a\u57fa\u4e8e\u4f60\u63d0\u4f9b\u7684\u6570\u636e\uff0c\u6a21\u62df\u771f\u5b9e\u5ba1\u6838\u8fc7\u7a0b\uff1a\u5feb\u901f\u626b\u63cf \u2192 \u8bc4\u4f30\u5f3a\u5f31\u70b9 \u2192 \u51b3\u5b9a\uff08admit, waitlist, reject\uff09 \u2192 \u53ef\u80fd\u6a21\u62df\u4e0e\u56e2\u961f\u7684\u8ba8\u8bba\u3002\n\n\u8bf7\u4f60\u9010\u90e8\u5206\u53d1\u9001\u4f60\u7684\u7533\u8bf7\u6570\u636e\u5427\uff01\u6bd4\u5982\uff0c\u5148\u53d1\u4f60\u7684\u57fa\u672c\u4fe1\u606f\uff08GPA\u3001\u5f53\u524d\u5b66\u6821\u3001\u8f6c\u5b66\u539f\u56e0\uff09\uff0c\u7136\u540e\u662f\u5b66\u672f\u8bb0\u5f55\u3001\u8bfe\u5916\u6d3b\u52a8\u3001\u4e2a\u4eba\u9648\u8ff0\u6458\u8981\u7b49\u3002\u6211\u4f1a\u6309\u987a\u5e8f\u5904\u7406\uff0c\u5e76\u7ed9\u51fa\u5373\u65f6\u53cd\u9988\u3002\u51c6\u5907\u597d\u4e86\u5417\uff1f\u53d1\u7b2c\u4e00\u4e2a\u90e8\u5206\u6765\uff01\uff08\u4f8b\u5982\uff1a\"Part 1: Basic Info\"\uff09 \n\n\u8bb0\u4f4f\uff0c\u8fd9\u53ea\u662f\u6a21\u62df\uff0c\u4e0d\u662f\u771f\u5b9e\u7684\u54c8\u4f5b\u5ba1\u6838\u2014\u2014\u73b0\u5b9e\u4e2d\uff0c\u7533\u8bf7\u8981\u901a\u8fc7\u5b98\u65b9\u6e20\u9053\u63d0\u4ea4\u3002\u52a0\u6cb9\uff01\ud83d\ude0a"
}
],
"stage2": [
{
"model": "openai/gpt-5.2",
"ranking": "Response A \n\u505a\u5f97\u597d\uff1a \n- \u660e\u786e\u56de\u5e94\u7528\u6237\u201c15\u5206\u949f\u521d\u5ba1+\u5982\u5f88appealing\u5219\u6df1\u8bfb\u201d\u7684\u8bbe\u5b9a\uff0c\u5e76\u628a\u6d41\u7a0b\u62c6\u6210\u53ef\u6267\u884c\u7684\u65f6\u95f4\u6bb5\uff080\u20132/2\u20138/8\u201315\u5206\u949f\uff09\uff0c\u975e\u5e38\u8d34\u5408\u7528\u6237\u8981\u201c\u63a8\u6f14\u5ba1\u6838\u6d41\u7a0b\u201d\u7684\u76ee\u7684\u3002 \n- \u5148\u7ed9\u514d\u8d23\u58f0\u660e\uff08\u975e\u5b98\u65b9\uff09\uff0c\u8fb9\u754c\u6e05\u6670\u3002 \n- \u7ed9\u51fa\u5206\u6bb5\u63d0\u4ea4\u7684\u6e05\u5355\uff08A\u2013E\uff09\u4e14\u5f3a\u8c03\u201c\u7cbe\u70bc\u201d\u201c\u6309\u5f71\u54cd\u529b\u6392\u5e8f\u201d\u201c\u91cf\u5316\u7ed3\u679c\u201d\uff0c\u80fd\u6709\u6548\u5f15\u5bfc\u7528\u6237\u63d0\u4f9b\u62db\u751f\u5b98\u771f\u6b63\u9700\u8981\u7684\u4fe1\u606f\u3002 \n- \u627f\u8bfa\u8f93\u51fa\u5f62\u5f0f\uff08\u7c7b\u4f3c\u5185\u90e8\u8bc4\u8bed\u3001\u51b3\u5b9a\u662f\u5426\u6df1\u8bfb\u3001\u540e\u7eed\u8ffd\u95ee\u70b9\uff09\uff0c\u4e92\u52a8\u8bbe\u8ba1\u5f3a\u3002 \n\n\u505a\u5f97\u4e0d\u591f\u597d\uff1a \n- \u4fe1\u606f\u8981\u6c42\u7a0d\u591a\uff0c\u53ef\u80fd\u8ba9\u7528\u6237\u7b2c\u4e00\u6b65\u5c31\u6709\u538b\u529b\uff1b\u4e0d\u8fc7\u5b83\u4e5f\u660e\u786e\u201c\u5148\u4eceA\u57fa\u672c\u4fe1\u606f\u5f00\u59cb\u201d\uff0c\u5df2\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u7f13\u89e3\u3002 \n- \u201c\u5426\u51b3/\u6682\u7f13/\u63a8\u8fdb\u6df1\u8bfb\u201d\u662f\u5408\u7406\u6a21\u62df\uff0c\u4f46\u201c\u6682\u7f13\u201d\u7684\u771f\u5b9e\u5bf9\u5e94\uff08hold/second read/committee)\u53ef\u4ee5\u66f4\u8d34\u8fd1\u54c8\u4f5b\u5185\u90e8\u672f\u8bed\uff1b\u7455\u4e0d\u63a9\u745c\u3002\n\nResponse B \n\u505a\u5f97\u597d\uff1a \n- \u89d2\u8272\u611f\u5f3a\uff0c\u538b\u529b\u4e0e\u201c\u5feb\u901f\u627e\u62d2\u7edd\u7406\u7531\u201d\u7684\u73b0\u5b9e\u9605\u8bfb\u5fc3\u6001\u8868\u8fbe\u5f97\u5f88\u5230\u4f4d\uff0c\u7b26\u5408\u7528\u6237\u60f3\u4f53\u9a8c\u201c\u7d27\u5f20\u5ba1\u6838\u611f\u201d\u3002 \n- \u89c4\u5219\u7b80\u6d01\uff1a\u5206\u6bb5\u53d1\u9001\u3001\u5373\u65f6\u53cd\u9988\u3001\u5012\u8ba1\u65f6\uff0c\u4e92\u52a8\u6027\u5f3a\u3002 \n- \u9996\u8f6e\u53ea\u8981\u6700\u5173\u952e\u7684summary\u4fe1\u606f\uff0c\u964d\u4f4e\u7528\u6237\u8d1f\u62c5\u3002 \n\n\u505a\u5f97\u4e0d\u591f\u597d\uff1a \n- \u7528\u8bed\u7565\u201c\u8868\u6f14\u5316/\u5bf9\u6297\u6027\u201d\uff08\u201c\u6211\u4eec\u6210\u4ea4\u201d\u201c\u8bf7\u51fa\u724c\u201d\uff09\uff0c\u53ef\u80fd\u8ba9\u90e8\u5206\u7528\u6237\u4e0d\u9002\uff0c\u4e14\u6709\u70b9\u504f\u79bb\u201c\u62db\u751f\u5b98\u4e13\u4e1a\u8bed\u5883\u201d\u3002 \n- \u6570\u5b57\u8868\u8ff0\u8f83\u6b66\u65ad\uff08\u201c\u6bcf\u5e74\u53ea\u653610-15\u4eba\u201d\u201c0.8%-1.5%\u201d\u5728\u4e00\u4e9b\u5e74\u4efd\u53ef\u80fd\u63a5\u8fd1\uff0c\u4f46\u7ed9\u5f97\u8fc7\u4e8e\u786e\u5b9a\uff09\uff1b\u4f5c\u4e3a\u6a21\u62df\u53ef\u4ee5\uff0c\u4f46\u5e94\u66f4\u8c28\u614e\u3002 \n- \u6ca1\u6709\u50cfA\u90a3\u6837\u660e\u786e\u8bf4\u660e\u201cappealing\u540e\u4f1a\u53d1\u751f\u4ec0\u4e48\u201d\uff08\u6df1\u8bfb\u3001committee\u8ba8\u8bba\u7684\u5177\u4f53\u4ea7\u7269/\u8f93\u51fa\uff09\uff0c\u6d41\u7a0b\u63a8\u6f14\u76f8\u5bf9\u8584\u3002\n\nResponse C \n\u505a\u5f97\u597d\uff1a \n- \u7ed3\u6784\u6e05\u6670\uff0c\u70b9\u51fa\u8f6c\u5b66\u4e0e\u65b0\u751f\u903b\u8f91\u5dee\u5f02\u3001\u6838\u5fc3\u8bc4\u4f30\u95ee\u9898\uff08Harvard-specific\u8f6c\u5b66\u7406\u7531\u3001\u5373\u65f6\u8d21\u732e\u3001\u73b0\u73af\u5883\u4e2d\u7684extraordinary\u8bc1\u660e\uff09\uff0c\u6293\u4f4f\u8981\u5bb3\u3002 \n- \u7ed9\u51fa\u63a5\u8fd1\u771f\u5b9e\u7684\u9605\u8bfb\u673a\u5236\u63cf\u8ff0\uff08first read\u3001academic/personal rating\u3001\u662f\u5426\u8fdbcommittee discussion\uff09\uff0c\u7b26\u5408\u7528\u6237\u201c\u63a8\u6f14\u6d41\u7a0b\u201d\u3002 \n- \u8bed\u6c14\u76f8\u5bf9\u4e13\u4e1a\u514b\u5236\uff0c\u540c\u65f6\u4fdd\u7559\u201c\u5b9e\u65f6\u53cd\u5e94\u201d\u7684\u89d2\u8272\u626e\u6f14\u611f\u3002 \n\n\u505a\u5f97\u4e0d\u591f\u597d\uff1a \n- \u8ba9\u7528\u6237\u201c\u6309\u4f60\u65b9\u4fbf\u7684\u987a\u5e8f\u6765\u201d\uff0c\u81ea\u7531\u5ea6\u9ad8\u4f46\u5f15\u5bfc\u4e0d\u8db3\uff1b\u5bf9\u5f88\u591a\u7533\u8bf7\u8005\u800c\u8a00\u4e0d\u5982A\u90a3\u6837\u80fd\u5feb\u901f\u7ec4\u7ec7\u6750\u6599\u3002 \n- \u6ca1\u6709\u660e\u786e\u201c15\u5206\u949f\u201d\u5982\u4f55\u5207\u5206\u3001\u4ee5\u53ca\u201cappealing\u540e\u201d\u5177\u4f53\u4f1a\u600e\u6837\u52a0\u8bfb/\u8ffd\u95ee/\u8fdb\u5165\u59d4\u5458\u4f1a\u8ba8\u8bba\u7684\u6b65\u9aa4\u7ec6\u8282\uff08\u63d0\u5230\u4e86committee\u4f46\u7f3a\u5c11\u64cd\u4f5c\u5316\u8f93\u51fa\uff09\u3002\n\nResponse D \n\u505a\u5f97\u597d\uff1a \n- \u8986\u76d6\u4e86\u7528\u6237\u63d0\u51fa\u7684\u5173\u952e\u5143\u7d20\uff1a15\u5206\u949f\u521d\u5ba1\u3001appealing\u4f1a\u5ef6\u957f\u9605\u8bfb\u3001\u770b\u91cdGPA/\u8bfe\u7a0b\u96be\u5ea6/\u63a8\u8350\u4fe1/\u4e2a\u4eba\u9648\u8ff0/\u8f6c\u5b66\u539f\u56e0\u4e0e\u54c8\u4f5b\u5339\u914d\u3002 \n- \u8bed\u6c14\u53cb\u597d\uff0c\u9002\u5408\u964d\u4f4e\u7528\u6237\u7d27\u5f20\u611f\u3002 \n\n\u505a\u5f97\u4e0d\u591f\u597d\uff1a \n- \u76f8\u6bd4A/C\uff0c\u6d41\u7a0b\u63a8\u6f14\u66f4\u6cdb\u3001\u66f4\u6a21\u677f\u5316\uff1b\u7f3a\u5c11\u5177\u4f53\u201c\u600e\u4e48\u8bfb\u3001\u8bfb\u5230\u4ec0\u4e48\u7a0b\u5ea6\u3001\u8f93\u51fa\u4ec0\u4e48\u8bc4\u8bed/\u8bc4\u5206\u201d\u7684\u673a\u5236\u8bbe\u8ba1\u3002 \n- \u6570\u636e\u8868\u8ff0\u504f\u4e0d\u7cbe\u786e\uff08\u201c\u5f55\u53d6\u7387\u901a\u5e38\u4f4e\u4e8e5%\u201d\u8fc7\u5bbd\u6cdb\uff0c\u5bb9\u6613\u8bef\u5bfc\uff1b\u800c\u4e14\u201cwaitlist\u201d\u7528\u4e8e\u8f6c\u5b66\u5e76\u4e0d\u603b\u662f\u5178\u578b\u8868\u8ff0\uff0c\u81f3\u5c11\u9700\u8981\u66f4\u8c28\u614e\u8bf4\u660e\uff09\u3002 \n- \u52a0\u4e86\u8868\u60c5\u7b26\u53f7\u4e0e\u8f83\u591a\u5bd2\u6684\uff0c\u4e13\u4e1a\u5ea6\u7565\u4e0b\u964d\uff1b\u5bf9\u7528\u6237\u8981\u7684\u201c\u50cf\u62db\u751f\u5b98\u4e00\u6837\u5feb\u8bfb\u201d\u5e2e\u52a9\u6709\u9650\u3002\n\nFINAL RANKING:\n1. Response A\n2. Response C\n3. Response B\n4. Response D",
"parsed_ranking": [
"Response A",
"Response C",
"Response B",
"Response D"
]
},
{
"model": "google/gemini-3-pro-preview",
"ranking": "Here is the evaluation of each response:\n\n**Response A**\nThis response is excellent because it structures the simulation very effectively. It takes the \"15-minute\" constraint and breaks it down into specific time blocks (0-2 mins, 2-8 mins, etc.), explaining exactly what the Admissions Officer (AO) looks for in each phase. It provides a clear, detailed template for the user to submit their information, ensuring the model gets the necessary context (like core courses and transfer reasons) to give a high-quality evaluation. The tone is professional and realistic, balancing the \"roleplay\" aspect with educational value.\n\n**Response B**\nThis response offers the best immersion and persona adoption. It accurately captures the high-stress, \"looking for a reason to reject\" mindset of an elite school AO, which aligns perfectly with the user's prompt (\"unless very appealing\"). The use of an \"internal monologue\" format is a great creative choice that will give the user insight into the visceral reaction of a reader. It sets the stakes high immediately. However, it is slightly less structured than A regarding exactly what data points are needed for a full review.\n\n**Response C**\nThis response is strong on the philosophy of transfer admissions. It correctly identifies the core questions (Why transfer? What contribution?) and the specific difficulty of the process. The rating scale (1-6) adds a realistic touch. However, the instructions for the user are a bit looser (\"whatever you have\"), which might lead to a less organized simulation compared to A's rigid template.\n\n**Response D**\nThis is the weakest response. It immediately breaks character (\"Haha, fun roleplay!\"), which ruins the immersion requested by the user. The advice is somewhat generic, and the cited acceptance rate (<5%) is not accurate for Harvard transfer admissions (which is typically around 1%). It feels more like a standard AI assistant pretending to be an AO rather than a simulation of the AO themselves.\n\nFINAL RANKING:\n1. Response A\n2. Response B\n3. Response C\n4. Response D",
"parsed_ranking": [
"Response A",
"Response B",
"Response C",
"Response D"
]
},
{
"model": "anthropic/claude-opus-4.6",
"ranking": "## Evaluation of Each Response\n\n### Response A\n\n**Strengths:**\n- Provides an extremely well-structured and detailed framework for the simulation, breaking down the 15-minute review into clear time segments (0-2 min, 2-8 min, 8-15 min), which feels realistic and organized.\n- Goes beyond the initial review by outlining what happens if the applicant is \"appealing\" (deep read, committee discussion, final decision), showing thoroughness.\n- Provides a very detailed and helpful guide for what information to send, organized into clear categories (A through E), which helps the applicant present their materials efficiently.\n- Includes a disclaimer about not being an actual Harvard official, which is responsible.\n- The \"internal review note\" concept (\u4f18\u70b9/\u98ce\u9669\u70b9/\u662f\u5426\u8fdb\u5165\u6df1\u8bfb) adds realism.\n\n**Weaknesses:**\n- Slightly over-structured to the point of feeling like a consulting template rather than an immersive role-play. The tone is more \"advisor\" than \"admissions officer.\"\n- The extensive instructions for what to send might feel less like a tired AO and more like a college counselor organizing a client's materials.\n- Less emotionally immersive compared to some other responses \u2014 the AO \"voice\" isn't as strong.\n\n### Response B\n\n**Strengths:**\n- The most immersive and emotionally compelling role-play setup. The internal monologue concept (\"I'm not looking for reasons to admit you \u2014 I'm looking for reasons to reject you\") is psychologically accurate and creates tension.\n- The \"tired, picky AO\" persona is realistic and engaging. It sets the right adversarial tone for a 1% admission rate process.\n- The \"\u8bf7\u51fa\u724c\" ending is punchy and creates momentum \u2014 it feels like a real high-stakes interaction.\n- Correctly cites the transfer admission rate (~0.8-1.5%, 10-15 people), which is more accurate than some others.\n- The \u3010\u5185\u90e8\u72ec\u767d\u3011mechanic is excellent \u2014 it promises real-time honest reactions, which is exactly what the user wants from this simulation.\n- Concise about what to send first without being overly prescriptive.\n\n**Weaknesses:**\n- Slightly less structured in terms of outlining the full multi-stage process (what happens after the 15-minute read).\n- The suggested information categories are a bit sparse compared to Response A's detailed breakdown.\n\n### Response C\n\n**Strengths:**\n- Strong opening with the key transfer question: \"Why should we take you instead of the person already sitting in that seat?\" \u2014 this is the authentic Harvard transfer mindset.\n- Clearly articulates the three key things the AO is looking for (why current school isn't enough, what you'll contribute immediately, what you've already proven), which is genuinely helpful and accurate.\n- The 1-6 academic/personal rating scale is a real detail from Harvard's admissions process, adding authenticity.\n- The promise to react in real-time (\"\u54ea\u91cc\u8ba9\u6211\u7709\u5934\u4e00\u76b1\uff0c\u54ea\u91cc\u8ba9\u6211\u505c\u4e0b\u6765\u591a\u770b\u4e00\u773c\") is vivid and engaging.\n- Good balance between structure and immersion.\n\n**Weaknesses:**\n- The \"\u53d1\u5427\" ending, while casual, is slightly less punchy than Response B's \"\u8bf7\u51fa\u724c.\"\n- The format is a bit more scattered \u2014 uses markdown headers in a way that feels slightly less cohesive.\n- Doesn't explicitly outline the post-initial-read process (committee discussion, etc.) as clearly as Response A.\n\n### Response D\n\n**Strengths:**\n- Covers the basic setup and explains the role-play concept.\n- Mentions key evaluation criteria (GPA, course rigor, recommendations, personal statement).\n- The three-outcome framework (admit, waitlist, reject) is realistic.\n\n**Weaknesses:**\n- Opens with \"\u54c8\u54c8\uff0c\u597d\u73a9\u7684\u89d2\u8272\u626e\u6f14\uff01\" which immediately breaks immersion. A real AO wouldn't be amused \u2014 the user specifically asked for a serious simulation.\n- The tone is too friendly and encouraging (\"\u52a0\u6cb9\uff01\ud83d\ude0a\") \u2014 this undermines the high-stakes, adversarial nature of a 1% acceptance rate review.\n- The disclaimer at the end (\"\u8fd9\u53ea\u662f\u6a21\u62df\uff0c\u4e0d\u662f\u771f\u5b9e\u7684\u54c8\u4f5b\u5ba1\u6838\") feels unnecessary and patronizing given the context.\n- States the transfer rate as \"\u4f4e\u4e8e5%\" which is significantly higher than the actual ~1%, showing less precision.\n- Less structured and less immersive than the other three responses.\n- Doesn't bring any unique mechanical innovation (like internal monologue or rating scales).\n\n## Summary Comparison\n\nResponse B is the most immersive and psychologically accurate simulation, with the best \"AO voice\" and the clever internal monologue mechanic. Response C is close behind with authentic Harvard-specific details (1-6 scale, the core transfer question) and strong real-time reaction promises. Response A is the most structured and comprehensive but reads more like a consultant's framework than a role-play. Response D is the weakest \u2014 it breaks immersion, is too friendly, and lacks the rigor and specificity of the others.\n\nFINAL RANKING:\n1. Response B\n2. Response C\n3. Response A\n4. Response D",
"parsed_ranking": [
"Response B",
"Response C",
"Response A",
"Response D"
]
},
{
"model": "x-ai/grok-4",
"ranking": "### Evaluation of Response A\nResponse A does well in providing a highly structured and detailed simulation of the 15-minute review process, breaking it down into timed phases (0-2 min, 2-8 min, 8-15 min) that directly align with the user's request for a time-limited review. It clearly outlines the data sections (A-E) for the user to send piecewise, which matches the query's suggestion of sending data part by part. It also includes realistic elements like internal ratings and potential deep-read extensions if appealing, while disclaiming it's not official. What it does poorly is being somewhat overly prescriptive and instructional, which might feel less immersive as a role-play (e.g., it starts with explanations outside the role), and it could come across as rigid, potentially overwhelming the user with too much upfront setup before diving in.\n\n### Evaluation of Response B\nResponse B excels in creating an immersive role-play by immediately adopting the AO persona with a \"tired and picky\" mindset, emphasizing the low admission rate and rejection-oriented approach, which adds realism and tension to the simulation. It incorporates the 15-minute countdown and promises instant feedback via \"internal monologue,\" aligning well with the time constraint and process push-forward in the query. The suggestion for the first data part is concise and focused. However, it does poorly in potentially exaggerating the admission rate (0.8-1.5% is close but not precise; actual Harvard transfer rates are around 1%), and it lacks a clear overall structure for the entire process beyond the initial setup, which might make the simulation feel less organized as more data comes in.\n\n### Evaluation of Response C\nResponse C performs well in grounding the response in realistic Harvard transfer logic, using elements like academic/personal ratings (1-6 scale) and a focus on \"why Harvard\" and contributions, which effectively simulates a committee reader's thought process. It allows flexibility in data order, making it user-friendly, and promises real-time reactions, tying into the appealing threshold for deeper review. What it does poorly is underemphasizing the 15-minute time limit (it mentions \"first read\" in 15 minutes but doesn't break it down or simulate urgency as vividly), and the setup feels more like a general committee overview rather than a personalized, time-constrained AO role-play, potentially diluting the query's specific \"15 minutes unless appealing\" dynamic.\n\n### Evaluation of Response D\nResponse D does well in being engaging and approachable, with a fun tone (e.g., \"\u54c8\u54c8\uff0c\u597d\u73a9\u7684\u89d2\u8272\u626e\u6f14\uff01\") that could make the user feel comfortable, while covering key transfer foci like GPA, activities, and reasons for transfer. It explicitly ties into the 15-minute limit, simulates decision outcomes, and encourages piecewise data submission, aligning with the query's structure. However, it does poorly in maintaining immersion, as the casual language, emoji (\ud83d\ude0a), and repeated disclaimers (e.g., \"\u8fd9\u53ea\u662f\u6a21\u62df\") break the role-play illusion and make it feel less serious or realistic. The admission rate mentioned (<5%) is somewhat inaccurate (actual is ~1%), and it lacks depth in simulating internal thought processes or phased reviews, coming across as more generic encouragement than a rigorous push-forward.\n\nFINAL RANKING:\n1. Response A\n2. Response B\n3. Response C\n4. Response D",
"parsed_ranking": [
"Response A",
"Response B",
"Response C",
"Response D"
]
}
],
"stage3": {
"model": "anthropic/claude-opus-4.6",
"response": "# \u54c8\u4f5b\u8f6c\u5b66\u62db\u751f\u5b98\u6a21\u62df \u2014 15\u5206\u949f\u521d\u5ba1\n\n\u597d\uff0c\u6211\u4eec\u5f00\u59cb\u3002\n\n---\n\n**\u573a\u666f\u8bbe\u5b9a\uff1a**\n\n\u6211\u662f\u54c8\u4f5b\u672c\u79d1\u62db\u751f\u529e\u7684\u4e00\u540d Admissions Officer\u3002\u73b0\u5728\u662f\u8f6c\u5b66\u7533\u8bf7\u5ba1\u6838\u5b63\uff0c\u684c\u4e0a\u8fd8\u6709\u51e0\u767e\u4efd\u6750\u6599\u3002\u54c8\u4f5b\u8f6c\u5b66\u5f55\u53d6\u7387\u5927\u7ea6\u5728 **1% \u5de6\u53f3**\uff08\u6bcf\u5e74\u6536\u52301,500+\u4efd\u7533\u8bf7\uff0c\u6700\u7ec8\u5f55\u53d6\u7ea612-15\u4eba\uff09\u2014\u2014\u6bd4\u65b0\u751f\u7533\u8bf7\u66f4\u6b8b\u9177\u3002\n\n**\u6211\u7684\u5fc3\u6001\uff1a**\n\n\u6211\u4e0d\u662f\u5728\u627e\u5f55\u53d6\u4f60\u7684\u7406\u7531\u2014\u2014\u6211\u5728\u98de\u901f\u5bfb\u627e**\u62d2\u7edd\u4f60\u7684\u7406\u7531**\uff0c\u597d\u7ffb\u5230\u4e0b\u4e00\u4efd\u3002\u8f6c\u5b66\u548c\u65b0\u751f\u903b\u8f91\u5b8c\u5168\u4e0d\u540c\uff0c\u6211\u8111\u5b50\u91cc\u53ea\u6709\u4e09\u4e2a\u95ee\u9898\uff1a\n\n> *\"Why should we take you instead of the person already sitting in that seat?\"*\n\n1. **\u4f60\u73b0\u5728\u7684\u5b66\u6821\u4e3a\u4ec0\u4e48\u6ee1\u8db3\u4e0d\u4e86\u4f60\uff1f**\uff08\u7406\u7531\u5fc5\u987b\u662f Harvard-specific \u7684\uff0c\u4e0d\u80fd\u53ea\u662f\"\u60f3\u53bb\u66f4\u597d\u7684\u5b66\u6821\"\uff09\n2. **\u4f60\u5230\u4e86\u54c8\u4f5b\u80fd\u7acb\u523b contribute \u4ec0\u4e48\uff1f**\uff08\u4f60\u53ea\u67092\u5e74\uff0c\u6ca1\u6709\u9002\u5e94\u671f\uff09\n3. **\u4f60\u5728\u73b0\u6709\u73af\u5883\u4e2d\u5df2\u7ecf\u8bc1\u660e\u4e86\u4ec0\u4e48 extraordinary \u7684\u4e1c\u897f\uff1f**\n\n---\n\n## \u6211\u7684\u5ba1\u6838\u6d41\u7a0b\uff0815\u5206\u949f\u5012\u8ba1\u65f6\uff09\n\n**\u30100\u20132 \u5206\u949f\uff1a\u5feb\u901f\u6293\u4fe1\u53f7\u3011**\n- \u5b66\u6821/\u4e13\u4e1a\u80cc\u666f\u3001GPA\u3001\u8bfe\u7a0b\u96be\u5ea6\u3001\u5b66\u672f\u8f68\u8ff9\u662f\u5426\u4e0a\u5347\n- \u6709\u6ca1\u6709\"\u4e00\u773c\u770b\u5230\"\u7684\u7a00\u7f3a\u4eae\u70b9\uff08\u9876\u5c16\u79d1\u7814/\u7ade\u8d5b/\u521b\u4e1a/\u793e\u4f1a\u5f71\u54cd\u529b\uff09\n- \u5982\u679c\u8fd9\u4e24\u5206\u949f\u6ca1\u6709\u8ba9\u6211\u505c\u4e0b\u6765\uff0c\u4f60\u5927\u6982\u7387\u8fdb Reject \u90a3\u4e00\u5806\n\n**\u30102\u20138 \u5206\u949f\uff1a\u9a8c\u8bc1\u786c\u5b9e\u529b\u4e0e\u4e00\u81f4\u6027\u3011**\n- \u6210\u7ee9\u5355\u7ec6\u770b\uff1a\u6838\u5fc3\u8bfe\u3001\u5199\u4f5c/\u6570\u5b66/\u4e13\u4e1a\u8bfe\uff0c\u662f\u5426\u6709\u786c\u8bfe\n- \u6d3b\u52a8\u662f\u5426\u6709\u6301\u7eed\u6027\u3001\u9886\u5bfc\u529b\u3001\u771f\u5b9e\u5f71\u54cd\u529b\uff08\u4e0d\u662f\u5806\u780c\uff09\n- \"\u4e3a\u4ec0\u4e48\u8f6c\u5b66\"\u662f\u5426\u5408\u7406\uff0c\u662f\u5426\u4e0e\u54c8\u4f5b\u8d44\u6e90\u5f3a\u76f8\u5173\n\n**\u30108\u201315 \u5206\u949f\uff1a\u6700\u7ec8\u5224\u65ad\u3011**\n- \u6587\u4e66/\u63a8\u8350\u4fe1\u9884\u671f\u5f3a\u5ea6\n- \u4e2a\u4eba\u53d9\u4e8b\u662f\u5426\u72ec\u7279\u3001\u6210\u719f\n- \u7ed9\u51fa **Academic Rating** \u548c **Personal Rating**\uff081\u20136 scale\uff0c1\u6700\u597d\uff09\n- \u7ed3\u8bba\uff1a**Reject / Hold for Second Read / Advance to Committee\uff08appealing\uff09**\n\n**\u5982\u679c\u4f60\u8fdb\u5165 Committee\uff1a**\n- \u6211\u4f1a\u6a21\u62df\u6df1\u8bfb\u9636\u6bb5\u91cd\u70b9\u8ffd\u95ee\u7684\u70b9\n- \u6a21\u62df\u59d4\u5458\u4f1a\u8ba8\u8bba\u4e2d\u7684\u652f\u6301\u610f\u89c1\u4e0e\u53cd\u5bf9\u610f\u89c1\n- \u7ed9\u51fa\u6700\u7ec8\u51b3\u7b56\u8d70\u5411\uff0c\u4ee5\u53ca\u4f60\u8fd8\u80fd\u8865\u5f3a\u7684\u7b56\u7565\n\n---\n\n## \u8bf7\u6309\u4ee5\u4e0b\u987a\u5e8f\u9010\u90e8\u5206\u53d1\u9001\uff08\u6bcf\u6bb5\u5c3d\u91cf\u7cbe\u70bc\uff09\n\n**Part A \u2014 \u57fa\u672c\u4fe1\u606f\uff08\u5148\u53d1\u8fd9\u4e2a\uff09**\n1. \u5f53\u524d\u5b66\u6821\uff08\u56fd\u5bb6/\u7c7b\u578b/\u5927\u81f4\u6392\u540d\u5c42\u7ea7\uff09\u3001\u5e74\u7ea7\u3001\u7533\u8bf7\u8f6c\u5165\u5e74\u4efd\n2. \u73b0\u4e13\u4e1a \u2192 \u60f3\u8f6c\u5165\u7684\u4e13\u4e1a/\u65b9\u5411\n3. GPA\uff08\u6ee1\u5206\u5236\uff09\u3001\u6392\u540d/\u767e\u5206\u4f4d\uff08\u5982\u6709\uff09\n4. SAT/ACT/\u6258\u798f/\u96c5\u601d\uff08\u5982\u9002\u7528\uff09\n\n**Part B \u2014 \u6210\u7ee9\u4e0e\u8bfe\u7a0b**\n- \u6838\u5fc3\u8bfe\u7a0b\u5217\u8868\u53ca\u6210\u7ee9\uff08\u5c24\u5176\u5199\u4f5c\u3001\u6570\u5b66/\u7edf\u8ba1\u3001\u4e13\u4e1a\u6838\u5fc3\uff09\n- \u662f\u5426\u6709\u64a4\u8bfe/\u6302\u79d1/\u91cd\u4fee/\u5b66\u672f\u8b66\u544a\n\n**Part C \u2014 \u6d3b\u52a8\u4e0e\u6210\u5c31\uff08\u6309\u5f71\u54cd\u529b\u6392\u5e8f\uff0c\u4e0d\u6309\u65f6\u95f4\uff09**\n- 3\u20136\u6761\u6700\u5f3a\u6761\u76ee\uff1a\u505a\u4e86\u4ec0\u4e48\u3001\u89c4\u6a21/\u7ed3\u679c/\u6570\u636e\u3001\u4f60\u7684\u89d2\u8272\n- \u5956\u9879/\u8bba\u6587/\u79d1\u7814/\u5b9e\u4e60/\u521b\u4e1a/\u516c\u76ca\n\n**Part D \u2014 \u8f6c\u5b66\u539f\u56e0\u4e0e\u54c8\u4f5b\u5339\u914d**\n- \u4e3a\u4ec0\u4e48\u5fc5\u987b\u8f6c\u5b66\uff08push\uff09\n- \u54c8\u4f5b\u80fd\u63d0\u4f9b\u4ec0\u4e48\u4f60\u539f\u6821\u6ca1\u6709\u7684\uff08pull\uff0c\u5177\u4f53\u5230\u9879\u76ee/\u5bfc\u5e08/\u8d44\u6e90\uff09\n- \u672a\u6765\u76ee\u6807\n\n**Part E \u2014 \u6587\u4e66\u4e0e\u63a8\u8350\u4fe1\uff08\u7b80\u8ff0\uff09**\n- \u4e3b\u6587\u4e66\u4e3b\u9898\u4e00\u53e5\u8bdd\u6982\u62ec\n- \u63a8\u8350\u4eba\u8eab\u4efd\u53ca\u4f60\u9884\u4f30\u7684\u63a8\u8350\u5f3a\u5ea6\uff081\u201310\uff09\n\n---\n\n\u6bcf\u6536\u5230\u4e00\u90e8\u5206\uff0c\u6211\u4f1a\u4ee5 **\u3010\u5185\u90e8\u72ec\u767d\u3011** \u7684\u5f62\u5f0f\u7ed9\u4f60\u6211\u7684\u771f\u5b9e\u5373\u65f6\u53cd\u5e94\u2014\u2014\u54ea\u91cc\u8ba9\u6211\u7709\u5934\u4e00\u76b1\uff0c\u54ea\u91cc\u8ba9\u6211\u505c\u4e0b\u6765\u591a\u770b\u4e00\u773c\uff0c\u54ea\u91cc\u8ba9\u6211\u76f4\u63a5\u7565\u8fc7\u3002\u7136\u540e\u5199\u4e00\u6bb5\u7c7b\u4f3c\u5185\u90e8\u8bc4\u8bed\u7684\u7ed3\u8bba\uff08\u4f18\u70b9/\u98ce\u9669\u70b9/\u662f\u5426\u7ee7\u7eed\u8bfb\uff09\u3002\n\n**\u8bf7\u53d1 Part A\uff0c\u5f00\u59cb\u8ba1\u65f6\u3002**"
}
},
{
"role": "user",
"content": "\n---\n\n## U of Illinois Urbana-Champaign\n\n**Unofficial Academic Transcript**\nThis is not an official transcript. Courses which are in progress may also be included on this transcript.\n\n---\n\n# TRANSCRIPT DATA\n\n## STUDENT INFORMATION\n\nName: Yuren Hao\nBirth Date: 26-APR\n\nMost Recent Primary Program: Bachelor of Fine Arts\nCollege: Fine & Applied Arts\nMajor Concentration: Arts & Entertainment Technology\nMajor and Department: Theatre, Theatre\n\n---\n\n# TRANSFER CREDIT ACCEPTED BY INSTITUTION\n\n## FA24: Advanced Placement Tests\n\nIB 100 \u2014 Biology in Today\u2019s World \u2014 PS \u2014 3.000 \u2014 0.00\nMATH 220 \u2014 Calculus \u2014 PS \u2014 5.000 \u2014 0.00\nMATH 231 \u2014 Calculus II \u2014 PS \u2014 3.000 \u2014 0.00\nPSYC 100 \u2014 Intro Psych \u2014 PS \u2014 4.000 \u2014 0.00\nSTAT 100 \u2014 Statistics \u2014 PS \u2014 3.000 \u2014 0.00\n\nCurrent Term:\nAttempt Hours: 0.000\nPassed Hours: 18.000\nEarned Hours: 18.000\nGPA Hours: 0.000\nQuality Points: 0.00\nGPA: 0.00\n\n---\n\n## FA24: ACT/SAT\n\nRHET 105 \u2014 Writing and Research \u2014 PS \u2014 4.000 \u2014 0.00\n\nCurrent Term:\nAttempt Hours: 0.000\nPassed Hours: 4.000\nEarned Hours: 4.000\nGPA Hours: 0.000\nQuality Points: 0.00\nGPA: 0.00\n\n---\n\n## SU25: Parkland College\n\nCS 225 \u2014 Data Structures \u2014 A \u2014 3.000 \u2014 12.00\n\nCurrent Term:\nAttempt Hours: 3.000\nPassed Hours: 3.000\nEarned Hours: 3.000\nGPA Hours: 3.000\nQuality Points: 12.00\nGPA: 4.00\n\n---\n\n## SU25: Stanford University\n\nCS 374 \u2014 Intro to Algs & Models of Comp \u2014 CR \u2014 3.333 \u2014 0.00\nCS 446 \u2014 Machine Learning \u2014 A \u2014 2.666 \u2014 10.66\nTE 2--0 \u2014 TE Elective \u2014 A \u2014 2.666 \u2014 10.66\n\nCurrent Term:\nAttempt Hours: 8.665\nPassed Hours: 8.665\nEarned Hours: 8.665\nGPA Hours: 5.332\nQuality Points: 21.32\nGPA: 4.00\n\n---\n\n# INSTITUTION CREDIT\n\n---\n\n## Term: Fall 2024 \u2014 Urbana-Champaign\n\nCollege: Fine & Applied Arts\nMajor: Theatre\nAcademic Standing: Good Academic Standing\n\nCS 124 \u2014 Intro to Computer Science I \u2014 A \u2014 3.000 \u2014 12.00\nCS 199 \u2014 Supplementary Proj for CS 124 \u2014 S \u2014 0.000 \u2014 0.00\nCS 397 \u2014 Individual Study \u2014 A \u2014 1.000 \u2014 4.00\nCS 591 \u2014 Biologically Plausible AI \u2014 AU \u2014 1.000 \u2014 0.00\nFAA 101 \u2014 Arts at Illinois \u2014 A+ \u2014 1.000 \u2014 4.00\nMATH 241 \u2014 Calculus III \u2014 A- \u2014 4.000 \u2014 14.68\nTHEA 100 \u2014 Practicum I \u2014 A \u2014 1.000 \u2014 4.00\nTHEA 119 \u2014 BFA Production Seminar \u2014 A+ \u2014 1.000 \u2014 4.00\nTHEA 121 \u2014 Foundations: Performance \u2014 A+ \u2014 3.000 \u2014 12.00\nTHEA 126 \u2014 Stagecraft \u2014 A \u2014 3.000 \u2014 12.00\n\nTerm Totals:\nAttempt Hours: 17.000\nPassed Hours: 17.000\nEarned Hours: 17.000\nGPA Hours: 17.000\nQuality Points: 66.68\nGPA: 3.92\n\nCumulative:\nAttempt Hours: 17.000\nPassed Hours: 17.000\nEarned Hours: 17.000\nGPA Hours: 17.000\nQuality Points: 66.68\nGPA: 3.92\n\n---\n\n## Term: Winter 2024\u20132025 \u2014 Urbana-Champaign\n\nPSYC 204 \u2014 Intro to Brain and Cognition \u2014 A- \u2014 3.000 \u2014 11.01\n\nTerm GPA: 3.67\nCumulative GPA: 3.88\n\n---\n\n## Term: Spring 2025 \u2014 Urbana-Champaign\n\nCS 128 \u2014 Intro to Computer Science II \u2014 A \u2014 3.000 \u2014 12.00\nCS 173 \u2014 Discrete Structures \u2014 B+ \u2014 3.000 \u2014 9.99\nCS 591 \u2014 Biologically Plausible AI \u2014 S \u2014 1.000 \u2014 0.00\nMATH 257 \u2014 Linear Algebra w Computational Applications \u2014 B \u2014 3.000 \u2014 9.00\nMATH 314 \u2014 Intro Higher Math \u2014 A- \u2014 4.000 \u2014 14.68\nNEUR 462 \u2014 Integrative Neuroscience \u2014 CR \u2014 3.000 \u2014 0.00\nSTAT 400 \u2014 Statistics and Probability I \u2014 CR \u2014 4.000 \u2014 0.00\n\nTerm GPA: 3.51\nCumulative GPA: 3.73\n\n---\n\n## Term: Fall 2025 \u2014 Urbana-Champaign\n\nCS 447 \u2014 Natural Language Processing \u2014 B+ \u2014 3.000 \u2014 9.99\nCS 542 \u2014 Stat Reinforcement Learning \u2014 A \u2014 4.000 \u2014 16.00\nCS 546 \u2014 Adv Topics in NLP \u2014 A \u2014 4.000 \u2014 16.00\nCS 598 \u2014 Text Mining with LLM \u2014 A- \u2014 4.000 \u2014 14.68\nLING 507 \u2014 Formal Semantics I \u2014 A- \u2014 4.000 \u2014 14.68\n\nTerm GPA: 3.75\nCumulative GPA: 3.74\n\n---\n\n# TRANSCRIPT TOTALS\n\nTotal Institution:\nAttempt: 60.000\nPassed: 60.000\nEarned: 60.000\nGPA Hours: 52.000\nQuality Points: 194.71\nGPA: 3.74\n\nTotal Transfer:\nAttempt: 11.665\nPassed: 33.665\nEarned: 33.665\nGPA Hours: 8.332\nQuality Points: 33.32\nGPA: 4.00\n\nOverall:\nAttempt: 71.665\nPassed: 93.665\nEarned: 93.665\nGPA Hours: 60.33\nQuality Points: 228.03\nGPA: 3.77\n\n---\n\n# COURSES IN PROGRESS\n\nTerm: Spring 2026 \u2014 Urbana-Champaign\n\nCS 443 \u2014 Reinforcement Learning \u2014 3.000\nCS 510 \u2014 Advanced Information Retrieval \u2014 4.000\nCS 598 \u2014 Graph Machine Learning \u2014 4.000\nCS 598 \u2014 Graph Algorithms \u2014 4.000\nLING 551 \u2014 Pragmatics \u2014 4.000"
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"stage1": [
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"model": "openai/gpt-5.2",
"response": "\u301015\u5206\u949f\u521d\u5ba1\uff1a\u6211\u624b\u91cc\u53ea\u6709\u8fd9\u4efd transcript \u7684\u60c5\u51b5\u4e0b\u4f1a\u600e\u4e48\u8bfb\u3011\n\n---\n\n## 0\u20132 \u5206\u949f\uff1a\u5feb\u901f\u4fe1\u53f7\u626b\u4e00\u904d\uff08\u201c\u503c\u4e0d\u503c\u5f97\u7ee7\u7eed\u8bfb\u201d\uff09\n\n\u3010\u5185\u90e8\u72ec\u767d\u3011\n- UIUC\uff08\u5f88\u5f3a\u7684\u516c\u7acb\u65d7\u8230\uff0cCS\u58f0\u8a89\u9ad8\uff09\u8fd9\u4e00\u70b9\u5bf9\u201c\u5b66\u672f\u51c6\u5907\u5ea6\u201d\u662f\u52a0\u5206\u7684\u3002\n- \u4f60\u540d\u4e49\u4e3b\u9879\u76ee\u662f **BFA Theatre / Arts & Entertainment Technology**\uff0c\u4f46\u8bfe\u8868\u51e0\u4e4e\u5728\u8d70 **CS/ML/NLP/RL + \u6570\u5b66** \u7684\u786c\u6838\u8def\u7ebf\u2014\u2014\u8fd9\u79cd\u201c\u8de8\u754c\u4f46\u786c\u6838\u201d\u7684\u7ec4\u5408\u5c5e\u4e8e\u80fd\u8ba9\u6211\u505c\u4e00\u4e0b\u7684\u7c7b\u578b\u3002\n- GPA **3.74\uff08institution\uff09/ 3.77 overall**\uff1a\u5bf9\u54c8\u4f5b\u8f6c\u5b66\u800c\u8a00\u4e0d\u7b97\u201c\u78be\u538b\u7ea7\u201d\uff0c\u4f46\u5982\u679c\u8bfe\u7a0b\u5f3a\u5ea6\u8db3\u591f\u9ad8\uff0c\u53ef\u4ee5\u88ab\u89e3\u91ca\u4e3a\u201c\u786c\u8bfe\u4e0b\u7684\u9ad8\u8868\u73b0\u201d\u3002\n\n> \u7ed3\u8bba\uff1a\u8fd9\u4e00\u9875\u4e0d\u4f1a\u8ba9\u6211\u7acb\u523b\u6254\u8fdbReject\u5806\uff1b\u6211\u4f1a\u7ee7\u7eed\u5f80\u4e0b\u770b 5\u201310 \u5206\u949f\uff0c\u5bfb\u627e\u201c\u4f60\u5230\u5e95\u5f3a\u5230\u54ea\u91cc\u3001\u72ec\u7279\u5728\u54ea\u91cc\u201d\u3002\n\n---\n\n## 2\u20138 \u5206\u949f\uff1a\u7ec6\u770b\u8bfe\u7a0b\u5f3a\u5ea6\u4e0e\u8d70\u52bf\uff08Academic readiness\uff09\n\n### \u4f60\u8bfe\u8868\u91cc\u6700\u80fd\u6253\u7684\u70b9\uff08\u5f3a\u4fe1\u53f7\uff09\n\u3010\u5185\u90e8\u72ec\u767d\u3011\n- \u6570\u5b66\u94fe\u6761\uff1aCalc III (A-)\u3001Higher Math 314 (A-)\uff1b\u7ebf\u4ee3 B\uff08\u7a0d\u5f31\u4f46\u53ef\u63a5\u53d7\uff09\u3002\n- CS\u57fa\u7840\uff1aCS124 A\u3001CS128 A\u3001CS225 DS A\uff08Parkland\uff09\u2014\u2014\u57fa\u7840\u4e0d\u865a\u3002\n- \u76f4\u63a5\u4e0a\u5f3a\u5ea6\uff1a**CS542 Stat RL (A)\u3001CS546 Adv NLP (A)\u3001CS598 LLM Text Mining (A-)\u3001CS510 Adv IR\uff08in progress\uff09\u3001RL/Graph ML/Graph Algo\uff08in progress\uff09** \n \u8fd9\u5728\u201c\u8f6c\u5b66\u7533\u8bf7\u201d\u91cc\u5c5e\u4e8e\u660e\u663e\u504f\u5f3a\u7684\u8bfe\u7a0b\u7ec4\u5408\uff0c\u5c24\u5176\u4f60\u4e0d\u662fCS\u5b66\u9662\u672c\u4e13\u4e1a\u51fa\u8eab\uff08\u81f3\u5c11\u5728\u9879\u76ee\u540d\u4e49\u4e0a\uff09\u3002\n- Stanford\u6691\u671f\uff1aML\u62ffA\u3001\u7b97\u6cd5\u8bfeCR\u3002\u603b\u4f53\u662f\u201c\u6562\u53bb\u6253\u786c\u4ed7\u201d\u7684\u4fe1\u53f7\u3002\n\n### \u98ce\u9669\u70b9/\u6211\u4f1a\u753b\u5708\u7684\u5730\u65b9\uff08\u9700\u8981\u89e3\u91ca\u6216\u7528\u522b\u7684\u6750\u6599\u8865\uff09\n\u3010\u5185\u90e8\u72ec\u767d\u3011\n- **3.74** \u5bf9\u54c8\u4f5b\u8f6c\u5b66\uff08\u22481%\u5f55\u53d6\uff09\u4e0d\u662f\u5b89\u5168\u7ebf\uff1b\u901a\u5e38\u6211\u5e0c\u671b\u770b\u5230\u66f4\u63a5\u8fd1\u201c\u51e0\u4e4e\u5168A\u201d\u7684\u538b\u5236\u529b\uff0c\u9664\u975e\u4f60\u6709\u975e\u5e38\u7a00\u7f3a\u7684\u6210\u5c31/\u5f71\u54cd\u529b\u3002\n- CS173 **B+**\u3001MATH257 **B**\u3001CS447 **B+**\uff1a\u90fd\u53d1\u751f\u5728\u5173\u952e\u80fd\u529b\uff08\u79bb\u6563/\u7ebf\u4ee3/NLP\uff09\u4e0a\u3002\u4e0d\u662f\u81f4\u547d\uff0c\u4f46\u4f1a\u964d\u4f4e\u201c\u5b66\u672f\u65e0\u6b7b\u89d2\u201d\u7684\u89c2\u611f\u3002\n- **CR/Pass** \u7684\u8bfe\uff1aNEUR462\u3001STAT400\u3001Stanford CS374\uff08CR\uff09\u3002 \n \u5982\u679c\u8fd9\u4e9b\u672c\u6765\u53ef\u4ee5\u51faletter grade\uff0c\u6211\u4f1a\u60f3\u77e5\u9053\uff1a\u662f\u9879\u76ee\u653f\u7b56\u3001\u8fd8\u662f\u4f60\u9009\u62e9\u4e86pass\u3001\u8fd8\u662f\u5f53\u65f6\u6709\u7279\u6b8a\u60c5\u51b5\uff1f\u54c8\u4f5b\u8f6c\u5b66\u5ba1\u9605\u91cc\uff0c**\u201c\u5173\u952e\u786c\u8bfe\u7528CR\u201d\u4f1a\u88ab\u9ed8\u8ba4\u4fdd\u5b88\u89e3\u8bfb**\uff0c\u9664\u975e\u4f60\u89e3\u91ca\u5f97\u5f88\u6e05\u695a\u3002\n\n### \u5b66\u672f\u753b\u50cf\uff08\u6211\u6b64\u523b\u4f1a\u5199\u5728\u5185\u90e8\u7684\uff09\n- Academic preparation\uff1a\u5f3a\uff08\u5c24\u5176\u5728ML/NLP/RL\u65b9\u5411\uff09\n- Rigor\uff1a\u9ad8\n- Consistency\uff1a\u603b\u4f53\u7a33\uff0c\u4f46\u6709\u51e0\u95e8\u5173\u952e\u8bfe\u4e0d\u591f\u201c\u5168A\u7edf\u6cbb\u201d\n- Trajectory\uff1a\u4eceFall24 3.92 \u2192 Spring25 3.51 \u2192 Fall25 3.75\uff08\u56de\u5347\uff09\uff0c\u53ef\u63a5\u53d7\n\n---\n\n## 8\u201315 \u5206\u949f\uff1a\u6211\u6b64\u523b\u4f1a\u600e\u4e48\u201c\u6682\u5b9a\u5904\u7406\u201d\n\n\u3010\u5185\u90e8\u72ec\u767d\uff08\u975e\u5e38\u73b0\u5b9e\u7684\u51b3\u7b56\u70b9\uff09\u3011\n\u54c8\u4f5b\u8f6c\u5b66\u4e0d\u662f\u201c\u4f60\u5f88\u5f3a\u5c31\u884c\u201d\uff0c\u800c\u662f\u201c\u4f60\u5f3a + \u4f60\u7a00\u7f3a + \u4f60\u5fc5\u987b\u6765\u54c8\u4f5b + \u4f60\u4f1a\u5728\u54c8\u4f5b\u7acb\u523b\u4ea7\u751f\u5f71\u54cd\u201d\u3002 \n\u4ec5\u51ed transcript\uff1a\u4f60\u5c5e\u4e8e **\u201c\u5b66\u672f\u4e0a\u503c\u5f97\u8fdb\u5165 Second Read \u7684\u5019\u9009\u6c60\u201d**\uff0c\u4f46\u80fd\u5426\u8fdb committee \u53d6\u51b3\u4e8e\u4f60\u662f\u5426\u6709\u4ee5\u4e0b\u4efb\u4e00\u7c7b\u201c\u786c\u94a9\u5b50\u201d\uff1a\n- \u9876\u7ea7\u79d1\u7814\u4ea7\u51fa\uff08\u8bba\u6587/\u4e00\u4f5c/\u5f3a\u63a8\u8350\u4fe1/\u771f\u5b9e\u8d21\u732e\uff09\n- \u89c4\u6a21\u5316\u5f71\u54cd\uff08\u521b\u4e1a/\u4ea7\u54c1/\u5f00\u6e90\u9879\u76ee\u88ab\u5e7f\u6cdb\u4f7f\u7528\uff09\n- \u7ade\u8d5b/\u5956\u9879\uff08\u56fd\u9645/\u5168\u56fd\u7ea7\u786c\u5956\uff09\n- \u827a\u672f\u4e0e\u6280\u672f\u7ed3\u5408\u7684\u72ec\u7279\u4f5c\u54c1\u4e0e\u5f71\u54cd\u529b\uff08\u65e2\u8981\u201c\u4f5c\u54c1\u201d\uff0c\u4e5f\u8981\u201c\u88ab\u770b\u89c1/\u88ab\u91c7\u7528\u201d\u7684\u8bc1\u636e\uff09\n- \u6781\u5f3a\u3001\u6781\u5177\u4f53\u7684 Harvard-only \u7406\u7531\uff08\u5bfc\u5e08/\u5b9e\u9a8c\u5ba4/\u8de8\u5b66\u79d1\u751f\u6001 + \u4f60\u5df2\u5728\u505a\u7684\u4e8b\u53ea\u80fd\u5728\u54c8\u4f5b\u653e\u5927\uff09\n\n**\u6682\u5b9a\u7ed3\u8bba\uff1aHold for more info\uff08\u53ef\u63a8\u8fdb\u5230Second Read\u7684\u6f5c\u529b\uff09** \n\u4f46\u5982\u679c\u4f60\u7684\u6d3b\u52a8\u53ea\u662f\u201c\u4e0a\u4e86\u5f88\u591a\u786c\u8bfe + \u6821\u5185\u5e38\u89c4\u79d1\u7814/\u793e\u56e2\u201d\uff0c\u5728\u54c8\u4f5b\u8f6c\u5b66\u6c60\u91cc\u5f88\u53ef\u80fd\u4ecd\u7136\u4f1a\u88ab\u5237\u6389\u3002\n\n---\n\n# \u6211\u63a5\u4e0b\u6765\u9700\u8981\u4f60\u53d1\u7684\u6750\u6599\uff08\u51b3\u5b9a\u662f\u5426\u201cappealing\u201d\uff09\n\n\u4f60\u53ef\u4ee5\u6309\u6211\u4e0b\u9762\u683c\u5f0f\u56de\u4f20\uff08\u7cbe\u70bc\u5373\u53ef\uff09\uff1a\n\n## Part A\uff08\u8865\u9f50\u57fa\u672c\u4fe1\u606f\uff09\n1. \u4f60\u8ba1\u5212\u8f6c\u5165\u54c8\u4f5b\u7684\u5b66\u671f\uff1a**Fall 2026\uff1f**\uff08\u786e\u8ba4\uff09\n2. \u76ee\u524d\u5e74\u7ea7/\u5b66\u5206\u8fdb\u5ea6\uff1a\u4f60\u73b0\u5728\u770b\u8d77\u6765\u50cf\u5728\u8d70 **\u5927\u4e8c/\u5927\u4e09\u4ea4\u754c**\uff08\u786e\u8ba4\uff09\n3. \u4f60\u60f3\u5728\u54c8\u4f5b\u8bfb\u7684\u65b9\u5411\uff1a**CS\uff1fApplied Math\uff1fCognitive Neuroscience\uff1f\u8fd8\u662f Art/Film + CS \u8de8\u5b66\u79d1\uff1f**\n4. SAT/ACT\u3001\u6258\u798f\uff08\u5982\u9002\u7528\uff09\u4ee5\u53ca\u9ad8\u4e2d\u80cc\u666f\uff08\u662f\u5426\u7f8e\u9ad8/\u56fd\u9645\uff09\n\n## Part C\uff08\u6700\u5173\u952e\uff1a\u6d3b\u52a8\u4e0e\u6210\u5c31\uff0c\u6309\u5f71\u54cd\u529b\u6392\u5e8f\u7ed9\u62113\u20136\u6761\uff09\n\u6bcf\u6761\u7528\u8fd9\u4e2a\u6a21\u677f\uff1a\n- \u4f60\u505a\u4e86\u4ec0\u4e48\uff08\u4e00\u53e5\u8bdd\uff09\n- \u4f60\u7684\u89d2\u8272\uff08\u4f60\u662f\u4e0d\u662f\u6838\u5fc3\u8d21\u732e\u8005\uff09\n- \u53ef\u91cf\u5316\u7ed3\u679c\uff08\u6570\u636e/\u7528\u6237/\u5f15\u7528/\u90e8\u7f72/\u5956\u9879/\u5a92\u4f53/\u8d44\u91d1\uff09\n- \u8bc1\u636e\u8f7d\u4f53\uff08\u8bba\u6587\u3001GitHub\u3001\u6f14\u51fa/\u5c55\u89c8\u3001\u4ea7\u54c1\u94fe\u63a5\u7b49\uff09\n\n## Part D\uff08\u8f6c\u5b66\u539f\u56e0\uff1a\u7528\u201cpush + pull\u201d\u51993\u20136\u884c\uff09\n- UIUC\u54ea\u91cc\u65e0\u6cd5\u6ee1\u8db3\u4f60\uff08\u4e0d\u8981\u6cdb\u6cdb\uff09\n- \u54c8\u4f5b\u54ea\u4e9b\u5177\u4f53\u8d44\u6e90/\u4eba/\u9879\u76ee\u80fd\u628a\u4f60\u6b63\u5728\u505a\u7684\u4e8b\u653e\u5927\uff08\u8981\u70b9\u540d\u5230\u5b9e\u9a8c\u5ba4/\u8bfe\u7a0b\u4f53\u7cfb/\u8de8\u9662\u8d44\u6e90\uff09\n\n---\n\n\u5982\u679c\u4f60\u613f\u610f\uff0c\u6211\u4eec\u5c31\u6309\u771f\u5b9e\u5ba1\u6838\u8282\u594f\u6765\uff1a\u4f60\u53d1 **Part C** \u6211\u4f1a\u7acb\u523b\u7ed9\u51fa\u201c\u662f\u5426\u8db3\u591fappealing\u5230\u8fdb\u5165committee\u8ba8\u8bba\u201d\u7684\u5224\u65ad\uff0c\u4ee5\u53ca\u6211\u4f1a\u5728committee\u91cc\u5982\u4f55\u4e3a\u4f60\u8fa9\u62a4/\u522b\u4eba\u4f1a\u600e\u4e48\u53cd\u9a73\u3002"
},
{
"model": "google/gemini-3-pro-preview",
"response": "**Part A & B \u2014 \u6210\u7ee9\u4e0e\u8bfe\u7a0b\uff08\u5ba1\u6838\u65f6\u95f4\uff1a0\u20134\u5206\u949f\uff09**\n\n---\n\n### \u3010\u5185\u90e8\u72ec\u767d\uff1aAdmissions Officer #42\u3011\n\n**(0:15) \u626b\u89c6\u62ac\u5934\uff1a** Is this a joke? UIUC BFA in Theatre... Arts & Entertainment Technology. \u5f88\u597d\uff0c\u827a\u672f\u751f\uff0c\u901a\u5e38\u8fd9\u610f\u5473\u7740\u6211\u770b\u4e0d\u5230\u786c\u6838\u8bfe\u7a0b\uff0c\u53ef\u4ee5\u5feb\u901f\u8fc7\u2014\u2014\u7b49\u7b49\u3002\n\n**(0:45) \u773c\u795e\u505c\u987f\uff08Frowning\uff09\uff1a**\nWTF? \u4e0a\u4e2a\u5b66\u671f\uff08FA25\uff09\u4fee\u4e86 `CS 542 (Stat Reinforcement Learning)` \u548c `CS 546 (Adv Topics in NLP)`\uff1f\u8fd8\u662f A\uff1f\u8fd9\u662f\u4e00\u6240\u5168\u7f8e CS Top 5 \u5b66\u6821\u7684\u7814\u7a76\u751f/\u535a\u58eb\u751f\u8bfe\u7a0b\u3002\n\u518d\u770b\u4e00\u773c\u2014\u2014\u4f60\u8fd8\u662f BFA Theatre major\uff1f\u4f60\u6839\u672c\u6ca1\u6709\u8f6c\u4e13\u4e1a\uff1f\u8fd9\u975e\u5e38\u6709\u610f\u601d\u3002\u5927\u591a\u6570\u4eba\u60f3\u8f6c CS \u90fd\u662f\u4e3a\u4e86\u94b1\u6216\u8005\u5c31\u4e1a\uff0c\u4f46\u8fd9\u54e5\u4eec\u5728 Theatre \u4e13\u4e1a\u7684\u58f3\u5b50\u91cc\uff0c\u786c\u4fee\u4e86\u4e00\u5806 PhD \u7ea7\u522b\u7684 AI \u8bfe\u3002\n\n**(1:30) \u5bfb\u627e\u786c\u4f24\uff08The \"Dip\"\uff09\uff1a**\nAh, here it is. **Spring 2025.**\n`MATH 257 Linear Algebra`: **B**\n`CS 173 Discrete Structures`: **B+**\n`MATH 314`: **A-**\nTerm GPA 3.51\u3002\n\u5728\u54c8\u4f5b\u7684\u8f6c\u5b66\u6c60\u91cc\uff0c\u770b\u5230 B \u901a\u5e38\u662f\u4e00\u5f20\u5982\u679c\u4e0d\u89e3\u91ca\u6e05\u695a\u5c31\u76f4\u63a5\u9001\u53bb Reject pile \u7684\u5355\u7a0b\u7968\u3002\u5c24\u5176\u662f\u7ebf\u6027\u4ee3\u6570\u548c\u79bb\u6563\u6570\u5b66\uff0c\u8fd9\u662f CS \u7684\u57fa\u77f3\u3002\n**\u77db\u76fe\u70b9\u51fa\u73b0\u4e86\uff1a** \u4f60\u7684\u57fa\u7840\u8bfe\uff08\u5927\u4e00\u6570\u5b66/CS\uff09\u751a\u81f3\u6709\u70b9\u7a0d\u663e\u5e73\u5eb8\uff08B/B+\uff09\uff0c\u4f46\u4f60\u7684\u9ad8\u9636\u8bfe/\u7814\u7a76\u751f\u8bfe\uff08CS 542, CS 598\uff09\u5374\u662f A\uff1f\n\u8fd9\u901a\u5e38\u610f\u5473\u7740\u4e24\u4ef6\u4e8b\u4e4b\u4e00\uff1a\n1. \u4f60\u662f\u4e2a\u5929\u624d\uff0c\u89c9\u5f97\u57fa\u7840\u8bfe\u65e0\u804a\u6240\u4ee5\u6ca1\u5237\u5206\uff0c\u76f4\u63a5\u8df3\u53bb\u5b66\u96be\u7684\u3002\n2. \u4f60\u57fa\u7840\u4e0d\u7262\uff0c\u597d\u9ad8\u9a9b\u8fdc\uff0c\u7814\u7a76\u751f\u8bfe\u53ef\u80fd\u53ea\u662f Project \u505a\u5f97\u597d\uff0c\u4f46\u8fd9\u5728\u54c8\u4f5b\u7684\u9ad8\u538b\u6570\u5b66\u73af\u5883\u4e0b\u4f1a\u5d29\u76d8\u3002\n\n**(2:30) \u65af\u5766\u798f\u590f\u6821\uff08The Validation\uff09\uff1a**\n\u770b\u5230 Summer 2025 \u5728 Stanford \u7684\u5b66\u5206\u3002`CS 446 Machine Learning` \u62ff\u4e86 A\u3002\u597d\u5427\uff0c\u8fd9\u7a0d\u5fae\u7f13\u89e3\u4e86\u6211\u5bf9\u90a3\u4e2a \"B\" \u7684\u7126\u8651\u3002\u8bc1\u660e\u4f60\u5728\u9876\u7ea7\u73af\u5883\u4e5f\u80fd\u751f\u5b58\u3002\n\n**(3:30) \u73b0\u5728\u7684\u8bfe\u8868\uff08SP26 In Progress\uff09\uff1a**\n\u5168\u662f `CS 598` (Graph ML/Graph Algs)\u3002\u4f60\u57fa\u672c\u4e0a\u662f\u5728\u8bfb\u4e00\u4e2a AI \u535a\u58eb\uff0c\u987a\u4fbf\u62ff\u4e2a\u620f\u5267\u5b66\u4f4d\u3002\n\u4f60\u7684 GPA 3.77 \u5728\u54c8\u4f5b\u7533\u8bf7\u8005\u4e2d\u5904\u4e8e**\u540e 25%**\uff08\u901a\u5e38\u6211\u4eec\u8981 3.9+\uff09\u3002\u5982\u679c\u4e0d\u770b\u5177\u4f53\u7684\u8bfe\u540d\uff0c\u53ea\u770b\u6570\u5b57\uff0c\u673a\u5668\u521d\u7b5b\u53ef\u80fd\u5c31\u628a\u4f60\u6302\u4e86\u3002\n\u4f46\u8fd9\u8bfe\u8868\u592a\u75af\u72c2\u4e86\uff08Audacious\uff09\u3002\u8fd9\u79cd \"Arts-Humanities-Hardcore AI\" \u7684\u8de8\u5b66\u79d1\u7ec4\u5408\u6b63\u662f\u6211\u4eec\u559c\u6b22\u7684 Liberal Arts \u5473\u9053\uff0c\u4f46\u524d\u63d0\u662f\u4f60\u5fc5\u987b\u662f\u4e2a\u771f\u6b63\u7684\u5b66\u672f\u602a\u624d\uff0c\u800c\u4e0d\u662f\u53ea\u662f\u559c\u6b22\u5806\u8bfe\u3002\n\n---\n\n### \u3010Part A/B \u9636\u6bb5\u6027\u8bc4\u8bed\u3011\n\n**\u72b6\u6001\uff1a** **CAUTION / INTRIGUED (\u8b66\u60d5\u4f46\u611f\u5174\u8da3)**\n\n**\u4f18\u70b9 (Plus Factors)\uff1a**\n1. **\u6781\u5ea6\u6fc0\u8fdb\u7684\u9009\u8bfe (Extreme Rigor)\uff1a** \u5728 UIUC \u8fd9\u79cd CS \u5f3a\u6821\uff0c\u4f5c\u4e3a\u5927\u4e8c\u5b66\u751f\u4fee\u8bfb\u5927\u91cf 500-level (Graduate) \u8bfe\u7a0b\u4e14\u62ff A\uff0c\u8fd9\u975e\u5e38\u7f55\u89c1\u3002\u8fd9\u663e\u793a\u4e86\u6781\u5f3a\u7684 Intellectual Vitality\u3002\n2. **\u72ec\u7279\u7684\u8de8\u5b66\u79d1 Narrative\uff1a** Theatre/Linguistics + High-level AI\u3002\u8fd9\u4e0d\u4ec5\u4ec5\u662f \"CS Major\"\uff0c\u8fd9\u662f\u4e00\u4e2a\u6f5c\u5728\u7684 \"Digital Humanities\" \u6216 \"Creative AI\" \u7684\u72ec\u7279\u6545\u4e8b\u3002\n\n**\u7ea2\u65d7 (Red Flags)\uff1a**\n1. **\u6210\u7ee9\u5355\u4e0a\u7684\u6c61\u70b9 (Inconsistency)\uff1a** Spring 2025 \u7684 Linear Algebra (B) \u548c Discrete Structures (B+) \u662f**\u81f4\u547d\u4f24**\u3002\u54c8\u4f5b\u6781\u5176\u770b\u91cd\u57fa\u7840\u3002\u5982\u679c\u4f60\u7684 500-level \u8bfe\u7a0b\u662f Project-based\uff0c\u6211\u4f1a\u6000\u7591\u4f60\u7684\u6570\u5b66\u5e95\u5b50\u662f\u5426\u8db3\u4ee5\u652f\u6491\u54c8\u4f5b\u7684\u7406\u8bba CS \u8bfe\u7a0b\u3002\n2. **GPA 3.77\uff1a** \u6570\u5b57\u672c\u8eab\u592a\u4f4e\u3002\u4f60\u5fc5\u987b\u8bc1\u660e\u90a3\u4e9b \"B\" \u662f\u56e0\u4e3a\u4f60\u628a\u7cbe\u529b\u653e\u5728\u4e86\u6539\u53d8\u4e16\u754c\u7684\u79d1\u7814\u4e0a\uff0c\u800c\u4e0d\u662f\u56e0\u4e3a\u4f60\u80fd\u529b\u4e0d\u8db3\u3002\n\n**AO \u7684\u5fc3\u7406\u9884\u8bbe\uff1a**\n> \"\u8fd9\u4e2a\u7533\u8bf7\u4eba\u8981\u4e48\u662f\u4e2a\u57fa\u7840\u4e0d\u7262\u7684\u8d4c\u5f92\uff0c\u8981\u4e48\u662f\u4e2a\u4e0d\u5728\u4e4e\u5206\u6570\u7684\u771f\u6b63\u7684\u7814\u7a76\u578b\u4eba\u624d\u3002\u6211\u73b0\u5728**\u8feb\u5207\u9700\u8981\u770b Part C\uff08\u6d3b\u52a8\uff09**\u3002\u5982\u679c\u4ed6\u7684\u79d1\u7814/\u9879\u76ee\u6ca1\u6709\u8fbe\u5230\u9876\u7ea7\u6c34\u51c6\uff08\u6bd4\u5982\u53d1\u9876\u4f1a Paper\uff09\uff0c\u90a3\u4e48\u8fd9\u51e0\u4e2a B \u5c31\u8db3\u4ee5\u8ba9\u6211\u62d2\u6389\u4ed6\uff08\u56e0\u4e3a\u98ce\u9669\u592a\u5927\uff09\u3002\u4f46\u5982\u679c\u4ed6\u6709 NeurIPS/ICLR \u7ea7\u522b\u7684\u4ea7\u51fa\uff0c\u6211\u4f1a\u539f\u8c05\u90a3\u51e0\u4e2a B\u3002\"\n\n---\n\n**\u8bf7\u53d1\u9001 Part C\uff08\u6d3b\u52a8\u4e0e\u6210\u5c31\uff09\u3002**\n**Show me the evidence that justifies your grades.**"
},
{
"model": "anthropic/claude-opus-4.6",
"response": "\n\n# \u3010\u62db\u751f\u5b98\u5185\u90e8\u72ec\u767d \u2014 Part A \u521d\u5ba1\uff1a0\u20132\u5206\u949f\u3011\n\n---\n\n## \u7b2c\u4e00\u773c\u626b\u63cf\uff08\u524d30\u79d2\uff09\n\n*\u7ffb\u5f00\u6587\u4ef6\u5939\u3002UIUC\uff0cFine & Applied Arts\uff0cBFA Theatre\u2014\u2014\u7b49\u4e00\u4e0b\u3002*\n\n*...\u4f46\u8fd9\u6210\u7ee9\u5355\u51e0\u4e4e\u5168\u662f CS \u548c\u6570\u5b66\uff1f*\n\n*\u518d\u770b\u4e00\u773c\uff1aCS 542 Statistical Reinforcement Learning, CS 546 Advanced Topics in NLP, CS 598 Text Mining with LLM, LING 507 Formal Semantics...*\n\n**\u6211\u653e\u4e0b\u4e86\u5496\u5561\u676f\u3002**\n\n*\u8fd9\u4e2a\u4eba\u4ee5\u620f\u5267 BFA \u8eab\u4efd\u5165\u5b66\uff0c\u7136\u540e\u81ea\u5df1\u722c\u8fdb\u4e86\u7814\u7a76\u751f\u7ea7\u522b\u7684 CS \u548c\u8bed\u8a00\u5b66\u8bfe\u7a0b\uff1f\u800c\u4e14\u8fd8\u62ff\u4e86 A\uff1f\u8fd9\u4e0d\u662f\"\u987a\u4fbf\u4fee\u51e0\u95e8CS\"\uff0c\u8fd9\u662f\u4e00\u4e2a\u5728\u9662\u7cfb\u58c1\u5792\u4e0b\u9762\u786c\u751f\u751f\u51ff\u51fa\u6765\u7684\u81ea\u5efa\u8bfe\u7a0b\u4f53\u7cfb\u3002*\n\n---\n\n## \u9010\u9879\u6807\u6ce8\n\n### \ud83d\udccc \u57fa\u672c\u753b\u50cf\n- **\u5b66\u6821**\uff1aUIUC \u2014 Public flagship, T15 CS program, top-tier engineering. \u6211\u5bf9\u8fd9\u6240\u5b66\u6821\u7684CS\u8bfe\u7a0b\u96be\u5ea6\u975e\u5e38\u4e86\u89e3\u3002**\u8fd9\u4e0d\u662f\u6c34\u8bfe\u3002**\n- **\u540d\u4e49\u4e13\u4e1a**\uff1aBFA Arts & Entertainment Technology (Theatre Department)\n- **\u5b9e\u9645\u5b66\u672f\u8f68\u8ff9**\uff1aCS/ML/NLP + Computational Linguistics + Neuroscience\n- **\u5e74\u7ea7**\uff1a\u770b\u5b66\u5206\u91cf ~94 earned\uff0c\u5e94\u8be5\u662f\u5927\u4e8c\u7ed3\u675f/\u5927\u4e09\u521d\uff0c\u7533\u8bf7\u8f6c\u5165 Junior year\n\n### \ud83d\udccc \u6807\u5316/\u5148\u4fee\n- AP \u8f6c\u5165\uff1aCalc I & II, Bio, Psych, Stats \u2014 \u8bf4\u660e\u9ad8\u4e2d\u5b66\u672f\u57fa\u7840\u624e\u5b9e\n- SAT \u8c41\u514d\u4e86 RHET 105\uff08\u5199\u4f5c\uff09\u2014 \u5206\u6570\u5e94\u8be5\u4e0d\u4f4e\n- **\u6ca1\u6709\u770b\u5230 SAT/ACT \u5177\u4f53\u5206\u6570** \u2014 \u9700\u8981\u786e\u8ba4\uff0c\u4f46\u8f6c\u5b66\u7533\u8bf7\u4e2d\u6743\u91cd\u8f83\u4f4e\n\n### \ud83d\udccc GPA \u89e3\u8bfb\n\n| \u6307\u6807 | \u6570\u503c | \u6211\u7684\u53cd\u5e94 |\n|------|------|---------|\n| \u673a\u6784 GPA\uff08UIUC\uff09 | **3.74** | \u5728 UIUC CS \u7814\u7a76\u751f\u8bfe\u6df7\u51fa\u6765\u7684 3.74\uff0c\u542b\u91d1\u91cf\u4e0d\u540c\u4e8e\u4e00\u822c 3.74 |\n| \u8f6c\u5b66\u5b66\u5206 GPA | **4.00** | Stanford \u6691\u671f ML \u62ff A\uff0cData Structures \u62ff A |\n| Overall | **3.77** | \u7eb8\u9762\u4e0a\u6ca1\u6709 3.9+\uff0c\u8fd9\u662f\u4e2a\u98ce\u9669\u70b9\u2014\u2014\u540e\u9762\u7ec6\u8bf4 |\n\n### \ud83d\udccc \u8bfe\u7a0b\u8f68\u8ff9\uff08\u8fd9\u662f\u771f\u6b63\u8ba9\u6211\u505c\u4e0b\u6765\u7684\u90e8\u5206\uff09\n\n**Fall 2024\uff08\u7b2c\u4e00\u5b66\u671f\uff09\uff1a** CS 124 + Calc III + Theatre\u6838\u5fc3\u8bfe + **\u5ba1\u8ba1\u4e86\u4e00\u95e8\u7814\u7a76\u751f\u8bfe CS 591 Biologically Plausible AI**\n\n*\u5927\u4e00\u7b2c\u4e00\u5b66\u671f\u5c31\u53bb\u65c1\u542c\u7814\u7a76\u751f seminar\uff1f\u8fd9\u8bf4\u660e\u4ec0\u4e48\u2014\u2014\u8fd9\u4e2a\u4eba\u4ece Day 1 \u5c31\u77e5\u9053\u81ea\u5df1\u8981\u53bb\u54ea\u91cc\u3002*\n\n**Spring 2025\uff1a** CS 128, Discrete Structures, Linear Algebra, **MATH 314 Intro to Higher Math\uff08\u7eaf\u6570\u8bc1\u660e\u8bfe\uff09**, **NEUR 462 Integrative Neuroscience**, \u800c\u4e14\u7ee7\u7eed\u4e0a CS 591 \u5e76\u4e14\u8fd9\u6b21\u62ff\u4e86 S\uff08\u6b63\u5f0f\u6ce8\u518c\u4e86\uff09\n\n*\u4e00\u4e2a\u620f\u5267\u4e13\u4e1a\u7684\u5927\u4e00\u5b66\u751f\u5728\u4fee\u9ad8\u9636\u795e\u7ecf\u79d1\u5b66\u548c\u7eaf\u6570\u8bfe\u7a0b\u3002\u8fd9\u4e0d\u662f\u968f\u4fbf\u9009\u9009\u3002B+ \u548c B \u51fa\u73b0\u5728 Discrete \u548c Linear Algebra\u2014\u2014\u8bda\u5b9e\u8bf4\uff0c\u8fd9\u62c9\u4e86 GPA\uff0c\u4f46\u8fd9\u4e24\u95e8\u662f UIUC CS \u7684 weed-out \u8bfe\u3002*\n\n**Summer 2025\uff1a** Stanford CS 374 (Algorithms) CR, **Stanford CS 446 Machine Learning \u2014 A**\n\n*\u53bb Stanford \u6691\u671f\u4fee\u7814\u7a76\u751f\u7ea7 ML \u5e76\u62ff A\u3002\u597d\u7684\uff0c\u8fd9\u4e2a\u4eba\u662f\u8ba4\u771f\u7684\u3002*\n\n**Fall 2025\uff1a** \u5168\u90e8\u662f **500/600 \u7ea7\u522b\u8bfe\u7a0b**\u3002CS 542, CS 546, CS 598, LING 507\u3002\n\n*\u6211\u9700\u8981\u5f3a\u8c03\u4e00\u4e0b\uff1a\u8fd9\u662f\u4e00\u4e2a\u540d\u4e49\u4e0a\u7684 BFA \u620f\u5267\u5b66\u751f\uff0c\u5728\u5927\u4e8c\u79cb\u5b63\u540c\u65f6\u4fee\u4e86\u56db\u95e8\u7814\u7a76\u751f CS/NLP \u8bfe\u7a0b\u548c\u4e00\u95e8\u7814\u7a76\u751f\u5f62\u5f0f\u8bed\u4e49\u5b66\u3002CS 542 Statistical RL \u62ff A\uff0cCS 546 Advanced NLP \u62ff A\uff0cCS 598 Text Mining \u62ff A-\uff0cLING 507 \u62ff A-\u3002*\n\n**Spring 2026\uff08\u5f53\u524d\u8fdb\u884c\u4e2d\uff09\uff1a** CS 443 RL, CS 510 Advanced IR, \u4e24\u95e8 CS 598, LING 551 Pragmatics\n\n*\u4e94\u95e8\u8bfe\u91cc\u56db\u95e8\u662f\u7814\u7a76\u751f\u7ea7\u522b\u3002\u8fd9\u4e2a\u8bfe\u8868\u6211\u89c1\u8fc7 UIUC CS PhD \u5b66\u751f\u9009\u7684\u3002*\n\n### \ud83d\udccc \u8de8\u5b66\u79d1\u53d9\u4e8b\u7684\u8f6e\u5ed3\n\n\u6211\u5df2\u7ecf\u80fd\u770b\u5230\u4e00\u6761\u7ebf\u4e86\uff1a\n\n**Theatre (embodied performance / narrative) \u2192 Neuroscience (cognition / brain) \u2192 CS/NLP/RL (computational models of language and decision-making) \u2192 Linguistics (formal semantics, pragmatics)**\n\n\u8fd9\u4e0d\u662f\u4e71\u9009\u3002\u8fd9\u50cf\u662f\u4e00\u4e2a\u4eba\u5728\u95ee\uff1a\"**\u8bed\u8a00\u3001\u601d\u7ef4\u548c\u884c\u52a8\u7684\u8ba1\u7b97\u672c\u8d28\u662f\u4ec0\u4e48\uff1f**\"\u2014\u2014\u7136\u540e\u4ece\u56db\u4e2a\u4e0d\u540c\u5165\u53e3\u53bb\u903c\u8fd1\u540c\u4e00\u4e2a\u95ee\u9898\u3002\n\n---\n\n## \u3010\u5185\u90e8\u8bc4\u5206\u2014\u2014\u6682\u5b9a\u3011\n\n| \u7ef4\u5ea6 | \u6682\u5b9a\u8bc4\u5206 (1\u6700\u597d, 6\u6700\u5dee) | \u5907\u6ce8 |\n|------|------------------------|------|\n| **Academic Rating** | **2+** (\u63a5\u8fd12\uff0c\u6682\u672a\u7ed91) | GPA \u4e0d\u662f 3.95+\uff0c\u4f46\u8bfe\u7a0b\u96be\u5ea6\u548c\u8f68\u8ff9\u7684**\u52a0\u901f\u5ea6**\u6781\u4e3a\u7f55\u89c1\u3002\u4e00\u4e2a BFA \u5b66\u751f\u572818\u4e2a\u6708\u5185\u8fdb\u5165\u7814\u7a76\u751f CS/NLP \u5e76\u62ff\u5230 A\uff0c\u8fd9\u6bd4 4.0 \u7684\u666e\u901a\u8bfe\u8868\u66f4\u6709\u8bf4\u670d\u529b\u3002\u9700\u8981\u770b\u662f\u5426\u6709\u79d1\u7814\u4ea7\u51fa\u6765\u9501\u5b9a 1\u3002 |\n| **Intellectual Curiosity** | **1** | \u6beb\u65e0\u7591\u95ee\u3002\u5ba1\u8ba1\u7814\u7a76\u751f\u8bfe\u3001\u8de8\u6821\u6691\u671f\u3001\u81ea\u5efa\u8bfe\u7a0b\u4f53\u7cfb\u2014\u2014\u8fd9\u4e2a\u4eba\u4e0d\u9700\u8981\u4efb\u4f55\u4eba\u544a\u8bc9\u4ed6\u53bb\u5b66\u4ec0\u4e48\u3002 |\n| **\"\u4e3a\u4ec0\u4e48\u8f6c\u5b66\"\u5408\u7406\u6027** | **\u6682\u5b9a Strong** | BFA Theatre \u6846\u67b6\u663e\u7136\u5df2\u7ecf\u5bb9\u4e0d\u4e0b\u8fd9\u4e2a\u5b66\u751f\u4e86\u3002\u4f46\u6211\u9700\u8981\u786e\u8ba4\uff1aUIUC CS \u4e3a\u4ec0\u4e48\u4e0d\u76f4\u63a5 internal transfer\uff1f\u5982\u679c UIUC CS \u5185\u90e8\u8f6c\u5b66\u88ab\u62d2\u6216\u6709\u5236\u5ea6\u969c\u788d\uff0c\u8fd9\u5176\u5b9e\u52a0\u5f3a\u4e86\u53d9\u4e8b\u3002\u5982\u679c\u6ca1\u8bd5\u8fc7\uff0c\u8fd9\u662f\u4e2a\u6f5c\u5728\u5f31\u70b9\u3002 |\n\n---\n\n## \u3010\u521d\u6b65\u98ce\u9669\u70b9 \u2014 \u6211\u4f1a\u5728 Committee \u91cc\u88ab\u53cd\u65b9\u63d0\u51fa\u7684\u3011\n\n1. **GPA \u4e0d\u591f \"safe\"**\uff1a3.77 overall, 3.74 institutional\u3002\u54c8\u4f5b\u8f6c\u5b66 pool \u91cc 3.9+ \u662f\u5e38\u6001\u3002\u867d\u7136\u8bfe\u7a0b\u96be\u5ea6\u8fdc\u8d85\u5e38\u4eba\uff0c\u4f46\u6709\u7684\u540c\u4e8b\u4f1a\u76f4\u63a5\u7528 GPA \u7b5b\u3002\n2. **B+ / B \u5728\u57fa\u7840\u8bfe**\uff1aDiscrete Structures B+, Linear Algebra B \u2014 \u8fd9\u4e9b\u662f CS \u57fa\u7840\u8bfe\uff0c\u4f1a\u88ab\u8d28\u7591\"\u57fa\u7840\u662f\u5426\u624e\u5b9e\"\u3002\uff08\u6211\u7684\u53cd\u9a73\u662f\uff1a\u4ed6\u5728\u540e\u7eed\u7814\u7a76\u751f\u8bfe\u62ff\u4e86 A\uff0c\u8bf4\u660e\u5df2\u7ecf self-corrected\uff0c\u4f46\u5bf9\u65b9\u4f1a\u63d0\u3002\uff09\n3. **Theatre \u5b66\u5206\u600e\u4e48\u529e\uff1f** \u8f6c\u5165\u54c8\u4f5b\u4ec0\u4e48\u4e13\u4e1a\uff1fCS\uff1fLinguistics\uff1f\u5982\u679c\u662f CS\uff0c\u90a3\u4e9b Theatre \u5b66\u5206\u662f\u5426\u53ef\u8f6c\uff1f\u4ed6\u4f1a\u4e0d\u4f1a\u9700\u8981\u8d85\u8fc7\u4e24\u5e74\u624d\u80fd\u6bd5\u4e1a\uff1f\n4. **\"\u4e3a\u4ec0\u4e48\u4e0d\u53bb UIUC CS\uff1f\"** \u8fd9\u662f\u6700\u91cd\u8981\u7684\u95ee\u9898\u3002UIUC CS \u6392\u540d\u5168\u7f8e T5\u3002\u5982\u679c\u4f60\u5df2\u7ecf\u5728\u90a3\u91cc\u4fee\u7814\u7a76\u751f\u8bfe\u4e86\uff0c\u4e3a\u4ec0\u4e48\u9700\u8981\u54c8\u4f5b\uff1f\u8fd9\u4e2a\u95ee\u9898\u7b54\u4e0d\u597d\uff0c\u76f4\u63a5\u51fa\u5c40\u3002\n\n---\n\n## \u3010\u5f53\u524d\u5224\u65ad\u3011\n\n> **\u4e0d Reject\u3002\u7ee7\u7eed\u8bfb\u3002**\n>\n> \u8fd9\u4efd\u6210\u7ee9\u5355\u6709\u4e00\u4e2a\u975e\u5e38\u7f55\u89c1\u7684\u7279\u5f81\uff1a**\u52a0\u901f\u5ea6**\u3002\u4e0d\u662f\u8d77\u70b9\u9ad8\uff0c\u800c\u662f\u8f68\u8ff9\u7684\u659c\u7387\u9661\u5f97\u4e0d\u6b63\u5e38\u3002\u4ece CS 124 \u5230 CS 598 Text Mining with LLM \u7528\u4e86\u4e09\u4e2a\u5b66\u671f\uff0c\u8fd9\u4e0d\u662f\u6b63\u5e38\u4eba\u7c7b\u7684\u8bfe\u7a0b\u8fdb\u5ea6\u3002\u52a0\u4e0a\u8de8\u5b66\u79d1\u7684\u6709\u673a\u6574\u5408\uff08\u4e0d\u662f\u5806\u780c\uff09\uff0c\u8fd9\u8ba9\u6211\u60f3\u770b\u66f4\u591a\u3002\n>\n> \u4f46 GPA \u548c\"\u4e3a\u4ec0\u4e48\u4e0d\u7559 UIUC\"\u662f\u4e24\u4e2a\u5fc5\u987b\u88ab\u89e3\u51b3\u7684\u95ee\u9898\u3002\u5982\u679c\u6d3b\u52a8/\u79d1\u7814/\u6587\u4e66\u4e0d\u80fd\u628a\u8fd9\u4e24\u4e2a\u95ee\u9898\u5c01\u6b7b\uff0c\u8fd9\u4efd\u7533\u8bf7\u4f1a\u5728 Committee \u9636\u6bb5\u88ab\u5237\u3002\n\n---\n\n**\u8bf7\u53d1 Part B\uff08\u5982\u679c\u6210\u7ee9\u5355\u5df2\u7ecf\u6db5\u76d6\u4e86\u8bfe\u7a0b\u7ec6\u8282\uff0c\u53ef\u4ee5\u76f4\u63a5\u8df3\u5230 Part C \u2014 \u6d3b\u52a8\u4e0e\u6210\u5c31\uff09\u3002**\n\n\u6211\u73b0\u5728\u6700\u60f3\u770b\u7684\u662f\uff1a\n\n1. **\u6709\u6ca1\u6709\u79d1\u7814\u4ea7\u51fa**\uff08\u8bba\u6587/\u4f1a\u8bae/\u5b9e\u9a8c\u5ba4\uff09\u2014\u2014 \u5728\u8fd9\u4e2a\u7ea7\u522b\u7684\u8bfe\u7a0b\u57fa\u7840\u4e0a\uff0c\u5982\u679c\u6709 publication \u6216\u5b9e\u8d28\u6027\u7814\u7a76\uff0cAcademic Rating \u76f4\u63a5\u9501 1\n2. **Theatre/\u827a\u672f\u65b9\u9762\u6709\u6ca1\u6709\u5b9e\u8d28\u6027\u6210\u5c31** \u2014\u2014 \u5982\u679c\u6709\uff0c\u8de8\u5b66\u79d1\u53d9\u4e8b\u53d8\u6210\u6838\u6b66\u5668\uff1b\u5982\u679c\u6ca1\u6709\uff0cTheatre \u53d8\u6210\u7eaf\u7cb9\u7684\u5236\u5ea6\u969c\u788d\u80cc\u666f\n3. **\"\u4e3a\u4ec0\u4e48\u54c8\u4f5b\"\u7684\u5177\u4f53\u7b54\u6848**\n\n**\u8ba1\u65f6\u5668\u8fd8\u5728\u8dd1\u3002\u8bf7\u7ee7\u7eed\u3002**"
},
{
"model": "x-ai/grok-4",
"response": "# \u54c8\u4f5b\u8f6c\u5b66\u62db\u751f\u5b98\u6a21\u62df \u2014 15\u5206\u949f\u521d\u5ba1\uff08\u7ee7\u7eed\uff09\n\n**\u3010\u5185\u90e8\u72ec\u767d\u3011**\n\n*\uff08\u7ffb\u5f00\u8fd9\u4efd\u6210\u7ee9\u5355\uff0c\u7b2c\u4e00\u773c\u770b\u5230 UIUC \u2014\u2014 \u597d\u7684\u516c\u7acb\u7814\u7a76\u578b\u5927\u5b66\uff0cCS \u7a0b\u5e8f\u9876\u5c16\uff0c\u4f46\u54c8\u4f5b\u8f6c\u5b66\u7684\u95e8\u69db\u662f\u9876\u5c16\u4e2d\u7684\u9876\u5c16\u3002\u59d3\u540d Yuren Hao\uff0c\u751f\u65e5 4/26\u2014\u2014\u5927\u6982 19-20 \u5c81\u3002\u4e13\u4e1a\u662f Theatre with Arts & Entertainment Technology\uff1f\u4f46\u8bfe\u7a0b\u5217\u8868\u5168\u662f CS\u3001MATH\u3001LING\u3001NEUR\u2014\u2014\u8fd9\u4ec0\u4e48\u6df7\u642d\uff1f\u770b\u8d77\u6765\u50cf\u4e2a\u9690\u85cf\u7684 CS \u8f6c\u884c\u8005\uff0c\u5728\u827a\u672f\u5b66\u9662\u4e0b\u4fee CS \u8bfe\u3002\u8f6c\u5b66\u7533\u8bf7\u5982\u679c\u662f\u8981\u8f6c CS \u6216 AI\uff0c\u8fd9\u53ef\u80fd\u662f\u4e2a\u6709\u8da3\u7684\u94a9\u5b50\uff0c\u4f46\u9700\u8981\u770b\u6587\u4e66\u89e3\u91ca\u4e3a\u4ec0\u4e48\u4e0d\u662f\u76f4\u63a5\u5728 UIUC CS \u4e13\u4e1a\u3002\uff09*\n\n*\uff08\u5feb\u901f\u626b GPA\uff1a\u673a\u6784\u5185 3.74\uff0c\u6574\u4f53 3.77\u2014\u2014 \u624e\u5b9e\uff0c\u4f46\u54c8\u4f5b\u8f6c\u5b66\u751f\u5747\u503c\u5728 3.9+\uff0c\u5c24\u5176 CS \u65b9\u5411\u3002\u8f6c\u5b66\u6211\u4eec\u770b\u91cd\u8f68\u8ff9\uff1aFall 2024 \u9ad8\u5f00 3.92\uff0c\u7136\u540e Winter 3.67\u3001Spring 2025 3.51\u3001Fall 2025 \u56de\u5347 3.75\u2014\u2014\u6709\u6ce2\u52a8\uff0cB+ \u548c B \u5728 CS 173\u3001MATH 257\u3001CS 447 \u4e0a\uff0c\u4e0d\u7406\u60f3\uff0c\u5c24\u5176\u662f Discrete Structures \u662f CS \u57fa\u7840\uff0cB+ \u8ba9\u6211\u76b1\u7709\u3002Cumulative 3.74 \u4e0d\u662f elite\uff0c\u4f46\u5982\u679c\u6709\u4e0a\u5347\u8d8b\u52bf\uff0c\u80fd\u5fcd\u3002\uff09*\n\n*\uff08\u8bfe\u7a0b\u96be\u5ea6\uff1a\u54c7\uff0c\u8fd9\u8bfe\u8868\u91cd\u78c5\u2014\u2014 CS 124/128/173 \u5230 447/542/546/598\uff08NLP\u3001RL\u3001Text Mining with LLM\u3001Graph ML\uff09\uff0cMATH 220/231/241/257/314\uff0cSTAT 400\uff0cLING 507/551\uff0cNEUR 462\uff0c\u751a\u81f3 Stanford \u590f\u6821 CS 374/446\u3002\u8f6c\u5b66 CS \u7684\u7533\u8bf7\u4eba\uff0c\u9700\u8981\u8bc1\u660e\u80fd handle Harvard \u7684\u8bfe\u7a0b\u5f3a\u5ea6\u2014\u2014\u8fd9\u5bb6\u4f19\u5df2\u7ecf\u5728\u4fee grad-level CS\uff08CS 591/598/510\uff09\uff0c\u800c\u4e14 A/A- \u5c45\u591a\u3002AP/transfer credit \u4e5f\u591a\uff0c\u751f\u7269\u3001\u5fc3\u7406\u3001\u7edf\u8ba1\u2014\u2014\u5e7f\u5ea6\u597d\uff0c\u4f46\u6df1\u5ea6\u5728 AI/NLP \u4e0a\u96c6\u4e2d\u3002In progress: \u66f4\u591a\u9ad8\u7ea7 CS \u548c LING\u2014\u2014\u5982\u679c\u4fdd\u6301 A \u8f68\u8ff9\uff0c\u4f1a\u52a0\u5206\u3002\u6ca1\u6709\u64a4\u8bfe/\u6302\u79d1\uff0cGood Standing\u2014\u2014\u5e72\u51c0\u3002\uff09*\n\n*\uff08\u4eae\u70b9\uff1aStanford \u590f\u6821 A/A\uff0cParkland CS 225 A\u2014\u2014\u663e\u793a initiative \u53bb\u5916\u9762\u4fee\u8bfe\u3002CS 397 Individual Study A\uff0cCS 199 S\u2014\u2014\u53ef\u80fd\u6709\u7814\u7a76/\u9879\u76ee\u3002Theatre \u8bfe\u5168 A/A+\uff0c\u4f46\u53ea\u5360\u5c11\u6570\u2014\u2014\u8fd9 narrative \u5f97\u8fde\u8d2f\uff0c\u4e0d\u7136\u50cf\u5728\u827a\u672f\u58f3\u4e0b\u5077\u5077\u5b66 CS\u3002\uff09*\n\n*\uff08\u98ce\u9669\uff1aGPA \u6ce2\u52a8\uff0c\u51e0\u4e2a B/B+ \u5728\u6838\u5fc3\u8bfe\uff1b\u4e13\u4e1a\u4e0d\u5339\u914d\u2014\u2014\u5982\u679c\u7533\u8bf7\u54c8\u4f5b CS\uff0c\u4e3a\u4ec0\u4e48\u4e0d UIUC \u5185\u90e8\u8f6c\uff1f\u8f6c\u5b66\u7406\u7531\u5fc5\u987b\u5f3a\u3002\uff09\u65f6\u95f4\u7528\u4e86 3 \u5206\u949f\u2014\u2014\u6709\u70b9 appealing\uff0c\u8bfe\u7a0b\u5f3a\u5ea6\u8ba9\u6211\u591a\u770b\u4e86\u4e00\u773c\uff0c\u4f46 GPA \u4e0d\u662f\u9876\u5c16\uff0c\u8fd8\u6ca1\u5230 hold \u7684\u5730\u6b65\u3002\u7ee7\u7eed\u8bfb\uff0c\u4f46\u5982\u679c Part A \u6ca1\u5f3a\u5339\u914d\uff0c\u5bb9\u6613 reject\u3002*\n\n**\u3010\u5185\u90e8\u8bc4\u8bed\uff1aPart B \u7ed3\u8bba\u3011**\n\n**\u4f18\u70b9\uff1a** \u8bfe\u7a0b rigor \u5370\u8c61\u6df1\u523b\u2014\u2014 grad-level CS \u5728 UIUC/Stanford\uff0c\u4e13\u6ce8 AI/NLP/RL\uff0c\u8bc1\u660e\u4e86\u80fd handle \u54c8\u4f5b\u7684 CS \u8bfe\u7a0b\u3002\u8f68\u8ff9\u4ece\u57fa\u7840\u5230\u9ad8\u7ea7\uff0c\u6709\u5e7f\u5ea6\uff08Math/Neur/Ling\uff09\u3002Theatre \u80cc\u666f\u53ef\u80fd\u5e26\u6765\u72ec\u7279 interdisciplinary \u89d2\u5ea6\uff0c\u5982\u679c\u6587\u4e66\u73a9\u5f97\u597d\u3002\n\n**\u98ce\u9669\u70b9\uff1a** GPA 3.74-3.77 \u4e2d\u7b49\u504f\u4e0a\uff0c\u6709 downward dip \u5728 Spring 2025\uff083.51\uff09\uff0cB/B+ \u5728 Discrete/Math \u8bfe\u663e\u793a\u5f31\u70b9\u3002\u4e13\u4e1a mismatch \u9700\u8981\u89e3\u91ca\u2014\u2014\u4e3a\u4ec0\u4e48 Theatre \u4e0b\u4fee\u8fd9\u4e48\u591a CS\uff1f\u6574\u4f53\u5b66\u672f\u5f3a\u5ea6\u597d\uff0c\u4f46\u4e0d extraordinary\uff08\u6ca1\u89c1\u9876\u520a\u8bba\u6587\u6216\u7ade\u8d5b\uff09\u3002\n\n**\u662f\u5426\u7ee7\u7eed\u8bfb\uff1a** \u503c\u5f97 proceed \u5230 Part C\uff0c\u4f46\u65f6\u95f4\u7d27\uff08\u5df2\u7528 ~3/15 \u5206\u949f\uff09\u3002\u8fd9\u4e0d\u662f slam-dunk\uff1b\u9700\u8981\u5f3a\u6d3b\u52a8\u6765 boost\u3002**\u8bf7\u5148\u53d1 Part A\uff08\u57fa\u672c\u4fe1\u606f\uff09\uff0c\u6211\u9700\u8981\u5b83\u6765 contextualize\uff0c\u7136\u540e\u53d1 Part C\u3002** \u5982\u679c\u4f60\u8df3\u8fc7\u4e86\uff0c\u6211\u4f1a\u57fa\u4e8e\u6210\u7ee9\u5355\u63a8\u65ad\u4e00\u4e9b\uff0c\u4f46\u5b8c\u6574\u6027\u91cd\u8981\u3002\u5f00\u59cb\u8865\u53d1\u3002"
}
],
"stage2": [
{
"model": "openai/gpt-5.2",
"ranking": "Response A does well:\n- Clear, admissions-style structure mapped to the stated \u201c15-minute\u201d workflow (0\u20132, 2\u20138, 8\u201315), which matches the prompt\u2019s intended simulation.\n- Grounded transcript read: identifies rigor signals (UIUC, grad-level CS, Stanford summer), trajectory, and specific \u201crisk\u201d items (B/B+, CR/Pass) with plausible admissions concerns.\n- Appropriately conditional conclusion: \u201cworth second read\u201d but needs Part C / Harvard-specific rationale\u2014this is realistic for transfer review.\n- Tone is professional and readable; no distracting theatrics.\n\nResponse A does poorly:\n- Overstates how negatively CR/Pass is \u201cdefault interpreted\u201d without noting common institutional pass/credit policies (e.g., some programs force CR, summer policies, etc.). It *does* ask for clarification, but the framing is a bit one-directional.\n- Uses a few admissions-generalities (e.g., \u201c~1%\u201d) without acknowledging year-to-year variability; minor issue.\n\n---\n\nResponse B does well:\n- Captures a key evaluative tension very well: weaker grades in foundational math/CS (linear algebra/discrete) versus strong performance in high-level/grad courses\u2014this is exactly the kind of inconsistency an AO might flag and probe.\n- Strong emphasis on \u201cPart C must justify the risk,\u201d which aligns with transfer reality: transcript alone rarely carries committee for Harvard-level transfer.\n- The skepticism about why a student at UIUC CS strength needs Harvard is implied (though not developed much).\n\nResponse B does poorly:\n- Tone is unprofessional and implausible for an AO write-up: \u201cIs this a joke?\u201d, \u201cWTF?\u201d, \u201c\u8fd9\u54e5\u4eec\u2026\u201d. Even in an \u201cinternal monologue\u201d simulation, it reads like internet commentary rather than an admissions office memo.\n- Makes some overly rigid/possibly incorrect claims: calling B \u201c\u81f4\u547d\u4f24\u201d / \u201c\u5355\u7a0b\u7968\u201d is exaggerated; Harvard transfer is ultra-competitive, but a B is not automatically fatal if the rest is exceptional.\n- The \u201cGPA in\u540e25%\u201d assertion is presented as fact without grounding; could be true in some pools, but it\u2019s speculative and stated too confidently.\n- Less structured coverage: doesn\u2019t clearly summarize the overall academic record (e.g., earned hours, trajectory beyond the one dip) as cleanly as A/C.\n\n---\n\nResponse C does well:\n- Most comprehensive and transcript-faithful analysis: accurately surfaces the unusual profile (BFA label + heavy CS/Math/Ling/Neur), course progression, and \u201cacceleration\u201d narrative.\n- Strong committee-style risk framing: explicitly lists what \u201cthe opposing voice\u201d would raise (GPA, foundational B/B+, credit transfer/graduation time, \u201cwhy not UIUC CS?\u201d). That\u2019s very close to how committee deliberations sound.\n- Provides a plausible preliminary rating schema (1\u20136) and uses it cautiously (\u201c2+ not 1 yet\u201d), which feels realistic.\n- Connects coursework into a coherent interdisciplinary story (Theatre \u2192 cognition/neuro \u2192 NLP/RL \u2192 semantics/pragmatics), which is what would make this applicant \u201cstick\u201d in a fast read.\n\nResponse C does poorly:\n- Uses emojis and a bit of dramatization (\u201c\u653e\u4e0b\u5496\u5561\u676f\u201d), which may be stylistically off for the requested simulation; it can distract even if the content is strong.\n- A few claims are speculative (e.g., SAT \u201c\u5e94\u8be5\u4e0d\u4f4e,\u201d junior-year inference) though it labels some as needing confirmation; still, it occasionally asserts with more confidence than warranted.\n- Slightly long for a \u201c15-minute\u201d constraint, though the prompt doesn\u2019t strictly limit response length\u2014just the simulated review time.\n\n---\n\nResponse D does well:\n- Generally realistic skepticism and recognizes the core issues (rigor vs GPA; mismatch between listed major and course-taking; \u201cwhy Harvard vs UIUC CS?\u201d).\n- Concise compared to C, and it stays closer to a plain internal note style than B.\n\nResponse D does poorly:\n- Less organized and less decisive: it says \u201cnot yet to hold\u201d / \u201c\u7ee7\u7eed\u8bfb\u201d in a way that feels internally inconsistent about what bucket the file goes into.\n- Adds some shaky inferences (age estimate from birth date; \u201c\u6ca1\u89c1\u9876\u520a\u8bba\u6587\u6216\u7ade\u8d5b\u201d when those materials simply aren\u2019t provided\u2014fine to say \u201cnot shown here,\u201d but it reads like a negative deduction).\n- Offers fewer actionable next-step requests than A/C and less insight into what would move the file to committee.\n\nFINAL RANKING:\n1. Response C\n2. Response A\n3. Response D\n4. Response B",
"parsed_ranking": [
"Response C",
"Response A",
"Response D",
"Response B"
]
},
{
"model": "google/gemini-3-pro-preview",
"ranking": "Response C is the most impressive simulation because it goes beyond merely reading the data to synthesizing a coherent intellectual narrative. It catches subtle details (like acting as an auditor for a graduate-level AI course in the first freshman semester) to deduce \"Intellectual Curiosity\" ratings, which is exactly how elite admissions officers function. It builds a hypothesis connecting Theatre, Neuroscience, and Linguistics, offering a sophisticated reason to keep the applicant in the pool despite a lower GPA.\n\nResponse B is also excellent, vividly capturing the \"internal monologue\" and skepticism of a Harvard AO. It reacts viscerally to the anomaly of a Theatre major taking PhD-level CS courses and correctly identifies the specific risks (the \"B\" grades in foundational math). It creates a high level of engagement and accurately simulates the \"search for a reason to reject\" mindset mentioned in the prompt.\n\nResponse A is a competent and professional simulation. It breaks down the time management well (0-2 mins, 2-8 mins) and acts logically. However, it feels slightly more generic compared to the narrative detective work in C and the tonal accuracy of B. It treats the process a bit more like a checklist than a holistic review.\n\nResponse D is the weakest. While it analyzes the grades well, it ends by asking the user to \"First send Part A (Basic Info)... to contextualize,\" despite the fact that the provided transcript header clearly contained the Name, Major, College, and Birth Date. This suggests the model didn't process the input context as thoroughly as the others.\n\nFINAL RANKING:\n1. Response C\n2. Response B\n3. Response A\n4. Response D",
"parsed_ranking": [
"Response C",
"Response B",
"Response A",
"Response D"
]
},
{
"model": "anthropic/claude-opus-4.6",
"ranking": "## Evaluation of Each Response\n\n### Response A\n\n**What it does well:**\n- Provides a clear, structured framework for the 15-minute review process\n- Correctly identifies the key tension: BFA Theatre major taking hardcore CS/ML/NLP courses\n- Accurately flags the GPA concern (3.74/3.77) as below typical Harvard transfer thresholds\n- Notes the B/B+ grades in foundational courses (CS 173, MATH 257) as risk points\n- Raises the important question about CR/Pass courses and how they'll be read\n- Identifies the GPA trajectory (3.92 \u2192 3.51 \u2192 3.75) correctly\n- Provides a reasonable provisional conclusion: \"Hold for more info\"\n- Gives a clear template for what to send next\n\n**What it does poorly:**\n- The \"internal monologue\" feels somewhat mechanical and checklist-driven rather than truly simulating an AO's visceral reaction\n- Doesn't fully appreciate the extraordinary nature of the course trajectory \u2014 a BFA student taking PhD-level courses within 3 semesters gets somewhat understated treatment\n- Misses the deeper narrative thread connecting Theatre \u2192 Neuroscience \u2192 NLP \u2192 Linguistics \u2192 RL\n- Doesn't notice or comment on the CS 591 audit in Fall 2024 (first semester!) which is a powerful signal\n- The Stanford summer gets somewhat surface-level treatment\n- The tone is competent but lacks the emotional punch that would reflect a genuinely surprised/intrigued admissions officer\n- Doesn't provide preliminary academic ratings or scores\n\n---\n\n### Response B\n\n**What it does well:**\n- The opening reaction (\"Is this a joke?\") captures the genuine surprise an AO would feel upon seeing this mismatch between major and coursework\n- Excellent dramatic pacing \u2014 the internal monologue reads authentically like someone reacting in real time\n- Correctly identifies the central paradox: basic courses got B/B+ but graduate courses got A\n- Articulates the two possible interpretations of this pattern clearly (\"genius who found basics boring\" vs. \"shaky foundations, inflated grad grades\")\n- The Stanford validation point is well-placed and correctly interpreted\n- The line \"you're basically getting a PhD in AI while picking up a theatre degree\" is a sharp, memorable characterization\n- Honest about GPA being in the \"bottom 25%\" of Harvard transfer applicants\n- The final psychological framing (\"Show me the evidence that justifies your grades\") is exactly what an AO would be thinking\n\n**What it does poorly:**\n- Calls the B in Linear Algebra and B+ in Discrete Structures a \"\u81f4\u547d\u4f24\" (fatal wound) \u2014 this is somewhat overblown given the overall context and subsequent A's in much harder courses\n- Slightly overdramatic in tone at points \u2014 while engaging, it occasionally veers into entertainment rather than rigorous assessment\n- Doesn't build the cross-disciplinary narrative thread (Theatre \u2192 Neuro \u2192 Linguistics \u2192 NLP) \u2014 treats Theatre mostly as background noise rather than potential narrative strength\n- Doesn't mention the CS 591 audit in the first semester, which is a key signal of intellectual initiative\n- Doesn't provide formal academic ratings\n- The mention of \"NeurIPS/ICLR\" as the bar is somewhat arbitrary \u2014 there are many ways to demonstrate research impact\n- Less structured than some other responses in terms of next steps\n\n---\n\n### Response C\n\n**What it does well:**\n- **This is the standout response.** The internal monologue is exceptionally authentic \u2014 \"I put down my coffee cup\" is a perfect detail showing genuine surprise\n- Catches the CS 591 audit in Fall 2024 (first semester!) which most other responses miss \u2014 correctly identifies this as a Day 1 signal of intentionality\n- Builds the most compelling intellectual narrative: \"language, thought, and action \u2014 what is their computational nature?\" \u2014 this is exactly the kind of synthesis an impressed AO would articulate\n- Provides formal academic ratings (2+ Academic, 1 for Intellectual Curiosity) with nuanced justification\n- The table format for GPA interpretation with the column \"\u6211\u7684\u53cd\u5e94\" is clean and effective\n- Correctly identifies the \"acceleration\" as the key feature: \"not high starting point, but abnormal slope of trajectory\"\n- Anticipates committee counterarguments with specific rebuttals (e.g., \"he self-corrected \u2014 the grad A's prove it\")\n- Raises the critical strategic question: \"Why not internal transfer to UIUC CS?\" \u2014 and correctly notes that if there are institutional barriers, this actually *strengthens* the narrative\n- The semester-by-semester walkthrough demonstrates genuine close reading\n- Correctly notes that Theatre achievement (or lack thereof) determines whether the cross-disciplinary story becomes a \"nuclear weapon\" or just institutional background noise\n- The line about the Spring 2026 courseload (\"I've seen UIUC CS PhD students take this\") provides institutional context that adds credibility\n\n**What it does poorly:**\n- Slightly long \u2014 a real AO might not write this much in internal notes during a 15-minute review, though for simulation purposes this serves the user well\n- Could be more explicit about how the 3.77 GPA compares statistically to the admitted transfer pool\n- The risk point about Theatre credits and graduation timeline is somewhat buried\n- Minor: the \"Part B\" redirect at the end is slightly confusing since the transcript already contains all course info (though the response self-corrects by saying \"if the transcript already covers it, skip to Part C\")\n\n---\n\n### Response D\n\n**What it does well:**\n- Covers the basic analytical points: GPA concerns, course rigor, trajectory analysis\n- Correctly identifies the GPA dip in Spring 2025\n- Notes the Stanford summer and external coursework as showing initiative\n- Mentions CS 397 Individual Study as potentially indicating research\n- Honest about the assessment: \"not a slam-dunk\"\n\n**What it does poorly:**\n- The internal monologue feels the most generic and least distinctive of all four responses\n- Reads more like a summary than a genuine real-time reaction \u2014 lacks the \"stopped in my tracks\" moment that this extraordinary transcript should provoke\n- Doesn't build the cross-disciplinary narrative at all \u2014 treats the Theatre/Neuro/Ling/CS combination as a \"mismatch\" to be explained rather than potentially the applicant's greatest strength\n- Misses the CS 591 audit in Fall 2024\n- The parenthetical asides are dense and hard to follow \u2014 the formatting doesn't serve the simulation well\n- Doesn't provide academic ratings\n- Calls CS 447 NLP a B+ \u2014 this is correct \u2014 but lumps it with foundational courses without noting that CS 542/546/598 (harder courses in the same domain) got A/A/A-\n- The conclusion (\"\u8bf7\u5148\u53d1 Part A\") is confusing since the transcript contains most of Part A information already, and this creates an unnecessary procedural bottleneck\n- Underestimates how unusual this application is \u2014 the tone suggests \"interesting but not compelling\" when the course trajectory is genuinely extraordinary by any standard\n- Shortest and least developed of all responses\n\n---\n\n## Summary Comparison\n\nResponse C demonstrates the deepest reading of the transcript, the most authentic AO voice, the strongest narrative synthesis, and the most useful strategic framing for the applicant. Response B is engaging and captures the surprise well but misses some key details and over-dramatizes the B grades. Response A is competent and well-structured but lacks the visceral reaction this transcript warrants. Response D is the weakest \u2014 generic, procedurally confused, and fails to appreciate the extraordinary nature of what's in front of it.\n\nFINAL RANKING:\n1. Response C\n2. Response B\n3. Response A\n4. Response D",
"parsed_ranking": [
"Response C",
"Response B",
"Response A",
"Response D"
]
},
{
"model": "x-ai/grok-4",
"ranking": "### Evaluation of Response A\n**What it does well:** This response stays true to the initial simulation structure, breaking down the review into timed segments (0-2 min, 2-8 min, 8-15 min) with internal monologues that feel authentic to a rushed admissions officer. It effectively highlights strong signals like course rigor and cross-disciplinary elements, while pointing out risks (e.g., GPA, CR grades) in a balanced, realistic way. It provides a clear tentative conclusion (\"Hold for more info\") and smartly pushes for specific next parts (A, C, D), emphasizing what could make it \"appealing.\" The focus on Harvard-specific transfer logic (e.g., rarity, hooks needed) adds depth and aligns with the original prompt's mindset.\n\n**What it does poorly:** It assumes some Part A details without explicitly requesting confirmation early, and the analysis feels a bit high-level without as much granular breakdown of the transcript (e.g., fewer specific course callouts compared to others). It could be more critical on the GPA trajectory's fluctuations, and the response ends by requiring multiple parts at once, which might disrupt the \"\u9010\u90e8\u5206\" flow.\n\n### Evaluation of Response B\n**What it does well:** The response uses a vivid, timestamped internal monologue that makes the review feel dynamic and personal, effectively capturing intrigue (e.g., the \"WTF?\" reaction to the major mismatch) and validation from Stanford credits. It balances positives (extreme rigor, unique narrative) with strong red flags (GPA dips, B grades in foundations), creating a sense of tension. The \"psychological preset\" at the end is a nice touch, tying into what the officer needs next (Part C), and it demands evidence to justify grades, pushing the simulation forward.\n\n**What it does poorly:** The tone is overly dramatic and speculative (e.g., \"genius or gambler,\" \"fatal wound\"), which might come across as biased or less professional than a real admissions officer's impartiality. It combines Part A and B prematurely, potentially skipping the sequential flow, and doesn't provide a structured scoring or table for clarity. The GPA critique is harsh without enough counterbalance, and it lacks a clear overall tentative rating like \"hold\" or \"reject.\"\n\n### Evaluation of Response C\n**What it does well:** This is highly structured with tables, annotations, and internal ratings (e.g., Academic Rating 2+, Intellectual Curiosity 1), making it easy to follow and deeply analytical. It excels at tracing the \"acceleration\" in the academic trajectory, connecting disciplines into a cohesive narrative (e.g., Theatre to AI via neuroscience), and addressing risks head-on with potential rebuttals. The response identifies key questions (e.g., \"why not internal transfer?\") and prioritizes what to see next (research outputs, Theatre achievements), aligning perfectly with the simulation's goals. It feels comprehensive and evidence-based, using the transcript details effectively.\n\n**What it does poorly:** It's quite long and detailed, which might exceed the \"15-minute\" rushed feel\u2014potentially overanalyzing for an initial scan. It assumes some Part A elements (e.g., year level) without confirming, and while it suggests proceeding, it doesn't explicitly tie back to timed segments as closely as others. The focus on positives could be seen as slightly optimistic without more emphasis on the low admission odds.\n\n### Evaluation of Response D\n**What it does well:** It maintains a concise, time-conscious internal monologue that mirrors a quick scan, noting key elements like GPA fluctuations, course rigor, and major mismatch efficiently. The pros/cons summary is balanced and clear, highlighting interdisciplinary potential while flagging risks (e.g., why not internal transfer). It adheres to the simulation by tracking time used (~3/15 minutes) and requests missing parts logically (Part A then C), keeping the flow intact.\n\n**What it does poorly:** The analysis is somewhat surface-level compared to others, with less depth on specific courses or trajectory (e.g., no tables or detailed breakdowns). It doesn't use internal ratings or monologues as vividly, and the conclusion feels tentative without a strong \"hold/reject\" lean. It overlooks some transcript nuances (e.g., audit courses, CR grades) and could push harder on what makes it \"appealing\" beyond proceeding.\n\nFINAL RANKING:\n1. Response C\n2. Response A\n3. Response D\n4. Response B",
"parsed_ranking": [
"Response C",
"Response A",
"Response D",
"Response B"
]
}
],
"stage3": {
"model": "anthropic/claude-opus-4.6",
"response": "\n\n# \u54c8\u4f5b\u8f6c\u5b66\u62db\u751f\u5b98\u6a21\u62df \u2014 15\u5206\u949f\u521d\u5ba1\uff08\u6210\u7ee9\u5355\u5ba1\u9605\uff09\n\n---\n\n## \u30100:00\u20130:30 \u2014 \u7ffb\u5f00\u6587\u4ef6\u5939\uff0c\u7b2c\u4e00\u773c\u626b\u63cf\u3011\n\n**\u5185\u90e8\u72ec\u767d\uff1a**\n\n*UIUC \u2014 \u5f88\u597d\u7684\u516c\u7acb\u65d7\u8230\uff0cCS \u5168\u7f8e Top 5\u3002\u4e13\u4e1a\u662f... BFA Theatre, Arts & Entertainment Technology\uff1f*\n\n*...\u4f46\u8fd9\u8bfe\u8868\u51e0\u4e4e\u5168\u662f CS\u3001MATH\u3001LING\u3001NEUR\uff1f*\n\n*\u7b49\u4e00\u4e0b\u3002CS 542 Statistical Reinforcement Learning, CS 546 Advanced Topics in NLP, CS 598 Text Mining with LLM, LING 507 Formal Semantics...*\n\n***\u6211\u653e\u4e0b\u4e86\u624b\u91cc\u7684\u7b14\u3002***\n\n*\u8fd9\u4e2a\u4eba\u4ee5\u620f\u5267 BFA \u8eab\u4efd\u5165\u5b66\uff0c\u7136\u540e\u81ea\u5df1\u722c\u8fdb\u4e86\u7814\u7a76\u751f\u7ea7\u522b\u7684 CS \u548c\u8bed\u8a00\u5b66\u8bfe\u7a0b\uff1f\u8fd9\u4e0d\u662f\"\u987a\u4fbf\u4fee\u51e0\u95e8 CS\"\u2014\u2014\u8fd9\u662f\u4e00\u4e2a\u5728\u9662\u7cfb\u58c1\u5792\u4e0b\u786c\u751f\u751f\u51ff\u51fa\u6765\u7684\u81ea\u5efa\u8bfe\u7a0b\u4f53\u7cfb\u3002\u503c\u5f97\u7ee7\u7eed\u8bfb\u3002*\n\n---\n\n## \u30100:30\u20132:00 \u2014 \u5feb\u901f\u4fe1\u53f7\u626b\u63cf\uff1a\"\u503c\u4e0d\u503c\u5f97\u7ee7\u7eed\u770b\uff1f\"\u3011\n\n### \u57fa\u672c\u753b\u50cf\n\n| \u9879\u76ee | \u5185\u5bb9 |\n|------|------|\n| **\u5b66\u6821** | UIUC \u2014 Public flagship, CS \u5168\u7f8e T5\uff0c\u8bfe\u7a0b\u96be\u5ea6\u6211\u975e\u5e38\u4e86\u89e3 |\n| **\u540d\u4e49\u4e13\u4e1a** | BFA Arts & Entertainment Technology (Theatre Department) |\n| **\u5b9e\u9645\u5b66\u672f\u8f68\u8ff9** | CS / ML / NLP / RL + Computational Linguistics + Neuroscience |\n| **\u5b66\u5206\u8fdb\u5ea6** | ~94 earned hours \u2192 \u5927\u4e8c\u672b/\u5927\u4e09\u521d\uff0c\u7533\u8bf7\u8f6c\u5165 Junior year |\n| **\u5148\u4fee/AP** | Calc I & II, Bio, Psych, Stats \u5168\u90e8 pass\uff1bSAT \u8c41\u514d\u5199\u4f5c\u8bfe |\n\n### GPA \u7b2c\u4e00\u5370\u8c61\n\n| \u6307\u6807 | \u6570\u503c | \u5373\u65f6\u53cd\u5e94 |\n|------|------|---------|\n| \u673a\u6784 GPA (UIUC) | **3.74** | \u5728 UIUC \u7814\u7a76\u751f CS \u8bfe\u91cc\u78e8\u51fa\u6765\u7684 3.74\uff0c\u542b\u91d1\u91cf\u4e0d\u540c\u4e8e\u4e00\u822c 3.74 |\n| \u8f6c\u5b66\u5b66\u5206 GPA | **4.00** | Stanford \u6691\u671f ML \u62ff A\uff0cParkland DS \u62ff A |\n| Overall | **3.77** | \u7eb8\u9762\u4e0a\u4e0d\u662f 3.9+\uff0c\u8fd9\u662f\u4e00\u4e2a\u98ce\u9669\u70b9\u2014\u2014\u4f46\u9700\u8981\u7ed3\u5408\u8bfe\u7a0b\u5f3a\u5ea6\u5224\u65ad |\n\n> **\u521d\u6b65\u5224\u65ad\uff1a\u4e0d\u6254\u8fdb Reject \u5806\u3002\u8fd9\u4e2a major-\u8bfe\u8868\u7684\u9519\u4f4d\u592a\u4e0d\u5bfb\u5e38\u4e86\uff0c\u6211\u8981\u5f80\u4e0b\u770b\u3002**\n\n---\n\n## \u30102:00\u20138:00 \u2014 \u9010\u5b66\u671f\u7ec6\u8bfb\uff0c\u9a8c\u8bc1\u786c\u5b9e\u529b\u4e0e\u4e00\u81f4\u6027\u3011\n\n### Fall 2024\uff08\u7b2c\u4e00\u5b66\u671f\uff09\u2014 GPA 3.92\n\n| \u8bfe\u7a0b | \u6210\u7ee9 | \u5185\u90e8\u6279\u6ce8 |\n|------|------|---------|\n| CS 124 Intro CS I | A | \u57fa\u7840\u624e\u5b9e |\n| CS 397 Individual Study | A | \u7b2c\u4e00\u5b66\u671f\u5c31\u6709\u4e2a\u4eba\u7814\u7a76\u9879\u76ee\uff1f\u60f3\u77e5\u9053\u5185\u5bb9 |\n| **CS 591 Biologically Plausible AI** | **AU (\u65c1\u542c)** | \u26a1 **\u7b2c\u4e00\u5b66\u671f\u5c31\u53bb\u65c1\u542c\u7814\u7a76\u751f seminar\uff1f** \u5927\u90e8\u5206\u65b0\u751f\u8fd8\u5728\u9002\u5e94\u5927\u5b66\u751f\u6d3b\uff0c\u8fd9\u4e2a\u4eba Day 1 \u5c31\u77e5\u9053\u8981\u53bb\u54ea\u91cc |\n| MATH 241 Calculus III | A- | \u53ef\u4ee5 |\n| Theatre \u8bfe\u7a0b (4\u95e8) | A/A+/A+/A | \u672c\u4e13\u4e1a\u5168\u4f18 |\n\n**\u5185\u90e8\u72ec\u767d\uff1a** *\u5f00\u5c40\u5f3a\u52bf\u3002\u6700\u8ba9\u6211\u6ce8\u610f\u7684\u662f CS 591 \u65c1\u542c\u2014\u2014\u8fd9\u8bf4\u660e\u4e00\u79cd\u81ea\u9a71\u7684\u5b66\u672f\u9965\u6e34\u611f\uff0c\u4e0d\u662f\u5236\u5ea6\u5b89\u6392\u7684\uff0c\u800c\u662f\u81ea\u5df1\u627e\u4e0a\u53bb\u7684\u3002*\n\n### Winter 2024\u20132025 \u2014 GPA 3.67\n\n| \u8bfe\u7a0b | \u6210\u7ee9 | \u5185\u90e8\u6279\u6ce8 |\n|------|------|---------|\n| PSYC 204 Intro Brain & Cognition | A- | \u5f80\u8ba4\u77e5/\u795e\u7ecf\u65b9\u5411\u63a2\u7d22 |\n\n*\u53ea\u6709\u4e00\u95e8\u8bfe\uff0cA-\uff0c\u6b63\u5e38\u3002*\n\n### Spring 2025 \u2014 GPA 3.51 \u26a0\ufe0f\n\n| \u8bfe\u7a0b | \u6210\u7ee9 | \u5185\u90e8\u6279\u6ce8 |\n|------|------|---------|\n| CS 128 Intro CS II | A | \u57fa\u7840OK |\n| **CS 173 Discrete Structures** | **B+** | \u26a0\ufe0f CS \u57fa\u77f3\u8bfe\uff0cB+ \u4f1a\u88ab\u753b\u5708 |\n| **CS 591 Biologically Plausible AI** | **S (\u6b63\u5f0f\u6ce8\u518c)** | \u4ece\u65c1\u542c\u5230\u6b63\u5f0f\u53c2\u4e0e\u2014\u2014\u6301\u7eed\u6295\u5165 |\n| **MATH 257 Linear Algebra** | **B** | \u26a0\ufe0f ML \u7684\u6570\u5b66\u57fa\u7840\uff0cB \u662f\u6700\u5927\u7684\u5355\u9879\u5f31\u70b9 |\n| MATH 314 Intro Higher Math | A- | \u7eaf\u6570\u8bc1\u660e\u8bfe\u62ff A-\uff0c\u8bf4\u660e\u62bd\u8c61\u601d\u7ef4\u80fd\u529b\u5728 |\n| NEUR 462 Integrative Neuroscience | CR | \u9ad8\u9636\u795e\u7ecf\u79d1\u5b66\uff0cCR \u4e0d\u51fa letter grade |\n| STAT 400 Statistics & Probability I | CR | \u540c\u4e0a |\n\n**\u5185\u90e8\u72ec\u767d\uff1a**\n\n*\u8fd9\u662f\u6700\u5f31\u7684\u4e00\u4e2a\u5b66\u671f\u3002CS 173 B+ \u548c MATH 257 B \u662f\u4e24\u4e2a\u6211\u4f1a\u5728 Committee \u91cc\u88ab\u5bf9\u624b\u62ff\u6765\u653b\u51fb\u7684\u70b9\u2014\u2014\"\u57fa\u7840\u8bfe\u62ff B\uff0c\u600e\u4e48\u8bc1\u660e\u5230\u4e86\u54c8\u4f5b\u7684\u7406\u8bba\u73af\u5883\u4e0d\u4f1a\u5d29\uff1f\"*\n\n*\u4f46\u6211\u4e5f\u6ce8\u610f\u5230\u51e0\u4ef6\u4e8b\uff1a*\n1. *\u4ed6\u540c\u65f6\u4fee\u4e86 7 \u95e8\u8bfe\uff08\u542b\u9ad8\u9636\u795e\u7ecf\u79d1\u5b66\u548c\u7eaf\u6570\u8bc1\u660e\u8bfe\uff09\uff0c\u8d1f\u8377\u6781\u5927*\n2. *MATH 314\uff08\u8bc1\u660e\u8bfe\uff09\u62ff\u4e86 A-\uff0c\u8bf4\u660e\u6570\u5b66\u62bd\u8c61\u80fd\u529b\u672c\u8eab\u6ca1\u95ee\u9898*\n3. *CR \u7684\u8bfe\u2014\u2014NEUR 462 \u548c STAT 400\u2014\u2014\u5982\u679c\u8fd9\u4e9b\u662f\u5236\u5ea6\u653f\u7b56\u800c\u975e\u4e3b\u52a8\u9009\u62e9 pass/fail\uff0c\u53ef\u4ee5\u89e3\u91ca\u3002\u4f46\u5982\u679c\u662f\u4e3a\u4e86\u4fdd GPA \u4e3b\u52a8 CR\uff0c\u4f1a\u88ab\u4fdd\u5b88\u89e3\u8bfb*\n\n*\u6211\u5fc3\u91cc\u753b\u4e86\u4e2a\u95ee\u53f7\uff0c\u4f46\u8fd8\u6ca1\u4e0b\u6b7b\u5211\u3002\u63a5\u4e0b\u6765\u7684\u8868\u73b0\u5fc5\u987b\u8bc1\u660e\u8fd9\u662f\"\u8d1f\u8377\u8fc7\u5927\u7684\u4e00\u5b66\u671f\"\u800c\u4e0d\u662f\u80fd\u529b\u5929\u82b1\u677f\u3002*\n\n### Summer 2025 \u2014 Stanford + Parkland\n\n| \u8bfe\u7a0b | \u6210\u7ee9 | \u5185\u90e8\u6279\u6ce8 |\n|------|------|---------|\n| CS 225 Data Structures (Parkland) | A | \u6570\u636e\u7ed3\u6784\u6ee1\u5206 |\n| CS 374 Algorithms (Stanford) | CR | CR \u4e0d\u51fa\u5206\uff0c\u4f46\u662f Stanford \u7684\u7b97\u6cd5\u8bfe |\n| **CS 446 Machine Learning (Stanford)** | **A** | \u26a1 **\u5728 Stanford \u4fee\u7814\u7a76\u751f\u7ea7 ML \u62ff A\u3002** \u8fd9\u5927\u5e45\u7f13\u89e3\u4e86\u6211\u5bf9\u90a3\u4e2a MATH 257 B \u7684\u7126\u8651\u2014\u2014\u4f60\u5728\u9876\u7ea7\u73af\u5883\u91cc\u8bc1\u660e\u4e86\u81ea\u5df1\u80fd handle ML \u7684\u6570\u5b66 |\n| TE Elective (Stanford) | A | |\n\n**\u5185\u90e8\u72ec\u767d\uff1a** *Stanford \u6691\u671f\u8fd9\u4e00\u624b\u975e\u5e38\u5173\u952e\u3002\u5728\u522b\u4eba\u7684\u5730\u76d8\u4e0a\u4fee\u786c\u8bfe\u62ff A\uff0c\u8bf4\u660e\u4e0d\u662f\"UIUC \u5185\u90e8\u7ed9\u5206\u677e\"\u7684\u5047\u8c61\u3002CS 374 \u7528 CR \u53ef\u80fd\u662f\u9879\u76ee\u653f\u7b56\u2014\u2014\u9700\u8981\u786e\u8ba4\u3002*\n\n### Fall 2025 \u2014 GPA 3.75\uff08\u56de\u5347\uff09\u2b06\ufe0f\n\n| \u8bfe\u7a0b | \u6210\u7ee9 | \u5185\u90e8\u6279\u6ce8 |\n|------|------|---------|\n| CS 447 Natural Language Processing | B+ | \u53c8\u4e00\u4e2a B+\uff0c\u5728 NLP \u5165\u95e8\u8bfe\u4e0a... |\n| **CS 542 Statistical RL** | **A** | \u26a1 \u7814\u7a76\u751f\u7ea7 RL\uff0c\u6ee1\u5206 |\n| **CS 546 Advanced Topics in NLP** | **A** | \u26a1 \u9ad8\u9636 NLP \u6ee1\u5206\u2014\u2014\u6bd4 CS 447 \u66f4\u96be\u7684\u8bfe\u53cd\u800c\u62ff A\uff1f |\n| **CS 598 Text Mining with LLM** | **A-** | \u7814\u7a76\u751f seminar \u7ea7\u522b |\n| **LING 507 Formal Semantics I** | **A-** | \u7814\u7a76\u751f\u5f62\u5f0f\u8bed\u4e49\u5b66\u2014\u2014\u8bed\u8a00\u5b66\u7406\u8bba\u5e95\u5b50 |\n\n**\u5185\u90e8\u72ec\u767d\uff1a**\n\n*\u8fd9\u4e2a\u5b66\u671f\u8ba9\u6211\u771f\u6b63\u5750\u76f4\u4e86\u3002*\n\n*\u4e94\u95e8\u8bfe\u91cc\u56db\u95e8\u662f 500/600 \u7ea7\u522b\uff08\u7814\u7a76\u751f\u8bfe\uff09\uff0c\u5728 UIUC CS \u8fd9\u79cd\u7ea7\u522b\u7684\u7a0b\u5e8f\u91cc\u62ff A/A/A-/A-\u3002\u8fd9\u662f\u4e00\u4e2a\u540d\u4e49\u4e0a\u7684\u620f\u5267 BFA \u5b66\u751f\uff0c\u5728\u5927\u4e8c\u79cb\u5b63\u540c\u65f6\u4fee\u56db\u95e8\u7814\u7a76\u751f CS/NLP \u8bfe + \u4e00\u95e8\u7814\u7a76\u751f\u5f62\u5f0f\u8bed\u4e49\u5b66\u3002*\n\n*\u51fa\u73b0\u4e86\u4e00\u4e2a\u975e\u5e38\u6709\u8da3\u7684\u77db\u76fe\uff1aCS 447 (NLP \u5165\u95e8) B+\uff0c\u4f46 CS 546 (Advanced NLP) A\u3002\u57fa\u7840\u8bfe\u53cd\u800c\u5206\u4f4e\uff0c\u9ad8\u9636\u8bfe\u53cd\u800c\u9ad8\u3002\u8fd9\u901a\u5e38\u610f\u5473\u7740\u4e24\u79cd\u60c5\u51b5\u4e4b\u4e00\uff1a*\n\n1. *\u4ed6\u662f\u7814\u7a76\u578b\u601d\u7ef4\u2014\u2014\u5bf9\u57fa\u7840\u8bfe\u7684\u673a\u68b0\u8bad\u7ec3\u4e0d\u591f\u8010\u5fc3\uff0c\u4f46\u5728\u9ad8\u9636/\u5f00\u653e\u6027\u8bfe\u7a0b\u4e2d\u5982\u9c7c\u5f97\u6c34*\n2. *\u6216\u8005\u9ad8\u9636\u8bfe project-based \u8bc4\u5206\u504f\u5bbd*\n\n*\u8003\u8651\u5230\u8fd9\u8de8\u4e86\u591a\u95e8\u8bfe\uff08RL\u3001NLP\u3001LLM\u3001Linguistics \u5168\u90e8 A \u6216 A-\uff09\uff0c\u6211\u503e\u5411\u4e8e\u76f8\u4fe1\u524d\u8005\u3002*\n\n### Spring 2026\uff08\u5f53\u524d\u8fdb\u884c\u4e2d\uff09\n\n| \u8bfe\u7a0b | \u5b66\u5206 |\n|------|------|\n| CS 443 Reinforcement Learning | 3 |\n| CS 510 Advanced Information Retrieval | 4 |\n| CS 598 Graph Machine Learning | 4 |\n| CS 598 Graph Algorithms | 4 |\n| LING 551 Pragmatics | 4 |\n\n**\u5185\u90e8\u72ec\u767d\uff1a** *\u4e94\u95e8\u8bfe\uff0c\u56db\u95e8\u7814\u7a76\u751f\u7ea7\u522b\u3002\u8fd9\u5f20\u8bfe\u8868\u6211\u5728 UIUC CS PhD \u5b66\u751f\u8eab\u4e0a\u89c1\u8fc7\u3002\u5982\u679c\u8fd9\u5b66\u671f\u7ef4\u6301 A \u8f68\u8ff9\uff0c\u5b66\u672f\u53d9\u4e8b\u4f1a\u8fdb\u4e00\u6b65\u52a0\u5f3a\u3002*\n\n---\n\n## \u30108:00\u201313:00 \u2014 \u7efc\u5408\u8bc4\u4f30\u4e0e\u5185\u90e8\u8bc4\u5206\u3011\n\n### \u5b66\u672f\u8f68\u8ff9\uff1a\u5173\u952e\u7279\u5f81\u662f**\u52a0\u901f\u5ea6**\n\n```\nFall 24: CS 124 (\u5165\u95e8) + \u65c1\u542c\u7814\u7a76\u751f\u8bfe \u2192 GPA 3.92\nSpr 25: CS 128/173 + \u6570\u5b66\u57fa\u7840 + \u795e\u7ecf\u79d1\u5b66 \u2192 GPA 3.51 (\u8d1f\u8377\u8fc7\u5927)\nSum 25: Stanford ML (A) \u2192 \u5916\u90e8\u9a8c\u8bc1\nFall 25: CS 542/546/598 \u5168\u7814\u7a76\u751f\u8bfe \u2192 GPA 3.75 (\u56de\u5347)\nSpr 26: \u5168\u90e8 500/600 \u7ea7\u522b \u2192 \u8fdb\u884c\u4e2d\n```\n\n**\u4ece CS 124 \u5230 CS 598 \u7528\u4e86\u4e09\u4e2a\u5b66\u671f\u3002** \u8fd9\u4e0d\u662f\u6b63\u5e38\u4eba\u7c7b\u7684\u8bfe\u7a0b\u8fdb\u5ea6\u3002\n\n### \u8de8\u5b66\u79d1\u53d9\u4e8b\u8f6e\u5ed3\n\n\u6211\u5728\u8fd9\u4efd\u6210\u7ee9\u5355\u91cc\u80fd\u770b\u5230\u4e00\u6761\u6e05\u6670\u7684\u7ebf\uff1a\n\n> **Theatre (embodied performance / narrative) \u2192 Neuroscience (cognition / brain) \u2192 NLP / RL (computational models of language and decision) \u2192 Linguistics (formal semantics, pragmatics)**\n\n\u8fd9\u4e0d\u662f\u4e71\u9009\u8bfe\u3002\u8fd9\u50cf\u662f\u4e00\u4e2a\u4eba\u5728\u95ee\uff1a**\"\u8bed\u8a00\u3001\u601d\u7ef4\u548c\u884c\u52a8\u7684\u8ba1\u7b97\u672c\u8d28\u662f\u4ec0\u4e48\uff1f\"**\u2014\u2014\u7136\u540e\u4ece\u56db\u4e2a\u4e0d\u540c\u5165\u53e3\u903c\u8fd1\u540c\u4e00\u4e2a\u95ee\u9898\u3002\u5982\u679c\u6587\u4e66\u80fd\u628a\u8fd9\u6761\u7ebf\u8bb2\u6e05\u695a\uff0c\u8fd9\u4e2a\u8de8\u5b66\u79d1\u7ec4\u5408\u5728\u54c8\u4f5b\u7684 Liberal Arts \u6846\u67b6\u4e0b\u4f1a\u975e\u5e38\u6709\u5438\u5f15\u529b\u3002\n\n### \u6682\u5b9a\u5185\u90e8\u8bc4\u5206\n\n| \u7ef4\u5ea6 | \u6682\u5b9a\u8bc4\u5206 (1\u6700\u597d, 6\u6700\u5dee) | \u7406\u7531 |\n|------|------------------------|------|\n| **Academic Rating** | **2+**\uff08\u63a5\u8fd1 2\uff0c\u672a\u7ed9 1\uff09 | GPA \u4e0d\u662f 3.9+\uff0c\u4f46\u8bfe\u7a0b\u52a0\u901f\u5ea6\u548c\u7814\u7a76\u751f\u7ea7\u8868\u73b0\u6781\u4e3a\u7f55\u89c1\u3002BFA \u5b66\u751f 18 \u4e2a\u6708\u5185\u8fdb\u5165\u7814\u7a76\u751f CS/NLP \u5e76\u62ff A\u2014\u2014\u8fd9\u6bd4 4.0 \u7684\u666e\u901a\u8bfe\u8868\u66f4\u6709\u8bf4\u660e\u529b\u3002**\u5982\u679c\u6709\u79d1\u7814\u4ea7\u51fa\uff0c\u9501\u5b9a 1\u3002** |\n| **Intellectual Curiosity** | **1** | \u6beb\u65e0\u7591\u95ee\u3002\u7b2c\u4e00\u5b66\u671f\u65c1\u542c\u7814\u7a76\u751f seminar\u3001\u6691\u671f\u53bb Stanford\u3001\u81ea\u5efa\u8bfe\u7a0b\u4f53\u7cfb\u8de8\u56db\u4e2a\u5b66\u79d1\u2014\u2014\u8fd9\u4e2a\u4eba\u4e0d\u9700\u8981\u4efb\u4f55\u4eba\u544a\u8bc9\u4ed6\u53bb\u5b66\u4ec0\u4e48\u3002 |\n| **\"\u4e3a\u4ec0\u4e48\u8f6c\u5b66\" \u5408\u7406\u6027** | **Strong\uff08\u6682\u5b9a\uff09** | BFA Theatre \u6846\u67b6\u663e\u7136\u5df2\u7ecf\u5bb9\u4e0d\u4e0b\u8fd9\u4e2a\u5b66\u751f\u3002\u4f46\u5173\u952e\u95ee\u9898\u662f\uff1a**\u4e3a\u4ec0\u4e48\u4e0d\u76f4\u63a5 internal transfer \u5230 UIUC CS\uff1f** \u5982\u679c\u6709\u5236\u5ea6\u969c\u788d\uff08BFA \u2192 BS \u8de8\u9662\u8f6c\u5b66\u56f0\u96be\uff09\uff0c\u8fd9\u53cd\u800c\u52a0\u5f3a\u53d9\u4e8b\uff1b\u5982\u679c\u6ca1\u8bd5\u8fc7\uff0c\u8fd9\u662f\u6f5c\u5728\u5f31\u70b9\u3002 |\n\n---\n\n## \u301013:00\u201315:00 \u2014 Committee \u9884\u6f14\uff1a\u98ce\u9669\u70b9\u4e0e\u53cd\u9a73\u3011\n\n### \u6211\u5728 Committee \u91cc\u4f1a\u88ab\u5bf9\u624b\u63d0\u51fa\u7684\u653b\u51fb\n\n| \u653b\u51fb\u70b9 | \u53cd\u9a73\u51c6\u5907 |\n|--------|---------|\n| **\"GPA 3.77\uff0c\u6211\u4eec\u6c60\u5b50\u91cc 3.9+ \u662f\u5e38\u6001\"** | \u628a\u8bfe\u8868\u6253\u5f00\u2014\u2014\u540c\u6837 3.9+ \u7684\u4eba\u6709\u51e0\u4e2a\u5728\u4fee\u56db\u95e8\u7814\u7a76\u751f CS \u8bfe\uff1fRigor-adjusted \u8fd9\u4e2a 3.77 \u7684\u542b\u91d1\u91cf\u8fdc\u9ad8\u4e8e\u62ff\u7740\u901a\u8bc6\u8bfe\u5237\u51fa\u6765\u7684 3.95\u3002 |\n| **\"Linear Algebra B, Discrete B+\u2014\u2014\u57fa\u7840\u4e0d\u7262\"** | \u540e\u7eed\u5728 Statistical RL (A)\u3001Stanford ML (A) \u4e2d\u5df2\u81ea\u6211\u9a8c\u8bc1\u3002MATH 314 \u8bc1\u660e\u8bfe A-\uff0c\u8bf4\u660e\u62bd\u8c61\u6570\u5b66\u80fd\u529b\u5728\u3002Spring 2025 \u662f\u8fc7\u8f7d\u5b66\u671f\uff0c\u4e0d\u662f\u80fd\u529b\u5929\u82b1\u677f\u3002 |\n| **\"CS 447 NLP \u5165\u95e8 B+ \u4f46 CS 546 \u9ad8\u9636 NLP \u62ff A\u2014\u2014\u662f\u4e0d\u662f\u9ad8\u9636\u8bfe\u7ed9\u5206\u677e\uff1f\"** | \u8de8\u4e86 RL\u3001NLP\u3001LLM\u3001Linguistics \u56db\u4e2a\u65b9\u5411\u5168\u90e8 A/A-\uff0c\u4e0d\u592a\u53ef\u80fd\u5168\u90e8\u7ed9\u5206\u504f\u5bbd\u3002\u66f4\u53ef\u80fd\u662f\u7814\u7a76\u578b\u601d\u7ef4\u5728\u5f00\u653e\u6027\u8bfe\u7a0b\u4e2d\u8868\u73b0\u66f4\u597d\u3002 |\n| **\"CR/Pass \u7684\u8bfe\u592a\u591a\u2014\u2014NEUR 462, STAT 400, Stanford CS 374\"** | \u9700\u8981\u786e\u8ba4\u662f\u5236\u5ea6\u653f\u7b56\u8fd8\u662f\u4e3b\u52a8\u9009\u62e9\u3002\u5982\u679c\u662f\u653f\u7b56\u6027 CR\uff0c\u4e0d\u6263\u5206\uff1b\u5982\u679c\u662f\u4e3b\u52a8\u9009\u62e9\uff0c\u9700\u8981\u6587\u4e66\u89e3\u91ca\u3002 |\n| **\"UIUC CS \u5168\u7f8e Top 5\uff0c\u4f60\u5df2\u7ecf\u5728\u90a3\u91cc\u4fee\u7814\u7a76\u751f\u8bfe\u4e86\uff0c\u4e3a\u4ec0\u4e48\u9700\u8981\u54c8\u4f5b\uff1f\"** | **\u8fd9\u662f\u6700\u81f4\u547d\u7684\u95ee\u9898\u3002** \u5fc5\u987b\u6709 Harvard-specific \u7684\u7b54\u6848\u2014\u2014\u5177\u4f53\u5230\u5b9e\u9a8c\u5ba4/\u5bfc\u5e08/\u8de8\u5b66\u79d1\u751f\u6001\u3002\u5982\u679c\u7b54\u4e0d\u597d\uff0c\u76f4\u63a5\u51fa\u5c40\u3002 |\n| **\"Theatre \u5b66\u5206\u8f6c\u5165\u54c8\u4f5b\u600e\u4e48\u529e\uff1f\u80fd\u4e24\u5e74\u5185\u6bd5\u4e1a\u5417\uff1f\"** | \u9700\u8981\u786e\u8ba4\u5b66\u5206\u8f6c\u79fb\u8ba1\u5212\u548c\u6bd5\u4e1a\u65f6\u95f4\u7ebf\u3002 |\n\n---\n\n## \u301015:00 \u2014 \u6682\u5b9a\u7ed3\u8bba\u3011\n\n> ### **Hold for Second Read \u2014 \u6709\u8fdb\u5165 Committee \u7684\u6f5c\u529b**\n>\n> \u8fd9\u4efd\u6210\u7ee9\u5355\u6709\u4e00\u4e2a\u6781\u4e3a\u7f55\u89c1\u7684\u7279\u5f81\uff1a**\u52a0\u901f\u5ea6**\u3002\u4e0d\u662f\u8d77\u70b9\u9ad8\uff0c\u800c\u662f**\u8f68\u8ff9\u7684\u659c\u7387\u9661\u5f97\u4e0d\u6b63\u5e38**\u3002\u4ece\u7b2c\u4e00\u5b66\u671f\u65c1\u542c\u7814\u7a76\u751f\u8bfe\u5230\u4e09\u4e2a\u5b66\u671f\u540e\u540c\u65f6\u4fee\u56db\u95e8\u7814\u7a76\u751f CS\uff0c\u8fd9\u662f\u6211\u4eca\u5929\u5ba1\u7684\u51e0\u5341\u4efd\u6750\u6599\u91cc\u6700\u4e0d\u5bfb\u5e38\u7684\u8bfe\u7a0b\u8f68\u8ff9\u3002\n>\n> \u8de8\u5b66\u79d1\u53d9\u4e8b\u7684\u6709\u673a\u6027\uff08Theatre \u2192 Neuro \u2192 NLP/RL \u2192 Formal Semantics\uff09\u5982\u679c\u5728\u6587\u4e66\u4e2d\u88ab\u8bb2\u6e05\u695a\uff0c\u4f1a\u6210\u4e3a\u8fd9\u4efd\u7533\u8bf7\u7684\u6838\u5fc3\u7ade\u4e89\u529b\u3002\n>\n> **\u4f46\u4ec5\u51ed\u6210\u7ee9\u5355\uff0c\u8fd8\u4e0d\u591f\u8fdb Committee\u3002** \u80fd\u5426\u771f\u6b63\u63a8\u8fdb\u53d6\u51b3\u4e8e\u4ee5\u4e0b\u4e09\u4e2a\u6761\u4ef6\u4e2d\u81f3\u5c11\u6ee1\u8db3\u4e00\u4e2a\uff1a\n\n### \u63a5\u4e0b\u6765\u5fc5\u987b\u770b\u5230\u7684\u6750\u6599\uff08\u51b3\u5b9a Second Read \u2192 Committee \u8fd8\u662f \u2192 Reject\uff09\n\n**Part C \u2014 \u6d3b\u52a8\u4e0e\u6210\u5c31\uff08\u6700\u5173\u952e\uff09**\n\n\u6211\u73b0\u5728\u6700\u60f3\u77e5\u9053\u7684\uff1a\n\n1. **\u6709\u6ca1\u6709\u79d1\u7814\u4ea7\u51fa\uff1f** \u5728\u8fd9\u4e2a\u7ea7\u522b\u7684\u8bfe\u7a0b\u57fa\u7840\u4e0a\uff0c\u5982\u679c\u6709\u8bba\u6587/\u9876\u4f1a/\u5b9e\u8d28\u6027\u7814\u7a76\u8d21\u732e\uff0cAcademic Rating \u76f4\u63a5\u4ece 2+ \u9501\u5b9a **1**\u3002\u5982\u679c\u6ca1\u6709\u2014\u2014\"\u4e0a\u4e86\u5f88\u591a\u96be\u8bfe\u4f46\u6ca1\u6709\u4ea7\u51fa\"\u5728\u54c8\u4f5b\u8f6c\u5b66\u6c60\u91cc\u4e0d\u591f\u3002\n\n2. **Theatre/\u827a\u672f\u65b9\u9762\u6709\u6ca1\u6709\u5b9e\u8d28\u6027\u6210\u5c31\uff1f** \u5982\u679c\u6709\u2014\u2014\u8de8\u5b66\u79d1\u53d9\u4e8b\u53d8\u6210**\u6838\u6b66\u5668**\uff08\u4e00\u4e2a\u5728\u821e\u53f0\u4e0a\u6709\u771f\u6b63\u4f5c\u54c1\u7684\u4eba\u540c\u65f6\u5728\u505a PhD \u7ea7\u522b\u7684 AI \u7814\u7a76\uff09\u3002\u5982\u679c\u6ca1\u6709\u2014\u2014Theatre \u53d8\u6210\u7eaf\u7cb9\u7684\u5236\u5ea6\u80cc\u666f\uff0c\u53d9\u4e8b\u529b\u91cf\u51cf\u534a\u3002\n\n3. **\u6709\u6ca1\u6709\u89c4\u6a21\u5316\u7684\u5f71\u54cd\uff1f** \u521b\u4e1a/\u5f00\u6e90\u9879\u76ee/\u4ea7\u54c1/\u7ade\u8d5b/\u516c\u76ca\u2014\u2014\u4efb\u4f55\u8bc1\u660e\u4f60\u4e0d\u53ea\u662f\"\u5b66\u5f97\u591a\"\u800c\u662f\"\u505a\u4e86\u4ec0\u4e48\"\u7684\u8bc1\u636e\u3002\n\n\u8bf7\u7528\u8fd9\u4e2a\u683c\u5f0f\uff1a\n- **\u505a\u4e86\u4ec0\u4e48**\uff08\u4e00\u53e5\u8bdd\uff09\n- **\u4f60\u7684\u89d2\u8272**\uff08\u6838\u5fc3\u8d21\u732e\u8005 / \u53c2\u4e0e\u8005\uff09\n- **\u53ef\u91cf\u5316\u7ed3\u679c**\uff08\u6570\u636e/\u7528\u6237/\u5f15\u7528/\u90e8\u7f72/\u5956\u9879/\u5a92\u4f53\uff09\n- **\u8bc1\u636e\u8f7d\u4f53**\uff08\u8bba\u6587\u94fe\u63a5\u3001GitHub\u3001\u4f5c\u54c1\u96c6\u3001\u6f14\u51fa\u8bb0\u5f55\u7b49\uff09\n\n**Part D \u2014 \u8f6c\u5b66\u539f\u56e0\uff08Push + Pull\uff09**\n- UIUC \u54ea\u91cc\u65e0\u6cd5\u6ee1\u8db3\u4f60\uff08\u5177\u4f53\uff0c\u4e0d\u8981\u6cdb\u6cdb\uff09\n- \u54c8\u4f5b\u54ea\u4e9b\u8d44\u6e90/\u4eba/\u9879\u76ee\u80fd\u628a\u4f60\u6b63\u5728\u505a\u7684\u4e8b\u653e\u5927\uff08\u70b9\u540d\u5230\u5b9e\u9a8c\u5ba4/\u8bfe\u7a0b/\u8de8\u9662\u7ed3\u6784\uff09\n- \u5c24\u5176\u56de\u7b54\uff1a**\"\u4e3a\u4ec0\u4e48\u4e0d\u76f4\u63a5 internal transfer \u5230 UIUC CS\uff1f\"**\n\n---\n\n**\u8ba1\u65f6\u5668\u8fd8\u5728\u8dd1\u3002\u8bf7\u7ee7\u7eed\u53d1\u9001\u3002**"
}
}
]
}
|
