{"id":35141,"date":"2025-08-10T12:42:50","date_gmt":"2025-08-10T04:42:50","guid":{"rendered":"https:\/\/www.kdjingpai.com\/?p=35141"},"modified":"2025-08-10T12:42:50","modified_gmt":"2025-08-10T04:42:50","slug":"cycleresearcher","status":"publish","type":"post","link":"https:\/\/www.kdjingpai.com\/ja\/cycleresearcher\/","title":{"rendered":"CycleResearcher\uff1aAI\u9a71\u52a8\u7684\u5b66\u672f\u7814\u7a76\u4e0e\u5ba1\u7a3f\u81ea\u52a8\u5316\u5de5\u5177"},"content":{"rendered":"<p>CycleResearcher \u662f\u4e00\u4e2a\u5f00\u6e90\u7684 AI \u9a71\u52a8\u5b66\u672f\u7814\u7a76\u4e0e\u5ba1\u7a3f\u751f\u6001\u7cfb\u7edf\uff0c\u65e8\u5728\u901a\u8fc7\u81ea\u52a8\u5316\u5de5\u5177\u63d0\u5347\u5b66\u672f\u7814\u7a76\u6548\u7387\u3002\u5b83\u5305\u542b\u4e09\u4e2a\u6838\u5fc3\u7ec4\u4ef6\uff1aCycleResearcher \u7528\u4e8e\u751f\u6210\u9ad8\u8d28\u91cf\u5b66\u672f\u8bba\u6587\uff0cCycleReviewer \u63d0\u4f9b\u8be6\u7ec6\u7684\u5b66\u672f\u5ba1\u7a3f\uff0cDeepReviewer \u6a21\u62df\u591a\u4f4d\u5ba1\u7a3f\u4eba\u5e76\u8fdb\u884c\u81ea\u6211\u9a8c\u8bc1\u3002\u8fd9\u5957\u7cfb\u7edf\u901a\u8fc7\u7814\u7a76\u4e0e\u5ba1\u7a3f\u7684\u95ed\u73af\u53cd\u9988\uff0c\u52a0\u901f\u79d1\u5b66\u53d1\u73b0\uff0c\u964d\u4f4e\u4eba\u5de5\u6210\u672c\u3002\u5b83\u652f\u6301\u591a\u79cd\u6a21\u578b\u548c\u6570\u636e\u96c6\uff0c\u9002\u5408\u7814\u7a76\u4eba\u5458\u3001\u5b66\u751f\u548c\u5f00\u53d1\u8005\u4f7f\u7528\u3002\u7528\u6237\u53ef\u901a\u8fc7\u7b80\u5355\u5b89\u88c5\u548c API \u8c03\u7528\u5feb\u901f\u4e0a\u624b\uff0c\u9002\u7528\u4e8e\u5b66\u672f\u5199\u4f5c\u3001\u8bba\u6587\u8bc4\u5ba1\u7b49\u573a\u666f\u3002<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-35132\" src=\"https:\/\/www.kdjingpai.com\/wp-content\/uploads\/2025\/08\/7f5b4418fb57cd2.png\" alt=\"\" width=\"1080\" height=\"540\" srcset=\"https:\/\/www.kdjingpai.com\/wp-content\/uploads\/2025\/08\/7f5b4418fb57cd2.png 1080w, https:\/\/www.kdjingpai.com\/wp-content\/uploads\/2025\/08\/7f5b4418fb57cd2-18x9.png 18w\" sizes=\"auto, (max-width: 1080px) 100vw, 1080px\" \/><\/p>\n<p>&nbsp;<\/p>\n<h2>\u529f\u80fd\u5217\u8868<\/h2>\n<ul>\n<li><strong>\u8bba\u6587\u751f\u6210<\/strong>\uff1aCycleResearcher \u57fa\u4e8e\u7528\u6237\u63d0\u4f9b\u7684\u4e3b\u9898\u548c\u53c2\u8003\u6587\u732e\u751f\u6210\u5b66\u672f\u8bba\u6587\uff0c\u652f\u6301\u591a\u79cd\u6a21\u578b\uff08\u5982 12B\u300172B\u3001123B\uff09\u3002<\/li>\n<li><strong>\u5b66\u672f\u5ba1\u7a3f<\/strong>\uff1aCycleReviewer \u63d0\u4f9b\u8be6\u7ec6\u7684\u8bba\u6587\u8bc4\u5ba1\uff0c\u8f93\u51fa\u5e73\u5747\u8bc4\u5206\u548c\u63a5\u53d7\/\u62d2\u7edd\u51b3\u5b9a\uff0c\u51c6\u786e\u7387\u8fbe 74.24%\u3002<\/li>\n<li><strong>\u591a\u89c6\u89d2\u5ba1\u7a3f<\/strong>\uff1aDeepReviewer \u6a21\u62df\u591a\u4e2a\u5ba1\u7a3f\u4eba\uff0c\u63d0\u4f9b\u5feb\u901f\u3001\u6807\u51c6\u548c\u6700\u4f73\u4e09\u79cd\u6a21\u5f0f\uff0c\u652f\u6301\u80cc\u666f\u77e5\u8bc6\u641c\u7d22\u548c\u81ea\u6211\u9a8c\u8bc1\u3002<\/li>\n<li><strong>AI \u68c0\u6d4b<\/strong>\uff1a\u5185\u7f6e AIDetector \u5de5\u5177\uff0c\u5206\u6790\u8bba\u6587\u662f\u5426\u7531 AI \u751f\u6210\uff0c\u5e76\u8f93\u51fa\u6982\u7387\u548c\u7f6e\u4fe1\u5ea6\u3002<\/li>\n<li><strong>\u6587\u732e\u67e5\u8be2<\/strong>\uff1aOpenScholar \u96c6\u6210\u5b66\u672f\u95ee\u7b54\u529f\u80fd\uff0c\u652f\u6301\u901a\u8fc7 Semantic Scholar API \u68c0\u7d22\u6587\u732e\u3002<\/li>\n<li><strong>\u6613\u4e8e\u96c6\u6210<\/strong>\uff1a\u63d0\u4f9b Python \u5e93\u548c API \u63a5\u53e3\uff0c\u65b9\u4fbf\u5f00\u53d1\u8005\u5d4c\u5165\u73b0\u6709\u5de5\u4f5c\u6d41\u3002<\/li>\n<li><strong>\u591a\u6a21\u578b\u652f\u6301<\/strong>\uff1a\u652f\u6301\u591a\u79cd\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u5982 Mistral\u3001Llama3.1 \u548c Qwen2.5\uff0c\u6ee1\u8db3\u4e0d\u540c\u6027\u80fd\u9700\u6c42\u3002<\/li>\n<\/ul>\n<h2>\u4f7f\u7528\u5e2e\u52a9<\/h2>\n<h3>\u5b89\u88c5\u6d41\u7a0b<\/h3>\n<p>CycleResearcher \u7684\u5b89\u88c5\u975e\u5e38\u7b80\u5355\uff0c\u7528\u6237\u53ea\u9700\u5728 Python \u73af\u5883\u4e2d\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\u5373\u53ef\uff1a<\/p>\n<pre><code>pip install ai_researcher\r\n<\/code><\/pre>\n<p>\u786e\u4fdd Python \u7248\u672c\u4e3a 3.8 \u6216\u4ee5\u4e0a\u3002\u5982\u679c\u9700\u8981\u4f7f\u7528 OpenScholar\uff0c\u8fd8\u9700\u7533\u8bf7 Semantic Scholar API \u5bc6\u94a5\u5e76\u542f\u52a8\u76f8\u5173\u670d\u52a1\u3002<\/p>\n<h4>OpenScholar \u5b89\u88c5\u4e0e\u542f\u52a8<\/h4>\n<ol>\n<li><strong>\u7533\u8bf7 API \u5bc6\u94a5<\/strong>\uff1a\u8bbf\u95ee\u00a0<a href=\"https:\/\/www.semanticscholar.org\/product\/api\">Semantic Scholar API<\/a>\u00a0\u7533\u8bf7\u5bc6\u94a5\u3002<\/li>\n<li><strong>\u542f\u52a8\u6a21\u578b\u670d\u52a1<\/strong>\uff1a\n<pre><code>cd OpenScholar\r\nchmod +x start_models.sh\r\n.\/start_models.sh\r\n<\/code><\/pre>\n<\/li>\n<li><strong>\u542f\u52a8 API \u670d\u52a1<\/strong>\uff1a\n<pre><code>python openscholar_api.py \\\r\n--s2_api_key YOUR_SEMANTIC_SCHOLAR_API_KEY \\\r\n--reranker_path OpenSciLM\/OpenScholar_Reranker\r\n<\/code><\/pre>\n<\/li>\n<li><strong>\u6d4b\u8bd5 API<\/strong>\uff1a<br \/>\n\u4f7f\u7528 Python requests \u5e93\u53d1\u9001\u95ee\u9898\uff1a<\/p>\n<pre><code>import requests\r\nresponse = requests.post(\"http:\/\/localhost:38015\/batch_ask\", json={\r\n\"questions\": [\"\u68c0\u7d22\u589e\u5f3a\u8bed\u8a00\u6a21\u578b\u5728\u77e5\u8bc6\u5bc6\u96c6\u4efb\u52a1\u4e2d\u7684\u8868\u73b0\u5982\u4f55\uff1f\"]\r\n})\r\nprint(\"OpenScholar \u56de\u7b54:\", response.json()[\"results\"][0][\"output\"])\r\n<\/code><\/pre>\n<\/li>\n<\/ol>\n<h3>\u4f7f\u7528 CycleResearcher \u751f\u6210\u8bba\u6587<\/h3>\n<p>CycleResearcher \u53ef\u751f\u6210\u57fa\u4e8e\u7279\u5b9a\u4e3b\u9898\u548c\u53c2\u8003\u6587\u732e\u7684\u5b66\u672f\u8bba\u6587\u3002\u4ee5\u4e0b\u662f\u64cd\u4f5c\u6b65\u9aa4\uff1a<\/p>\n<ol>\n<li><strong>\u521d\u59cb\u5316\u6a21\u578b<\/strong>\uff1a\n<pre><code>from ai_researcher import CycleResearcher\r\nfrom ai_researcher.utils import print_paper_summary\r\nresearcher = CycleResearcher(model_size=\"12B\")\r\n<\/code><\/pre>\n<p>\u9ed8\u8ba4\u4f7f\u7528 12B \u6a21\u578b\uff0c\u9002\u5408\u5927\u591a\u6570\u4efb\u52a1\u3002<\/li>\n<li><strong>\u52a0\u8f7d\u53c2\u8003\u6587\u732e<\/strong>\uff1a<br \/>\n\u51c6\u5907 BibTeX \u683c\u5f0f\u7684\u53c2\u8003\u6587\u732e\u6587\u4ef6\uff08\u5982\u00a0<code>cycleresearcher_references.bib<\/code>\uff09\uff0c\u7136\u540e\u8bfb\u53d6\uff1a<\/p>\n<pre><code>with open('cycleresearcher_references.bib', 'r') as f:\r\nreferences_content = f.read()\r\n<\/code><\/pre>\n<\/li>\n<li><strong>\u751f\u6210\u8bba\u6587<\/strong>\uff1a<br \/>\n\u6307\u5b9a\u4e3b\u9898\u548c\u53c2\u8003\u6587\u732e\uff0c\u751f\u6210\u4e00\u7bc7\u8bba\u6587\uff1a<\/p>\n<pre><code>generated_papers = researcher.generate_paper(\r\ntopic=\"AI Researcher\",\r\nreferences=references_content,\r\nn=1\r\n)\r\nprint_paper_summary(generated_papers[0])\r\n<\/code><\/pre>\n<p>\u751f\u6210\u7684\u8bba\u6587\u4f1a\u5305\u542b\u6458\u8981\u3001\u5f15\u8a00\u548c\u53c2\u8003\u6587\u732e\u7b49\u5b8c\u6574\u7ed3\u6784\u3002<\/li>\n<\/ol>\n<h3>\u4f7f\u7528 CycleReviewer \u5ba1\u7a3f<\/h3>\n<p>CycleReviewer \u63d0\u4f9b\u81ea\u52a8\u5316\u5ba1\u7a3f\u529f\u80fd\uff0c\u9002\u5408\u5feb\u901f\u8bc4\u4f30\u8bba\u6587\u8d28\u91cf\u3002<\/p>\n<ol>\n<li><strong>\u521d\u59cb\u5316\u6a21\u578b<\/strong>\uff1a\n<pre><code>from ai_researcher import CycleReviewer\r\nreviewer = CycleReviewer(model_size=\"8B\")\r\n<\/code><\/pre>\n<\/li>\n<li><strong>\u5ba1\u7a3f<\/strong>\uff1a<br \/>\n\u5047\u8bbe\u00a0<code>paper_text<\/code>\u00a0\u662f\u5f85\u5ba1\u8bba\u6587\u7684\u6587\u672c\u5185\u5bb9\uff0c\u8fd0\u884c\uff1a<\/p>\n<pre><code>review_results = reviewer.evaluate(paper_text)\r\nprint(f\"\u5e73\u5747\u8bc4\u5206: {review_results[0]['avg_rating']}\")\r\nprint(f\"\u5ba1\u7a3f\u51b3\u5b9a: {review_results[0]['paper_decision']}\")\r\n<\/code><\/pre>\n<p>\u8f93\u51fa\u5305\u62ec\u5e73\u5747\u8bc4\u5206\u548c\u662f\u5426\u63a5\u53d7\u7684\u51b3\u5b9a\uff0c\u51c6\u786e\u7387\u9ad8\u8fbe 74.24%\u3002<\/li>\n<\/ol>\n<h3>\u4f7f\u7528 DeepReviewer \u8fdb\u884c\u591a\u89c6\u89d2\u5ba1\u7a3f<\/h3>\n<p>DeepReviewer \u63d0\u4f9b\u66f4\u590d\u6742\u7684\u5ba1\u7a3f\u529f\u80fd\uff0c\u652f\u6301\u591a\u79cd\u6a21\u5f0f\u548c\u591a\u4f4d\u6a21\u62df\u5ba1\u7a3f\u4eba\u3002<\/p>\n<ol>\n<li><strong>\u521d\u59cb\u5316\u6a21\u578b<\/strong>\uff1a\n<pre><code>from ai_researcher import DeepReviewer\r\ndeep_reviewer = DeepReviewer(model_size=\"14B\")\r\n<\/code><\/pre>\n<\/li>\n<li><strong>\u6807\u51c6\u6a21\u5f0f\u5ba1\u7a3f<\/strong>\uff1a<br \/>\n\u6a21\u62df 4 \u4f4d\u5ba1\u7a3f\u4eba\uff1a<\/p>\n<pre><code>review_results = deep_reviewer.evaluate(\r\npaper_text,\r\nmode=\"Standard Mode\",\r\nreviewer_num=4\r\n)\r\nfor i, review in enumerate(review_results[0]['reviews']):\r\nprint(f\"\u5ba1\u7a3f\u4eba {i+1} \u8bc4\u5206: {review.get('rating', 'N\/A')}\")\r\nprint(f\"\u5ba1\u7a3f\u4eba {i+1} \u6458\u8981: {review.get('summary', 'N\/A')[:100]}...\")\r\n<\/code><\/pre>\n<\/li>\n<li><strong>\u6700\u4f73\u6a21\u5f0f\u5ba1\u7a3f<\/strong>\uff1a<br \/>\n\u542f\u7528\u80cc\u666f\u77e5\u8bc6\u641c\u7d22\u548c\u81ea\u6211\u9a8c\u8bc1\uff1a<\/p>\n<pre><code>review_results = deep_reviewer.evaluate(\r\npaper_text,\r\nmode=\"Best Mode\",\r\nreviewer_num=6,\r\nenable_search=True,\r\nself_verification=True\r\n)\r\n<\/code><\/pre>\n<p>\u6700\u4f73\u6a21\u5f0f\u9002\u5408\u9700\u8981\u6df1\u5165\u5206\u6790\u7684\u573a\u666f\uff0c\u63d0\u4f9b\u66f4\u5168\u9762\u7684\u53cd\u9988\u3002<\/li>\n<\/ol>\n<h3>\u4f7f\u7528 AIDetector \u68c0\u6d4b AI \u751f\u6210\u5185\u5bb9<\/h3>\n<p>\u68c0\u6d4b\u8bba\u6587\u662f\u5426\u7531 AI \u751f\u6210\uff1a<\/p>\n<pre><code>from ai_researcher import AIDetector\r\ndetector = AIDetector(device='cpu')\r\ndetection_result = detector.analyze_paper(paper)\r\nprint(f\"AI \u751f\u6210\u6982\u7387: {detection_result['probability'] * 100:.2f}%\")\r\nprint(f\"\u7f6e\u4fe1\u5ea6: {detection_result['confidence_level']}\")\r\n<\/code><\/pre>\n<h3>\u4f7f\u7528 OpenScholar \u67e5\u8be2\u5b66\u672f\u95ee\u9898<\/h3>\n<p>OpenScholar \u652f\u6301\u57fa\u4e8e\u68c0\u7d22\u7684\u5b66\u672f\u95ee\u7b54\uff0c\u9002\u5408\u5feb\u901f\u67e5\u627e\u6587\u732e\u6216\u89e3\u7b54\u95ee\u9898\u3002\u8fd0\u884c API \u670d\u52a1\u540e\uff0c\u901a\u8fc7 HTTP \u8bf7\u6c42\u53d1\u9001\u95ee\u9898\uff1a<\/p>\n<pre><code>response = requests.post(\"http:\/\/localhost:38015\/batch_ask\", json={\r\n\"questions\": [\"\u5982\u4f55\u63d0\u5347\u8bed\u8a00\u6a21\u578b\u5728\u5b66\u672f\u7814\u7a76\u4e2d\u7684\u8868\u73b0\uff1f\"]\r\n})\r\nprint(response.json()[\"results\"][0][\"output\"])\r\n<\/code><\/pre>\n<h3>\u6ce8\u610f\u4e8b\u9879<\/h3>\n<ul>\n<li><strong>\u6a21\u578b\u9009\u62e9<\/strong>\uff1a\u6839\u636e\u4efb\u52a1\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\uff08\u5982 12B\u300114B\uff09\uff0c\u66f4\u5927\u6a21\u578b\u9002\u5408\u590d\u6742\u4efb\u52a1\u3002<\/li>\n<li><strong>API \u5bc6\u94a5<\/strong>\uff1aOpenScholar \u9700\u8981 Semantic Scholar API \u5bc6\u94a5\uff0c\u786e\u4fdd\u7533\u8bf7\u5e76\u6b63\u786e\u914d\u7f6e\u3002<\/li>\n<li><strong>\u786c\u4ef6\u8981\u6c42<\/strong>\uff1aDeepReviewer \u7684\u6700\u4f73\u6a21\u5f0f\u9700\u8981\u8f83\u9ad8\u7b97\u529b\uff0c\u5efa\u8bae\u4f7f\u7528 GPU \u52a0\u901f\u3002<\/li>\n<li><strong>\u6570\u636e\u96c6\u652f\u6301<\/strong>\uff1a\u7cfb\u7edf\u63d0\u4f9b Review-5K\u3001Research-14K \u7b49\u6570\u636e\u96c6\uff0c\u53ef\u7528\u4e8e\u6a21\u578b\u5fae\u8c03\u6216\u6d4b\u8bd5\u3002<\/li>\n<\/ul>\n<h2>\u5e94\u7528\u573a\u666f<\/h2>\n<ol>\n<li><strong>\u5b66\u672f\u8bba\u6587\u64b0\u5199<\/strong><br \/>\n\u7814\u7a76\u4eba\u5458\u53ef\u4f7f\u7528 CycleResearcher \u5feb\u901f\u751f\u6210\u8bba\u6587\u521d\u7a3f\uff0c\u7ed3\u5408\u53c2\u8003\u6587\u732e\u751f\u6210\u7ed3\u6784\u5316\u5185\u5bb9\uff0c\u9002\u5408\u521d\u7a3f\u6784\u601d\u6216\u7075\u611f\u83b7\u53d6\u3002<\/li>\n<li><strong>\u8bba\u6587\u5ba1\u7a3f<\/strong><br \/>\n\u5b66\u672f\u4f1a\u8bae\u7ec4\u7ec7\u8005\u6216\u671f\u520a\u7f16\u8f91\u53ef\u901a\u8fc7 CycleReviewer \u548c DeepReviewer \u81ea\u52a8\u5316\u5ba1\u7a3f\uff0c\u6a21\u62df\u591a\u4f4d\u5ba1\u7a3f\u4eba\u63d0\u4f9b\u5ba2\u89c2\u8bc4\u4ef7\uff0c\u8282\u7701\u65f6\u95f4\u3002<\/li>\n<li><strong>\u5b66\u672f\u95ee\u9898\u89e3\u7b54<\/strong><br \/>\n\u5b66\u751f\u548c\u7814\u7a76\u4eba\u5458\u53ef\u901a\u8fc7 OpenScholar \u67e5\u8be2\u5b66\u672f\u95ee\u9898\uff0c\u5feb\u901f\u83b7\u53d6\u6587\u732e\u652f\u6301\u6216\u89e3\u7b54\u590d\u6742\u95ee\u9898\u3002<\/li>\n<li><strong>AI \u5185\u5bb9\u68c0\u6d4b<\/strong><br \/>\n\u671f\u520a\u7f16\u8f91\u53ef\u4f7f\u7528 AIDetector \u68c0\u67e5\u6295\u7a3f\u8bba\u6587\u662f\u5426\u7531 AI \u751f\u6210\uff0c\u786e\u4fdd\u5b66\u672f\u8bda\u4fe1\u3002<\/li>\n<li><strong>\u7814\u7a76\u5f00\u53d1<\/strong><br \/>\n\u5f00\u53d1\u8005\u53ef\u5229\u7528 CycleResearcher \u7684 API \u96c6\u6210\u5230\u5b66\u672f\u5de5\u4f5c\u6d41\u4e2d\uff0c\u6784\u5efa\u5b9a\u5236\u5316\u7684\u7814\u7a76\u5de5\u5177\u3002<\/li>\n<\/ol>\n<h2>QA<\/h2>\n<ol>\n<li><strong>CycleResearcher \u652f\u6301\u54ea\u4e9b\u6a21\u578b\uff1f<\/strong><br \/>\n\u652f\u6301 CycleResearcher-ML-12B\u300172B\u3001123B\uff0cCycleReviewer-ML-Llama3.1-8B\u300170B\u3001123B\uff0c\u4ee5\u53ca DeepReviewer-7B\u300114B\uff0c\u5747\u57fa\u4e8e\u4e3b\u6d41\u9884\u8bad\u7ec3\u6a21\u578b\u3002<\/li>\n<li><strong>\u5982\u4f55\u9009\u62e9 DeepReviewer \u7684\u5ba1\u7a3f\u6a21\u5f0f\uff1f<\/strong><br \/>\n\u5feb\u901f\u53cd\u9988\u7528 Fast Mode\uff0c\u5e73\u8861\u51c6\u786e\u6027\u548c\u901f\u5ea6\u7528 Standard Mode\uff0c\u9700\u6df1\u5165\u5206\u6790\u7528 Best Mode\u3002<\/li>\n<li><strong>OpenScholar \u9700\u8981\u989d\u5916\u914d\u7f6e\u5417\uff1f<\/strong><br \/>\n\u9700\u8981 Semantic Scholar API \u5bc6\u94a5\uff0c\u5e76\u542f\u52a8\u6a21\u578b\u548c API \u670d\u52a1\uff0c\u5177\u4f53\u89c1\u5b89\u88c5\u6d41\u7a0b\u3002<\/li>\n<li><strong>\u751f\u6210\u7684\u8bba\u6587\u8d28\u91cf\u5982\u4f55\uff1f<\/strong><br \/>\nCycleResearcher-12B \u7684\u5e73\u5747\u8bc4\u5206\u8fbe 5.36\uff0c\u63a5\u8fd1\u4f1a\u8bae\u63a5\u53d7\u8bba\u6587\u7684 5.69\uff0c\u4f18\u4e8e\u5176\u4ed6 AI \u5de5\u5177\u3002<\/li>\n<li><strong>\u662f\u5426\u652f\u6301\u4e2d\u6587\u8bba\u6587\u751f\u6210\uff1f<\/strong><br \/>\n\u76ee\u524d\u4ee5\u82f1\u6587\u8bba\u6587\u4e3a\u4e3b\uff0c\u4f46\u53ef\u901a\u8fc7\u5fae\u8c03\u652f\u6301\u4e2d\u6587\uff0c\u9700\u7528\u6237\u63d0\u4f9b\u76f8\u5173\u6570\u636e\u96c6\u3002<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>CycleResearcher \u662f\u4e00\u4e2a\u5f00\u6e90\u7684 AI \u9a71\u52a8\u5b66\u672f\u7814\u7a76\u4e0e\u5ba1\u7a3f\u751f\u6001\u7cfb\u7edf\uff0c\u65e8\u5728\u901a\u8fc7\u81ea\u52a8\u5316\u5de5\u5177\u63d0\u5347\u5b66\u672f\u7814\u7a76\u6548\u7387\u3002\u5b83\u5305\u542b\u4e09\u4e2a\u6838\u5fc3\u7ec4\u4ef6\uff1aCycleResearcher \u7528\u4e8e\u751f\u6210\u9ad8\u8d28\u91cf\u5b66\u672f\u8bba\u6587\uff0cCycleReviewer \u63d0\u4f9b\u8be6\u7ec6\u7684\u5b66\u672f\u5ba1\u7a3f\uff0c&#8230;<\/p>\n","protected":false},"author":1,"featured_media":32782,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20,419,427],"tags":[230,389],"class_list":["post-35141","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tool","category-ai-learning","category-research-assistant","tag-aikaiyuanxiangmu","tag-shengchengshenduyanjiuao"],"_links":{"self":[{"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/posts\/35141","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/comments?post=35141"}],"version-history":[{"count":0,"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/posts\/35141\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/media\/32782"}],"wp:attachment":[{"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/media?parent=35141"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/categories?post=35141"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kdjingpai.com\/ja\/wp-json\/wp\/v2\/tags?post=35141"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}