[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-xurrent-q2-2026-ai-release-turns-itsm-into-agents-zh":3,"article-related-xurrent-q2-2026-ai-release-turns-itsm-into-agents-zh":35,"series-tools-1e852f64-41ab-476c-83a6-d0c8732f86b1":84},{"id":4,"title":5,"content":6,"summary":7,"source":8,"source_url":9,"author":10,"image_url":11,"keywords":12,"language":18,"translated_content":10,"views":19,"is_premium":20,"created_at":21,"updated_at":21,"cover_image":11,"published_at":22,"rewrite_status":23,"rewrite_error":10,"rewritten_from_id":24,"slug":25,"category":26,"related_article_id":27,"status":28,"google_indexed_at":10,"x_posted_at":10,"tweet_text":10,"title_rewritten_at":10,"title_original":10,"key_takeaways":29,"topic_cluster_id":33,"embedding":34,"is_canonical_seed":20},"1e852f64-41ab-476c-83a6-d0c8732f86b1","Xurrent Q2 AI 讓 ITSM 變成代理","\u003Cp data-speakable=\"summary\">Xurrent 的 Q2 \u003Ca href=\"\u002Fnews\u002Ftop-ai-github-repositories-dominating-2026-zh\">2026\u003C\u002Fa> AI 發佈把 Assist、\u003Ca href=\"\u002Ftag\u002Fcopilot\">Copilot\u003C\u002Fa>、autonomous a\u003Ca href=\"\u002Fnews\u002Fagentic-ai-turns-autonomy-into-security-problem-zh\">gent\u003C\u002Fa>s 分層塞進 ITSM 工作流。\u003C\u002Fp>\u003Cp>我看 ITSM 工具有一陣子了，真的很容易一眼看出哪家是在做事，哪家只是把舊流程包一層 AI 皮。我打開 Xurrent 這份 Q2 2026 AI release 的時候，原本也以為會是老套路：多一個 sidebar、加一個聊天框、再把「智慧」兩個字貼滿頁面。結果讀下去，我反而有點不爽，因為它不是在賣單一 chatbot，而是在把 AI 拆成四種工作方式，直接塞回 service desk 的既有介面裡。這種做法比較像真的懂現場，不像那種只會做 demo 的產品簡報。\u003C\u002Fp>\u003Cp>我這次主要看的是 Xurrent 自家的更新文章：\u003Ca href=\"https:\u002F\u002Fwww.xurrent.com\u002Fproduct-updates\u002Fagentic-ai-xurrents-q2-2026-release\">Agentic AI: Xurrent’s Q2 2026 AI Releases\u003C\u002Fa>。作者是 Jim Hirschauer，發佈時間是 2026-05-12。文中還提到 Sera AI 已經在 91% 的客戶環境中上線，這個數字我覺得很重要，因為它不是實驗室玩具，而是已經進到真實工作流裡了。這也是我願意拆它的方法論，而不是只看行銷詞的原因。\u003C\u002Fp>\u003Ch2>先別把 AI 做成另一個分頁\u003C\u002Fh2>\u003Cblockquote>“Most enterprise teams aren’t ready to abandon their service desk interface for a blank chat prompt. They shouldn’t have to.”\u003C\u002Fblockquote>\u003Cp>翻譯一下就是：Xurrent 先承認一件很現實的事，企業團隊根本不想離開自己每天在用的 service desk，跑去對著一個空白 prompt 發呆。這句話很直白，但我很買單。因為我看過太多 AI 導入失敗，不是模型不夠強，而是工作流被切成兩半。系統裡有一半資料，\u003Ca href=\"\u002Ftag\u002Fai-工具\">AI 工具\u003C\u002Fa>裡有另一半，最後專家要自己在兩邊搬來搬去，超煩。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779140063861-o0jh.png\" alt=\"Xurrent Q2 AI 讓 ITSM 變成代理\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>Xurrent 的做法是把 Sera AI Assist 直接放進 specialist 的操作畫面裡。它不是要你開新頁面問問題，而是在 request 裡直接做摘要、找相似案件、拉出相關問題。這種設計看起來很小，實際上很關鍵，因為它解決的是上下文切換成本，不是炫技。\u003C\u002Fp>\u003Cp>我之前幫一個 support 團隊看過類似需求。模型可以摘要得很好，但大家就是不想把 ticket 內容複製到另一個工具裡。最後不是 AI 不行，是流程很蠢。工作都在 ticket 裡發生了，你卻要人跳出去問 AI，這就是在逼人中斷思考。\u003C\u002Fp>\u003Cp>實操上，我會先問三件事：\u003C\u002Fp>\u003Cul>\u003Cli>AI 能不能在同一個 request 畫面裡摘要內容？\u003C\u002Fli>\u003Cli>能不能直接找出相似 incidents、problems 或 KB？\u003C\u002Fli>\u003Cli>建議能不能被 specialist 直接接受、修改或忽略？\u003C\u002Fli>\u003C\u002Ful>\u003Cp>如果這三題答不出 yes，我就不會把它叫做 workflow AI。我會把它叫做 demo wrapper，漂亮但沒用。\u003C\u002Fp>\u003Ch2>Copilot 不是會聊天就夠了，它得看得懂系統\u003C\u002Fh2>\u003Cblockquote>“When they need to go deeper, they ask.”\u003C\u002Fblockquote>\u003Cp>這句話其實在講一個很重要的邊界：Assist 是幫你看表面，Copilot 才開始讓你追問。也就是說，Xurrent 想做的不是單純的摘要機，而是能在 request 上下文裡繼續問問題的互動式分析工具。這個差別很大，因為 service work 本來就不是單一答案的世界，而是要把歷史、責任歸屬、影響範圍、最近變更全部串起來。\u003C\u002Fp>\u003Cp>如果 Copilot 只會把人話改寫得更順，那它其實沒什麼用。真正有價值的是，它要能讀懂目前這張單、這個 CI、這個 assignment group，甚至最近相似事件的脈絡。沒有這些上下文，它再會講也只是 polished autocomplete，聽起來像懂，其實沒懂。\u003C\u002Fp>\u003Cp>我很常看到團隊被「AI assistant」騙到。介面做得很順，回答也像人話，但它根本分不出 password reset 跟 Sev 1 outage 的差別。這種東西拿來聊天可以，拿來做 service decision 就很危險。Xurrent 這份 release 比較有意思的地方，是它一直把 Sera AI 跟 service data、signals、workflow 綁在一起，意思就是它不是一顆飄在空中的腦袋。\u003C\u002Fp>\u003Cp>實操上，我會把 Copilot 的問題設計得很具體，別問那種「幫我看這張單」的廢話。直接問這些：\u003C\u002Fp>\u003Cul>\u003Cli>過去 24 小時哪些變更可能跟這次 spike 有關？\u003C\u002Fli>\u003Cli>有哪些相似 request 是同一個團隊解掉的？\u003C\u002Fli>\u003Cli>依照 CI 與分類，最可能的 owner 是誰？\u003C\u002Fli>\u003C\u002Ful>\u003Cp>如果它能在這種問題上給出可追溯的答案，才算真的有上下文。答不出來，那就是把錯誤講得比較順而已。\u003C\u002Fp>\u003Ch2>Autonomous agents 不是魔法，是有權限邊界的流程\u003C\u002Fh2>\u003Cblockquote>“Whether your team wants AI woven into the interface they already use, or you’re ready to deploy autonomous agents across your workflows, these capabilities meet you where you are.”\u003C\u002Fblockquote>\u003Cp>翻譯一下就是：Xurrent 沒把 autonomy 當成另一個獨立產品在賣，而是把它當成成熟度階梯的一段。這點我覺得比很多廠商誠實，因為大部分人一講 \u003Ca href=\"\u002Fnews\u002Fwhy-googles-gemini-spark-should-worry-anyone-using-ai-agents-zh\">agen\u003C\u002Fa>t 就像在講召喚獸，彷彿模型一接上去就能自己把事情做好。現實不是這樣，尤其在 ITSM 裡，\u003Ca href=\"\u002Ftag\u002Fagent\">agent\u003C\u002Fa> 一旦會動作，就一定要先問權限、範圍、稽核、例外處理。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779140063179-brdv.png\" alt=\"Xurrent Q2 AI 讓 ITSM 變成代理\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>我之前處理過一套自動化，表面上很聰明，實際上很難追。票被重新分派了、通知發了、流程被跳過了，但沒人能說清楚為什麼。這種 automation 不叫省工，叫製造法務和維運的共同噩夢。Xurrent 這次的說法比較像是想把 autonomy 留在平台治理框架裡，這才是正路。\u003C\u002Fp>\u003Cp>如果 agent 要真的去動 ITSM record，我會要求三件事：role-based permissions、完整 audit trail、明確 scope。最重要的是，human override 不能跟系統打架。你不能一邊說可控，一邊把人工接手做得比 agent 還麻煩。\u003C\u002Fp>\u003Cp>實操上，我會把 autonomy 切成四層，而不是二選一：\u003C\u002Fp>\u003Cul>\u003Cli>Tier 1：摘要與建議\u003C\u002Fli>\u003Cli>Tier 2：產生待審核動作\u003C\u002Fli>\u003Cli>Tier 3：在政策內執行低風險動作\u003C\u002Fli>\u003Cli>Tier 4：依信心門檻自動升級或轉派\u003C\u002Fli>\u003C\u002Ful>\u003Cp>只要你講不出風險邊界，就先不要談 autonomous agent。那不是成熟，那只是希望模型不要出事。\u003C\u002Fp>\u003Ch2>Sera AI 真有料，前提是你的資料也要像樣\u003C\u002Fh2>\u003Cblockquote>“Sera AI has been part of how Xurrent works for years, routing and classifying requests, drafting knowledge articles, and assisting users directly as a virtual agent, with 91% of customers already running it in production.”\u003C\u002Fblockquote>\u003Cp>這段我認為是整篇最有份量的地方，因為它把 release 從「未來規劃」拉回到「已經上線」。91% 這個數字是 Xurrent 自己提供的，我不亂加戲，但它確實代表一件事：這不是在空中畫餅，而是建立在既有 production 基礎上。\u003C\u002Fp>\u003Cp>白話講，新的 AI 功能不是從零開始，而是疊在既有的 signals、service data 跟 workflow 上。這很重要，因為 AI 最怕的不是模型不夠會講，而是你的環境根本沒什麼可讀的東西。資料亂、分類亂、關聯亂，最後只會得到一台很會講幹話的機器。\u003C\u002Fp>\u003Cp>我看過太多 service environment 的真實問題都不是模型問題，而是 taxonomy 爛、assignment group 過期、knowledge base 沒人維護、CMDB 一堆垃圾資料。這種情況下你上 AI，只是把混亂自動化。Xurrent 這次一直強調 service data 和 workflow，我反而覺得比「agentic」這個詞本身更值得注意。\u003C\u002Fp>\u003Cp>實操上，我會先做一輪 readiness check：\u003C\u002Fp>\u003Cul>\u003Cli>分類是否夠一致，能支撐自動分類或提示\u003C\u002Fli>\u003Cli>assignment group 是否還活著，不是歷史遺跡\u003C\u002Fli>\u003Cli>knowledge articles 是否夠新，能安全引用\u003C\u002Fli>\u003Cli>incidents 和 problems 是否有足夠關聯，能看出模式\u003C\u002Fli>\u003C\u002Ful>\u003Cp>這四項只要有兩項很爛，你就先別急著上 agent。先整理資料，不然 AI 只是在幫你更快複製混亂。\u003C\u002Fp>\u003Ch2>真正的賣點不是 AI 很多，是它放在大家信任的地方\u003C\u002Fh2>\u003Cblockquote>“All governed by the platform your team already trusts.”\u003C\u002Fblockquote>\u003Cp>這句話聽起來很像廠商口吻，我知道。但我不覺得它空。enterprise 裡面，治理跟信任就是能不能上線的差別。Xurrent 明顯想把 Sera AI 包成平台內的受控能力，而不是一個另開政策文件、另做風險審查的外掛 AI 產品。\u003C\u002Fp>\u003Cp>翻譯一下就是：這波產品策略不是在賣新奇，而是在賣 adoption。最容易被團隊接受的 AI，通常不是最聰明的，而是用幾天之後大家就忘了它叫 AI、只覺得流程變順的那種。這種東西不酷，但真的會被用。\u003C\u002Fp>\u003Cp>我自己也比較相信這種做法。AI 如果出現在 request、incident、knowledge article、assignment queue 這些熟悉物件裡，團隊學習成本很低。相反地，如果它跑去另一個 portal，還要人重新學一套互動方式，採用率通常很慘。\u003C\u002Fp>\u003Cp>實操上，我會把導入問題改寫成「AI 出現在哪裡」，而不是「AI 能做什麼」。如果它能直接出現在系統記錄、queue 或 dashboard 裡， adoption 就比較有機會。最後跟主管講的時候，也不要講什麼 transformation，講這四個就好：\u003C\u002Fp>\u003Cul>\u003Cli>少切換上下文\u003C\u002Fli>\u003Cli>更快 triage\u003C\u002Fli>\u003Cli>更穩定的摘要\u003C\u002Fli>\u003Cli>更一致的 routing\u003C\u002Fli>\u003C\u002Ful>\u003Cp>這才是能過會議的語言。其他那些很會喊的詞，通常只會讓人更想睡。\u003C\u002Fp>\u003Ch2>我會怎麼把這套 rollout 到自己團隊\u003C\u002Fh2>\u003Cblockquote>“Four new AI capabilities — from in-interface assistance to fully autonomous agents.”\u003C\u002Fblockquote>\u003Cp>這句話其實已經把 Xurrent 的產品路線講完了：不是單點功能，而是從 assistive 到 autonomous 的完整範圍。這種 framing 我覺得比較健康，因為它讓不同成熟度的團隊都能找到切入口，不會一上來就被迫接受最激進的玩法。\u003C\u002Fp>\u003Cp>白話講，這是一個分階段 adoption model。先從風險低的地方開始，證明有用，再慢慢擴大。這才是 enterprise service operations 的正常節奏。你如果一開始就把 agent 放到高風險流程裡，最後不是收到掌聲，是收到 incident review。\u003C\u002Fp>\u003Cp>我自己的做法會是三步走。第一步先開 Assist，讓它做摘要和相似 request 查找。第二步再開 Copilot，讓它針對固定類型的單做上下文問答。第三步才挑一個低風險流程交給 agent，讓它在 policy 內執行。不要反過來，真的會出事。\u003C\u002Fp>\u003Cp>如果要借 Xurrent 的邏輯，我會把 use case 切成三種：\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Assist\u003C\u002Fstrong>：摘要、建議、找相似案件\u003C\u002Fli>\u003Cli>\u003Cstrong>Copilot\u003C\u002Fstrong>：針對記錄做上下文問答\u003C\u002Fli>\u003Cli>\u003Cstrong>Agent\u003C\u002Fstrong>：執行一個邊界清楚的流程\u003C\u002Fli>\u003C\u002Ful>\u003Cp>這樣講給團隊聽很清楚，也比較容易跟資安、維運、主管對齊。每一步都對應不同風險和不同 success metric，不會整團人都在同一個模糊地帶打轉。\u003C\u002Fp>\u003Ch2>可抄的模板\u003C\u002Fh2>\u003Cpre>\u003Ccode># ITSM Agentic AI rollout template（可直接改成你們內部版本）\n\n## 1) 先挑 workflow，不要先挑模型\n- 選一個高頻、邊界清楚的 service workflow\n- 定義 AI 介入的確切時機\n- 第一版一定要放在既有 ticket \u002F incident 畫面裡\n\n## 2) 分三層上 AI\n### Assist\n用來：\n- 摘要目前 request\n- 找相似 incidents \u002F requests\n- 提示下一步建議\n\n### Copilot\n用來：\n- 針對目前 record 做上下文問答\n- 解釋歷史、owner、impact\n- 草擬回覆給 specialist 審核\n\n### Agent\n用來：\n- 執行一個低風險 workflow\n- 只在明確 policy 與權限內動作\n- 每一步都寫入 audit log\n\n## 3) 必備 guardrails\n- 開 RBAC，不要讓 agent 到處亂跑\n- 記錄 prompts、outputs、actions\n- 設 confidence threshold\n- 高風險動作一律保留 human approval\n- 超出 scope 就直接擋掉\n\n## 4) 資料準備清單\n導入前先確認：\n- 分類一致\n- assignment groups 還有效\n- request \u002F incident 歷史可用\n- knowledge articles 有在維護\n- CMDB 或 service data 沒一堆過期垃圾\n\n## 5) Pilot 成功指標\n追這些就夠：\n- time to triage\n- time to assignment\n- time to resolution\n- 減少多少 context switches\n- AI 建議被接受的比例\n- AI 動作需要 rollback 的比例\n\n## 6) 上線節奏\n1. 先開 inline summarization\n2. 再加 contextual Q&A\n3. 只讓一個 agent 跑一個 bounded workflow\n4. 每週 review logs\n5. 有信任再擴大\n\n## 7) 決策規則\n- AI 不能在 system of record 裡工作，就先不要上\n- AI 不能解釋自己的建議，就先不要上\n- AI 不能被 audit，就先不要上\n\n## 8) 一句操作原則\nAI 在 service management 裡的工作，是減少 friction、保留治理，並且待在大家原本就會用的 workflow 裡。\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp>這段就是我會真的丟給平台團隊的版本。它不花俏，但夠用，而且把治理、資料品質、導入節奏都講清楚了。比起喊 agentic，我更在意的是你能不能把它安全地放進既有流程。\u003C\u002Fp>\u003Cp>原始來源是 Xurrent 的產品更新頁：\u003Ca href=\"https:\u002F\u002Fwww.xurrent.com\u002Fproduct-updates\u002Fagentic-ai-xurrents-q2-2026-release\">Agentic AI: Xurrent’s Q2 2026 AI Releases\u003C\u002Fa>。上面這篇拆解裡，產品觀點和數字來自原文；流程判讀、導入建議和可抄模板則是我根據這份 release 延伸整理出來的。\u003C\u002Fp>","拆 Xurrent Q2 2026 AI 發佈怎麼把 Assist、Copilot、agent 分層塞進 ITSM，最後附可直接複製的 rollout 模板。","www.xurrent.com","https:\u002F\u002Fwww.xurrent.com\u002Fproduct-updates\u002Fagentic-ai-xurrents-q2-2026-release",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779140063861-o0jh.png",[13,14,15,16,17],"ITSM","agentic AI","Sera AI","Copilot","workflow automation","zh",0,false,"2026-05-18T21:33:56.788859+00:00","2026-05-18T21:33:56.748+00:00","done","aa0d321f-1a7c-4b7f-9e46-842f89f0e120","xurrent-q2-2026-ai-release-turns-itsm-into-agents-zh","tools","a87c9ef7-6731-4602-9d5e-b275e86d521a","published",[30,31,32],"Xurrent 不是只加 chatbot，而是把 AI 拆成 Assist、Copilot、agent 三層塞進同一個 service workflow。","真正的導入關鍵不是模型多強，而是資料品質、治理邊界和是否留在既有介面裡。","如果你要抄這套做法，先從低風險摘要與上下文問答開始，再往可審核的自治流程推進。","c3c88dd2-a940-438a-b359-0e5a24562273","[-0.024554174,0.011445187,0.018784532,-0.07660456,0.004784994,-0.0014734745,-0.0025637636,0.0037426357,-0.0178242,0.015994184,-0.008365292,0.011594279,0.012753182,0.015649708,0.14059633,0.0086061815,0.0073808013,0.001949321,-0.006257638,-0.011740409,0.02434481,0.016372256,-0.000754851,-0.004976879,-0.010924234,-0.00018835762,0.011669396,0.0069193207,0.03394316,-0.010052853,0.0017387149,0.018862795,0.0058925594,0.0044603366,0.0072362735,-0.0074172616,0.013913284,-0.0351161,0.0094708465,0.005158651,0.021992508,0.0077979597,0.016098894,0.006161201,-0.027502688,-0.008234906,-0.013995575,-0.05019301,-0.010127801,0.0013973333,-0.017460715,0.017411852,-0.011386823,-0.14404625,-0.010362401,0.014676992,-0.021588951,0.0016626907,0.031114355,-0.026143136,-0.0020576203,0.0077873548,-0.040484626,-0.022882776,-0.015251314,-0.020509353,0.020204749,0.0013768796,-0.00031891043,-0.0025142985,0.0017356414,0.010840307,0.010232886,-0.018659463,-0.0012555933,-0.047491387,0.006227807,0.008182955,-0.0026312252,-0.0034960504,0.014069387,-0.019542765,0.014311262,-0.014840564,0.009633897,-0.011085551,-0.013668188,-0.012049943,0.0255836,-0.005934615,0.0037696874,0.009604951,0.008739696,-0.030622745,0.022269426,0.0029867268,-0.0052771913,0.021497756,-0.0007567852,-0.018913046,-0.027931128,-0.039609056,-0.027220633,0.01125532,0.007064735,0.003897349,-0.01819916,-0.02309144,-0.025019692,-2.273933e-05,0.03187319,-0.01981275,0.010352605,0.0013352947,-0.017530434,-0.11532249,0.016914325,0.014281159,0.011462383,0.0003644605,-0.029668687,0.017596329,-0.003293803,0.01830043,-0.014485907,0.011690758,-0.0071206535,-0.0051258514,-0.02465287,0.023420185,-0.005652906,-0.020689465,-0.008991538,0.029668251,0.0095139695,-0.0056139007,-0.006909214,0.0017477759,-0.036803093,-0.022103393,-0.024051726,0.03465217,0.008077255,-0.007910557,-0.02193695,-0.006491447,-0.045683786,0.021250665,0.027882975,-0.011841401,0.009301965,-0.0025125165,-0.011019322,0.0019963686,-0.016321296,-0.029923312,-0.024977122,-0.009597262,-0.016331414,0.015492841,0.0032067527,-0.021600846,0.0067117806,0.010251383,0.0067320587,0.0063420082,0.0029045376,-0.0042992216,0.008644647,0.02064744,0.024054883,-0.033005156,-0.00035015942,0.014424752,0.0043877107,0.005556752,0.012137844,-0.014049314,0.009917248,-0.015063344,0.0137174595,0.025090516,-0.012273544,0.015232332,-0.0074340682,-0.01580339,-0.023412779,0.026313007,0.013699428,-0.012976083,-0.005764733,-0.022267232,0.008361841,-0.0039798934,0.017527372,-0.025995383,0.015837088,0.004496599,-0.010570739,0.020899674,-0.009112784,-0.005120101,0.003921183,-0.04519518,0.017636918,-0.01298122,-0.018682582,-0.023767533,0.013258718,-0.00836271,-0.025816577,0.0018653173,0.0063964888,-0.010488119,0.0072511435,-0.0091440175,-0.01587775,-0.012564738,-0.014451135,-0.0063519003,0.019954735,-0.0008625398,-0.008068339,0.026614808,0.012381884,-0.02385387,-0.0014441021,-0.014379704,0.010126605,0.023721304,0.005096502,0.014818387,0.009133955,0.011432003,-0.0018579926,-0.00026680433,-0.0005489375,0.008220596,-0.0026673481,0.02540467,-0.039632693,0.009201925,0.0008275811,0.003868277,0.033638094,-0.014971566,-0.0064165005,-0.043291535,0.0026013022,0.016262239,-0.026503902,0.02669011,0.012401046,-0.007912509,-0.016202774,-0.014265766,-0.012541118,0.008331753,-0.02812084,0.0070086382,-0.02412718,-0.010799768,-0.021034585,0.009932846,-0.009297116,-0.018471822,0.003124147,0.01172314,-0.008069893,-0.018606875,-0.009091046,0.018492125,-0.018141545,-0.01978159,0.00052192784,0.003218401,-0.04573506,0.04565135,-0.017372802,-0.024469811,-0.001744669,-0.0028499865,-0.0019308428,-0.0072010537,-0.023846632,-0.011505085,-0.024405997,-0.006163234,-0.015589777,-0.0013208911,0.022183644,0.011495278,0.010368505,0.009245584,-0.008331291,-0.018822849,0.009548825,0.031566933,-0.004275636,0.027085233,0.023791343,0.006366075,0.0037693814,0.05254629,-0.019525615,-0.004446883,0.022966279,0.024520203,-0.006247255,-0.002947273,0.014768991,-0.004261076,0.005612864,-0.021847492,-0.0210559,-0.03179254,-0.003934421,-0.007822379,0.019940795,0.013138144,-0.009248873,-0.020290665,-0.016016979,0.014101219,0.0170615,0.0033882638,-0.0034451338,0.0403211,0.017318614,0.0077438927,0.026596788,-0.0007597699,0.035056155,-0.033998944,0.0037547802,-0.009948622,0.00095282844,0.03693982,-0.02579694,0.01809592,-0.0075416914,0.009012857,0.009628567,0.007226048,-0.026033586,0.013325004,0.0057702,-0.010718693,0.0011110549,-0.04731287,0.029342193,-0.0035191015,-0.01758062,-0.019189892,-0.040847573,0.014520585,-0.025090758,-0.015804848,0.023637915,0.008180626,0.012154188,0.020814493,-0.008359752,-0.048119713,0.02986061,-0.011270905,0.008633622,0.008387673,0.00078589097,0.0089540975,-0.008484402,-0.005683141,-0.0020330485,0.0010919741,-0.0022388652,0.011708788,-0.0011597311,0.01281996,0.007913386,-0.0017701634,-0.011156299,0.019835854,-0.031387705,0.0028962241,-0.029214239,0.01607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