{"id":"638c5631-90a2-488c-9969-6e3204351d8e","kind":"companies","code":"UB-C12383","name":"Guiyin ・CauseLink","nameBasis":"translated","summary":"Meta finally lays its cards on the table","description":"","country":null,"category":null,"originalName":"归因・CauseLink","eventDate":null,"website":"https://linusshyu.dev/","sourceUpdatedAt":"2026-10-26T00:00:00.000Z","englishUpdatedAt":"2026-11-01T02:20:34.488Z","reviewedAt":"2026-11-01T02:20:34.488Z","snapshotAt":"2026-11-01T00:40:31.077Z","sourceHash":"e17356d9f18e9d1405ff8c99f9e8e0151a8a0cb561b2f8c49f4e69ea8678696f","companyId":null,"foundedYear":null,"logoUrl":null,"originalText":"Meta 彻底摊牌","sources":[],"related":[{"id":"366a92cf-22ab-42c7-a069-8125e2d198cc","kind":"products","code":"UB-D6991","name":"Guiyin ・CauseLink","nameBasis":"transliterated","summary":"Project background description: There are three obvious pain points in dealing with existing wrong questions: students still don’t know how to use them after sorting out the wrong questions, and are prone to \"making the same mistake again and again\"; it is difficult for teachers to quickly locate weak points in the class after correction, and there is a lack of accurate teaching basis; the school also lacks scientific academic data support, and teaching management still relies on experience. Tools such as Zuoyebang and Xiaoyuan Question Search focus more on answers and analysis, and less on tracking the underlying reasons such as lack of understanding of concepts, inability to use methods, and broken ideas. CauseLink therefore hopes to turn a wrong question on an exam into an explainable and actionable personal learning plan. The core highlight combines answer sheet scanning and exam wrong questions, and AI restores students' mastery of knowledge points in each section and generates a personal \"wrong question distribution map.\" It not only marks \"which question is wrong\", but also analyzes the underlying reasons behind the loss of points. For example, the loss of points for big questions comes from insufficient practice, unfamiliar methods or repeated calculation errors. Generate a \"personalized exam strategy\" based on the attribution results, turning abstract suggestions into executable answer sequences and choices for the next exam. Based on the attribution of wrong questions and blanks, mind maps and review documents of the knowledge points tested in this test paper are automatically generated. At the same time, it provides students, teachers and schools with three levels of value: personal review, class weakness and academic data. Project Growth and Reflection Growth and Reflection: This project made me understand that educational products cannot stop at “giving answers”, but must connect data collection, cause judgment and follow-up actions into a closed loop.","description":"Project background description: There are three obvious pain points in dealing with existing wrong questions: students still don’t know how to use them after sorting out the wrong questions, and are prone to \"making the same mistake again and again\"; it is difficult for teachers to quickly locate weak points in the class after correction, and there is a lack of accurate teaching basis; the school also lacks scientific academic data support, and teaching management still relies on experience. Tools such as Zuoyebang and Xiaoyuan Question Search focus more on answers and analysis, and less on tracking the underlying reasons such as lack of understanding of concepts, inability to use methods, and broken ideas. CauseLink therefore hopes to turn a wrong question on an exam into an explainable and actionable personal learning plan. The core highlight combines answer sheet scanning and exam wrong questions, and AI restores students' mastery of knowledge points in each section and generates a personal \"wrong question distribution map.\" It not only marks \"which question is wrong\", but also analyzes the underlying reasons behind the loss of points. For example, the loss of points for big questions comes from insufficient practice, unfamiliar methods or repeated calculation errors. Generate a \"personalized exam strategy\" based on the attribution results, turning abstract suggestions into executable answer sequences and choices for the next exam. Based on the attribution of wrong questions and blanks, mind maps and review documents of the knowledge points tested in this test paper are automatically generated. At the same time, it provides students, teachers and schools with three levels of value: personal review, class weakness and academic data. Project Growth and Reflection Growth and Reflection: This project made me understand that educational products cannot stop at “giving answers”, but must connect data collection, cause judgment and follow-up actions into a closed loop.","country":null,"category":"Knowledge management","originalName":"归因・CauseLink","eventDate":null,"website":null,"sourceUpdatedAt":"2026-10-27T05:00:18.770Z","englishUpdatedAt":"2026-10-28T00:56:06.838Z","reviewedAt":"2026-10-28T00:56:06.838Z","snapshotAt":"2026-10-28T00:47:51.550Z","sourceHash":"72011dd8201d76d3cee2490650fe1895ae5f987afc89c444b572fefbd5521c2f","companyId":"638c5631-90a2-488c-9969-6e3204351d8e","foundedYear":null,"logoUrl":null,"path":"/products/UB-D6991"}],"finance":{"funding":{"metric":"funding","count":0,"latest":null,"years":[],"points":[],"linePoints":[],"conflictGroups":0,"omittedRecordCount":0,"records":[],"totalRaised":null},"valuation":{"metric":"valuation","count":0,"latest":null,"years":[],"points":[],"linePoints":[],"conflictGroups":0,"omittedRecordCount":0,"records":[]},"revenue":{"metric":"revenue","count":0,"latest":null,"years":[],"points":[],"linePoints":[],"conflictGroups":0,"omittedRecordCount":0,"records":[]}},"events":[],"team":[],"productsRelated":[{"id":"366a92cf-22ab-42c7-a069-8125e2d198cc","kind":"products","code":"UB-D6991","name":"Guiyin ・CauseLink","nameBasis":"transliterated","summary":"Project background description: There are three obvious pain points in dealing with existing wrong questions: students still don’t know how to use them after sorting out the wrong questions, and are prone to \"making the same mistake again and again\"; it is difficult for teachers to quickly locate weak points in the class after correction, and there is a lack of accurate teaching basis; the school also lacks scientific academic data support, and teaching management still relies on experience. Tools such as Zuoyebang and Xiaoyuan Question Search focus more on answers and analysis, and less on tracking the underlying reasons such as lack of understanding of concepts, inability to use methods, and broken ideas. CauseLink therefore hopes to turn a wrong question on an exam into an explainable and actionable personal learning plan. The core highlight combines answer sheet scanning and exam wrong questions, and AI restores students' mastery of knowledge points in each section and generates a personal \"wrong question distribution map.\" It not only marks \"which question is wrong\", but also analyzes the underlying reasons behind the loss of points. For example, the loss of points for big questions comes from insufficient practice, unfamiliar methods or repeated calculation errors. Generate a \"personalized exam strategy\" based on the attribution results, turning abstract suggestions into executable answer sequences and choices for the next exam. Based on the attribution of wrong questions and blanks, mind maps and review documents of the knowledge points tested in this test paper are automatically generated. At the same time, it provides students, teachers and schools with three levels of value: personal review, class weakness and academic data. Project Growth and Reflection Growth and Reflection: This project made me understand that educational products cannot stop at “giving answers”, but must connect data collection, cause judgment and follow-up actions into a closed loop.","description":"Project background description: There are three obvious pain points in dealing with existing wrong questions: students still don’t know how to use them after sorting out the wrong questions, and are prone to \"making the same mistake again and again\"; it is difficult for teachers to quickly locate weak points in the class after correction, and there is a lack of accurate teaching basis; the school also lacks scientific academic data support, and teaching management still relies on experience. Tools such as Zuoyebang and Xiaoyuan Question Search focus more on answers and analysis, and less on tracking the underlying reasons such as lack of understanding of concepts, inability to use methods, and broken ideas. CauseLink therefore hopes to turn a wrong question on an exam into an explainable and actionable personal learning plan. The core highlight combines answer sheet scanning and exam wrong questions, and AI restores students' mastery of knowledge points in each section and generates a personal \"wrong question distribution map.\" It not only marks \"which question is wrong\", but also analyzes the underlying reasons behind the loss of points. For example, the loss of points for big questions comes from insufficient practice, unfamiliar methods or repeated calculation errors. Generate a \"personalized exam strategy\" based on the attribution results, turning abstract suggestions into executable answer sequences and choices for the next exam. Based on the attribution of wrong questions and blanks, mind maps and review documents of the knowledge points tested in this test paper are automatically generated. At the same time, it provides students, teachers and schools with three levels of value: personal review, class weakness and academic data. Project Growth and Reflection Growth and Reflection: This project made me understand that educational products cannot stop at “giving answers”, but must connect data collection, cause judgment and follow-up actions into a closed loop.","country":null,"category":"Knowledge management","originalName":"归因・CauseLink","eventDate":null,"website":null,"sourceUpdatedAt":"2026-10-27T05:00:18.770Z","englishUpdatedAt":"2026-10-28T00:56:06.838Z","reviewedAt":"2026-10-28T00:56:06.838Z","snapshotAt":"2026-10-28T00:47:51.550Z","sourceHash":"72011dd8201d76d3cee2490650fe1895ae5f987afc89c444b572fefbd5521c2f","companyId":"638c5631-90a2-488c-9969-6e3204351d8e","foundedYear":null,"logoUrl":null,"path":"/products/UB-D6991"}],"investors":[],"path":"/companies/causelink","structured":{"city":null,"country":null,"location":null,"role":null,"category":null,"company":null},"definition":"Guiyin ・CauseLink is a company.","summaryBasis":"translated","thin":false,"canonical":"https://uniquebase.ai/companies/causelink"}