While meta-analytic research is performed, it becomes time-consuming to filter through the sheer amount of sources made available by individual databases and search engines and therefore degrades the specificity of source analysis. This study sought to predict the feasibility of a research-oriented searching algorithm across all topics and a search technique to combat flaws in dealing with large datasets by automating three key components of meta-analysis: a query-based search associated with the intended research topic, selecting given sources and determining their relevance to the original query, and extracting applicable information including excerpts and citations. The algorithm was evaluated using 5 key historical topics, and results were broken down into 4 categories: the total number of relevant sources retrieved, the efficiency given a particular search, the total time it takes to finish a complete cycle, and the quality of the extracted sources when compared to results from current searching methods. Although results differed through several searches, on average, the program collected a total of 126 sources per search with an average efficiency of 19.55 sources per second which, when compared and qualitatively evaluated for definitive results, indicates that an algorithm developed across all subject areas will make progress in future research methods.
With Argus Eyes: Assessing Retrieval Gaps via Uncertainty Scoring to Detect and Remedy Retrieval Blind Spots
Lucerne University of Applied Sciences and Arts (HSLU)（卢塞恩应用科学与艺术大学）；Center for Information and Language Processing (CIS), Ludwig Maximilian University of Munich (LMU)（信息与语言处理中心（CIS），慕尼黑路德维希-马克西米利安大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
RAG Deserves an Index: Why Ingest-Time Compilation Beats Query-Time Interpretation
Endgame Labs, Inc.（终局实验室公司）；Asia AI Institute（亚洲人工智能研究院）；Musashino University（武藏野大学）；Faculty of Data Science, Musashino University（武藏野大学数据科学学院）；AIx, Inc.（AIx公司）
Fighting Numerical Hallucinations via Data-centric Compilation for Online Financial QA
Shenzhen Technology University（深圳科技大学）；FiT, Tencent（腾讯金融科技部）；South China University of Technology（华南理工大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Huazhong University of Science and Technology（华中科技大学）；McGill University（麦吉尔大学）
