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25년 회고

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Pangyoalto

Dec 25, 2025 • 7 min read
25년 회고
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[Paper review] REFRAG: Encoding/Decoding for RAG Optimization

[Paper review] REFRAG: Encoding/Decoding for RAG Optimization

This article reviews the paper REFRAG: Rethinking RAG based Decoding. RAG is one of the first methods considered when applying LLMs to services. The key advantage of RAG is its ability to leverage domain knowledge without model fine-tuning. However, as the knowledge base grows, longer contexts must be fed as
Dec 20, 2025 9 min read
[논문 리뷰] REFRAG: RAG 최적화를 위한 인코딩/디코딩

[논문 리뷰] REFRAG: RAG 최적화를 위한 인코딩/디코딩

이 글은 논문 REFRAG: Rethinking RAG based Decoding 을 리뷰합니다. RAG는 서비스에 LLM을 적용할 때 가장 먼저 검토하는 방법 중 하나입니다. 모델 파인튜닝 없이도 도메인 지식을 활용할 수 있다는 것이 RAG의 핵심 장점이죠. 하지만 활용할 지식이 많아질수록 입력으로 더 긴 컨텍스트를 넣어야 하고, 이는 높은 레이턴시와 메모리 소비를 발생시킵니다. 실제
Dec 20, 2025 17 min read
[Paper review] Vector Compression Method for HNSW – Flash

[Paper review] Vector Compression Method for HNSW – Flash

This post reviews the paper Accelerating Graph Indexing for ANNS on Modern CPUs. To fully understand this paper, familiarity with HNSW and Product Quantization is helpful. HNSW is the most widely used algorithm for vector search. Since the HNSW paper was published in 2016, it has now been in use
Sep 1, 2025 15 min read
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