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1.
How LinkedIn replaced five feed retrieval systems with one LLM model, at 1.3 billion-user scale (venturebeat.com)
2.
Agents need vector search more than RAG ever did (venturebeat.com)
3.
Enterprises are measuring the wrong part of RAG (venturebeat.com)
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This tree search framework hits 98.7% on documents where vector search fails (venturebeat.com)
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Why MongoDB thinks better retrieval — not bigger models — is the key to trustworthy enterprise AI (venturebeat.com)
6.
Databricks' Instructed Retriever beats traditional RAG data retrieval by 70% — enterprise metadata was the missing link (venturebeat.com)
7.
GAM takes aim at “context rot”: A dual-agent memory architecture that outperforms long-context LLMs (venturebeat.com)
8.
From shiny object to sober reality: The vector database story, two years later (venturebeat.com)
9.
Energy and memory: A new neural network paradigm (sciencedaily.com)
10.
Agentic RAG: Embedding Autonomous Agents into Retrieval-Augmented Generation (computer.org)
11.
Rerank-2.5 and rerank-2.5-lite: instruction-following rerankers (news.ycombinator.com)
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rerank-2.5 and rerank-2.5-lite: instruction-following rerankers (news.ycombinator.com)
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Beyond Retrieval: The Expanding Universe of Augmented Generation in AI (computer.org)
14.
Muvera: Making multi-vector retrieval as fast as single-vector search (news.ycombinator.com)
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