bge-reranker-v2-m3
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Headlines · 2
- Building Domain Agents from RAG to Agentic Research
This tutorial presents a progression from basic RAG to domain-specific agents and deeper research workflows. It covers document ingestion, chunking, embedding, hybrid retrieval, reranking, dynamic routing, ReAct loops, query planning, evidence gathering, memory, and answer verification, with a travel-policy example throughout. The article also compares RAG with fine-tuning and frames agentic search, integrated research, and full-stack AI research as progressively higher-complexity deliverables.
掘金 · 人工智能 · 🔥 3 - Glance lets a generator rerank its own retrieved passages
Adilet Metinov and Chingiz Arziev introduce Glance, a generator-based reranking method that adds a lightweight decision adapter and pointer head to score candidate passages from a shared prefill. The paper reports nDCG@10 of 0.741 on SciFact and 0.345 on NFCorpus after 39 minutes of MS MARCO fine-tuning, plus a 0.045 answer-F1 improvement on 300 HotpotQA questions, but evaluation remains limited to two retrieval datasets, one QA dataset and one backbone family.
Hugging Face · Papers · 🔥 0
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