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Readyset 的这篇技术博客深入讲解其查询转换管线如何将 SQL 重写为数据流图——包括二元等值连接、非关联子查询改写以及派生表内联等约束与优化策略，以便增量维护物化视图。
This paper introduces a self-evolving framework that uses an LLM-based agent to iteratively create and refine query rewriting rules for BM25 in legal case retrieval, outperforming non-evolutionary baselines on the LeCaRD-v2 benchmark without any parameter training.
This paper describes a system for SemEval-2026 Task 8 that uses a three-stage pipeline involving query rewriting with a fine-tuned Qwen model, hybrid retrieval, and cross-encoder reranking to improve multi-turn retrieval performance.
Skill-RAG is a failure-aware RAG framework that uses hidden-state probing and skill routing to diagnose and correct query-evidence misalignment in retrieval-augmented generation. The approach detects retrieval failures and selectively applies targeted skills (query rewriting, question decomposition, evidence focusing) to improve accuracy on hard cases and out-of-distribution datasets.