Where do retrieval systems fail and why?
Explores the core architectural limits of RAG retrieval—from vocabulary mismatches between queries and documents to mathematical constraints in embedding spaces—and what causes failures in production systems.
How should retrieval and reasoning integrate in RAG systems? How should systems retrieve and reason with external knowledge? Do vector embeddings actually measure task relevance? When should retrieval happen during model generation? Can long-context LLMs replace retrieval-augmented generation systems?
Retrieval and reasoning integrate most effectively through MDP formulation and process-level supervision. RAG systems need adaptive retrieval tightly coupled with reasoning. Vector embeddings measure semantic association, not task relevance Uncertainty-gated retrieval outperforms fixed interval retrieval Long-context LLMs replace RAG for semantic tasks but fail on structured queries
What makes dot product efficient for real-time retrieval over millions of items? How severely do minimal corpus modifications damage RAG accuracy in practice? Why does bidirectional RAG amplify the risk of corpus poisoning attacks? What makes dense retrievers vulnerable to partition-based poisoning exploitation? Do Doc2Query approaches suffer from the same misaligned-target problem? How do aspect-aware retrieval and surrogate models compare as explainability approaches? Do retrieval-augmented memory systems actually solve the compartmentalization problem? How does uncertainty-gated retrieval compare to continuous retrieval efficiency? Why does visual similarity retrieval fail for embodied agents? Can task-aware ranking replace similarity scoring in other RAG systems? What makes reranking during retrieval better than catching failures at plan time? Why does retrieval quality sometimes conflict with final answer quality?See all 128 inquiring lines on this note →