🔎 A provocative paper: "agentic search needs no embeddings or vector DB—just grep the raw corpus directly." Title: Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction URL: ❓ What is Direct Corpus Interaction (DCI)? 💡 A retrieval paradigm where the agent explores the raw corpus directly via grep, find, and shell commands—no embedding model, vector index, or retrieval API. It needs no offline indexing and adapts naturally to evolving local corpora. ❓ Why aren't conventional retrievers enough? 💡 Sparse or dense, they compress the corpus through a fixed similarity interface into top-k before reasoning begins. That makes exact lexical matching, combining weak clues, and checking local context hard—and evidence filtered out early can't be recovered no matter how strong the downstream reasoning. That's fatal for multi-step agent search. ❓ Can it really win without a retriever? 💡 Yes. On BrowseComp-Plus, swapping the retriever for DCI on the same Sonnet 4.6 lifts accuracy 69.0 → 80.0% (+11.0) while cutting cost 29.4%. Multi-hop QA averages 83.0 (+30.7 over the strongest baseline); IR ranking hits 68.5 NDCG@10 (+21.5). Even the lightweight GPT-5.4 nano version beats many baselines. ❓ What's actually driving it? 💡 The authors call it "retrieval interface resolution." Trajectory analysis shows the edge comes less from surfacing more gold documents and more from high-resolution local search and verification after reaching them. #AIAgents# #RAG#