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@zhuofengli96475

πŸš€ DCI just hit #1 on Hugging Face Daily Papers! Try it Now! @HuggingPapers https://t.co/h1CWuCtuQz https://t.co/8K2O7zZ7vq

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  "text": "πŸš€ DCI just hit #1 on Hugging Face Daily Papers! Try it Now! @HuggingPapers \n\nhttps://t.co/h1CWuCtuQz https://t.co/8K2O7zZ7vq",
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    "text": "πŸ”₯ Introducing Direct Corpus Interaction (DCI)! The best retriever for agentic search is no retriever.\n\nπŸš€ We replaced the entire agentic search pipeline β€” embedding model, vector index, top-k retrieval β€” with only `grep` and `bash`. πŸ”§\n\nπŸ“„ Paper: https://t.co/9FVvrdLCRf\n\nDCI unlocks the full agentic potential of any Claude Sonnet 4.6: 69.0% β†’ 80.0% on BrowseComp-Plus (+11.0, βˆ’$424).\n\nπŸ’‘The Magic:\nThe agent searches the raw corpus directly β€” `grep`, `find`, `bash`, shell pipelines β€” exactly like a coding agent navigating a codebase. No preprocess. No embedding model. No vector index. No offline indexing.\n\nπŸ“ŠThe Results:\nDCI outperforms top baselines across 13 benchmarks, with average gains of:\nπŸ” Agentic Search: +11.0%\n🧠 Multi-hop QA: +30.7%\nπŸ“ˆ IR Ranking: +21.5%\n\nπŸ’‘ Insights:\nBeyond accuracy, we conduct a series of controlled ablation studies to pinpoint the sources of DCI’s gains. Specifically, we examine trajectory-level search, evidence utilization corpus, context management, and tool usage (RQ2-RQ6).\n\nTry it yourself!\nπŸ› οΈCode: https://t.co/A8ch5QM1E4\nπŸ€– Demo: https://t.co/Y9H4abb2P6\nπŸ”Ž Eval logs: https://t.co/4iM7u1M8mz",
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