@zhuofengli96475
π 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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π 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": "π 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": "π₯ Introducing Direct Corpus Interaction (DCI)! The best retriever for agentic search is no retriever.\n\nπ We replaced the entire agentic search pipeline β embedding model, vector index, top-k retrieval β with only `grep` and `bash`. π§\n\nπ 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, β$424).\n\nπ‘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\nπThe Results:\nDCI outperforms top baselines across 13 benchmarks, with average gains of:\nπ Agentic Search: +11.0%\nπ§ Multi-hop QA: +30.7%\nπ IR Ranking: +21.5%\n\nπ‘ Insights:\nBeyond accuracy, we conduct a series of controlled ablation studies to pinpoint the sources of DCIβs gains. Specifically, we examine trajectory-level search, evidence utilization corpus, context management, and tool usage (RQ2-RQ6).\n\nTry it yourself!\nπ οΈCode: https://t.co/A8ch5QM1E4\nπ€ Demo: https://t.co/Y9H4abb2P6\nπ Eval logs: https://t.co/4iM7u1M8mz",
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