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A workshop/tutorial on agentic search techniques for context engineering, teaching how AI agents decide what context to retrieve from files, databases, memory, and the web using langchain and Elasticsearch.
The paper introduces BRIGHT-Pro, a new benchmark for reasoning-intensive retrieval, and RTriever-Synth, a synthetic corpus used to fine-tune RTriever-4B for improved performance in agentic search systems.
The paper introduces Direct Corpus Interaction (DCI), a novel approach allowing AI agents to query raw text directly using standard terminal tools instead of traditional embedding-based retrieval. By bypassing fixed similarity interfaces and offline indexing, DCI significantly outperforms conventional sparse, dense, and reranking baselines across multiple IR and agentic search benchmarks.