Summary
This arXiv paper (2605.15184) presents an empirical study of how retrieval strategy choice interacts with agent architecture and tool-calling paradigms in LLM-based agentic search. The authors—Sahil Sen, Akhil Kasturi, Elias Lummer, Anmol Gulati, and Vamse Kumar Subbiah—compare grep-style keyword retrieval against vector retrieval in two experiments. The study highlights under-explored practical dimensions such as how tool outputs are presented to the model and how performance degrades when searches must cope with increasing amounts of irrelevant surrounding text. The paper addresses a gap in the RAG and agentic search literature, which lacks a systematic comparison of retrieval strategies within agent loops. Posted on zhichai.net in the NLP category, the paper is relevant to researchers and engineers building retrieval-augmented generation systems and autonomous LLM agents that retrieve information, call tools, and reason over large corpora.
Paper Overview
Research Area: NLP
Authors: Sahil Sen, Akhil Kasturi, Elias Lumer, Anmol Gulati, Vamse Kumar Subbiah
Published: 2026-05-14
arXiv: 2605.15184
Original Abstract (translated excerpt)
Recent advances in Large Language Model (LLM) agents have enabled complex agentic workflows where models autonomously retrieve information, call tools, and reason over large corpora to complete tasks on behalf of users. Despite the growing adoption of retrieval-augmented generation (RAG) in agentic search systems, existing literature lacks a systematic comparison of how retrieval strategy choice interacts with agent architecture and tool-calling paradigm. Important practical dimensions, including how tool outputs are presented to the model and how performance changes when searches must cope with more irrelevant surrounding text, remain under-explored in agent loops. This paper reports an empirical study organized into two experiments. Experiment 1 compares grep and vector retrieval on a 11...
*(Abstract excerpt truncated at source; see the arXiv page for the full text.)*
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*Auto-collected on 2026-05-15*
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