# Agentic Retrieval-Augmented Generation

A single triple-axis neutron measurement draws on papers, instrument logs, data files and specialist software, and that evidence was previously scattered across separate stages with no traceable link. TASCopilot records sources, processing steps and human review decisions as an inspectable evidence chain, while language models only extract information and explain results; scientific calculations, validation rules and human review decide whether evidence can support a claim. On the BT-7 spectrometer at the NIST Center for Neutron Research it recovered a sample rotation deliberately parked 40° away to within 0.08° of the orientation-matrix prediction, and completed 40 adaptive measurement points with all 40 counted and no refusals.

A single triple-axis neutron measurement draws on papers, instrument logs, data files and specialist software, and that evidence was previously scattered across separate stages with no traceable link. TASCopilot records sources, processing steps and human review decisions as an inspectable evidence chain, while language models only extract information and explain results; scientific calculations, validation rules and human review decide whether evidence can support a claim. On the BT-7 spectrometer at the NIST Center for Neutron Research it recovered a sample rotation deliberately parked 40° away to within 0.08° of the orientation-matrix prediction, and completed 40 adaptive measurement points with all 40 counted and no refusals.

In multimodal long-document question answering, agentic retrieval is not always worth its extra computation and can even hurt answer accuracy. RouteDRAG makes both whether to invoke an agent and how it then executes into learned choices: a graph-backed index organizes cross-page, table and figure content, path routing predicts the benefit of an agent over shorter paths, and dynamic execution controls tool use and termination. Across two benchmarks it assigns only 79.3% of queries to an agent while averaging 3.47 percentage points higher accuracy than shorter-path-only execution, and it also surpasses uniform agent execution with fewer answering calls.

In multimodal long-document question answering, agentic retrieval is not always worth its extra computation and can even hurt answer accuracy. RouteDRAG makes both whether to invoke an agent and how it then executes into learned choices: a graph-backed index organizes cross-page, table and figure content, path routing predicts the benefit of an agent over shorter paths, and dynamic execution controls tool use and termination. Across two benchmarks it assigns only 79.3% of queries to an agent while averaging 3.47 percentage points higher accuracy than shorter-path-only execution, and it also surpasses uniform agent execution with fewer answering calls.

After GeneNetwork's 1TB structured database was remodeled as an RDF graph alongside a literature corpus, a system that runs document, graph and agentic retrieval-augmented generation in parallel and merges their answers raised the satisfaction rate from 36% for plain document retrieval to 86% on 40 expert questions, while delegating most work to a local model still reached 59%.

After GeneNetwork's 1TB structured database was remodeled as an RDF graph alongside a literature corpus, a system that runs document, graph and agentic retrieval-augmented generation in parallel and merges their answers raised the satisfaction rate from 36% for plain document retrieval to 86% on 40 expert questions, while delegating most work to a local model still reached 59%.