. . . . "Reason-Align-Respond: Aligning LLM Reasoning with Knowledge Graphs for KGQA" . . . . . . . . . . . . . . . . "EWEK-QA" . . "EffiQA" . . "GCR" . . "GNN-RAG+RA" . . "GraftNet" . . "KD-CoT" . . "NSM" . . "ReaRev" . . . "RAR is a novel framework that systematically integrates LLM reasoning with knowledge graphs for Knowledge Graph Question Answering (KGQA). It employs three fine-tuned LLM modules (Reasoner, Aligner, Responser) and an iterative Expectation-Maximization (EM) algorithm to jointly refine human-like reasoning chains and their corresponding KG paths, thereby reducing hallucinations and enhancing interpretability. This approach synergizes LLMs and KGs by using LLMs as agents to interact with and ground reasoning in KGs." . "Reason-Align-Respond (RAR)" . . . "RoG" . . "SR+NSM" . . "StructGPT" . . "ToG" . . . "2026-02-26T16:21:28.925Z"^^ . . . "LLM-KG assessment for paper 10.48550/arXiv.2505.20971" . "RSA" . "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAwNz2QK3SEifno78S7+48zUB0xpTex3mAzW73ZimHqNcdEMU5/apslrGrTHGFAt/Chocgo++r6JQp5ygY7NyJHGWdaIqnt85pjX4PbNfLAvapyUO00qZP34fY61w4eZ9UMtleWEsmZKRtQPyJ8ODl46i/rfPuZlcJGpM9Nmy5mpGWuepqIEvF4a/t7pLVeCEDFSYXT+yaiygt6ynIK5f7TtEDhZpeUf/Q74WhMPJXm4yTU/hqOX4IW+50kWHNArGGZwUaXwzyG6M3Zd6UMModryGkLqS4H/MSE3ZA1Ylnms7BfWLEXhMWlaKi6HRV4nGRDLhxVSi9LSRi3LWKLhNIIQIDAQAB" . "IuQksjsIy9oxDzBF38WIV1zozGRmHFfjY8PjdzAHY9v9R+pvY0KkZHZoAvbBSNHkpgYo8hIbCvjxXyNhytUt7zqcv21cUur4/Q39TzYz6fX54rLfb6gi5jZ1dDXS+/daD6z7HJpEAHrhh23HD4pHfo1z9lMpX8XS61q5B7CrC+VIXSRLFonMtubicDcrVichgntOpjULlaNXZEI1BOyyq07zFZQn/gaO+Yb2cXrg7uyzamk5B5XyvnxcrOigggL6zL47xu8dySGUHRRy1a2xSgItVUD18NQOE3s7YI8k48XvaCU6vW6bD1t9gvbTxP4aou5RB/jAEuQjNn4y5ZLSFQ==" . . .