ECHOES: An Adaptive LLM-Powered AR Glasses Docent for Evidence-Grounded Urban Heritage Interpretation
Abstract: Urban heritage tours are often delivered through fixed scripts, leaving limited room for visitors' situated questions, language preferences, or requests for evidence. We present ECHOES, an adaptive LLM-powered AR glasses docent for evidence-grounded interpretation of historical buildings. Specifically, the agent adapts along three dimensions: it modifies response structure according to the type of visitor question (identification, architectural detail, evidence-seeking, or route inquiry); it accumulates session-level interest signals from repeated questions and expressed curiosity to shape later explanations; and it tracks language preference and retrieval context across turns to maintain conversational continuity. The core of ECHOES is an LLM agent initialized as a source-aware urban heritage docent that realizes this adaptation through dialogue and preference context maintained across the session. Around this agent, ECHOES integrates visual site grounding, a self-built historical knowledge base supporting retrieval-augmented generation, and concise multilingual AR output designed for glanceable use. Through a working prototype centered on Shanghai's East Nanjing Road and the Bund, we demonstrate how an adaptive LLM-powered AR docent can transform historical tours from scripted narration into situated, question-responsive, and source-aware conversations.