EgoCITE: Situation-Aware Context-Augmented Indexing and Retrieval for Long-Horizon Egocentric Memory

Long-horizon egocentric memory requires agents to recover relevant experiences from days of continuous first-person video and audio. Existing systems typically store isolated video captions or speech transcripts and retrieve them using raw user queries. We find that this pipeline loses the situational context needed for reliable memory search: stored fragments are not self-contained, complementary descriptions of the same experience remain disconnected, and queries often express temporal intent only implicitly. We introduce EgoCITE, a situation-aware framework that contextualizes both memory storage and retrieval for long-horizon egocentric question answering. EgoScheme uses local multimodal context to transform fragmentary captions and transcripts into self-contained atomic memories. EgoIndex organizes complementary action, activity, utterance, and conversation representations into multi-view, multi-granularity indices that capture each situation from different perspectives. EgoRetrv interprets the situation implied by a question through question-conditioned temporal relevance scoring, then iteratively retrieves and curates matching evidence. We evaluate EgoCITE on EgoLifeQA, EgoMem, and EgoR1-Bench using answer accuracy and target-event retrieval alignment. Across the three benchmarks, EgoCITE improves average answer accuracy over the strongest agentic memory baselines by 4.4–14.2% while achieving 36$\times$ lower normalized cost than long-context LLM agents.