Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models
Long-horizon personalization requires dialogue assistants to retrieve user-specific facts from extended interaction histories. In practice, many relevant facts often have low semanticsimilarity to the query under dense retrieval. Standard Retrieval-Augmented Generation (RAG) and GraphRAG systems are still largely retrospective: they rely on embedding similarity to the query or on fixed graph traversals, so they often miss facts that matter for the user's needs but lie far from the query in embedding space. Inspired by prospection, the human ability to use imagined futures as cues for recall, we introduce Prospection-Guided Retrieval (PGR), which decouples retrieval from how memories are s...
| Authors | Harshita Chopra, Krishna Kant Chintalapudi, Suman Nath et al. |
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| Primary category | cs.IR |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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