Grounded Personal Memory Retrieval: A Location-Aware AI Assistant with Sentence-Level Verification for Cognitive Support

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@article{1724521,
      author = {Aadesh Rathod, Shriya Mishra, Purva Dalvi, Siddhi Deshmukh, Dr. Ramesh Shahabade},
      title = {Grounded Personal Memory Retrieval: A Location-Aware AI Assistant with Sentence-Level Verification for Cognitive Support},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {77-86},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724521.pdf},
      abstract = {People with mild cognitive impairment and early Alzheimer’s disease often cannot recall recent personal experiences. Conversational assistants built on large language models could help, but they carry a specific risk for this population: a user with memory impairment cannot check an answer against their own memory, so an unsupported statement may be accepted as a real memory. This paper presents a phone and web memory assistant that retrieves a user’s own recorded experiences by place and by plain-language question while constraining every answer to those records. Photographs, voice notes, and text notes are time-stamped and grouped into named places using geohash prefix search. Questions pass through a retrieval pipeline that refuses before any model call when nothing relevant exists, requires each drafted sentence to cite a retrieved record, and filters every sentence through a verification component that removes uncited, unretrieved, private, medical, or lexically unsupported content. Caregiver access is consent-based, deny-by-default, and visible to the user. We define six measurable grounding requirements and evaluate them on a synthetic persona with 32 records and 48 questions, without patient data. In offline mode, every statement was traceable to a retrieved record, no unsupported statement was produced, 9 of 10 unanswerable questions were refused, and all 28 answerable questions cited a correct record. We discuss the limitations of this evaluation and the work needed before clinical use.},
      keywords = {Alzheimer’s disease; assistive technology; grounding; hallucination; memory aids; mild cognitive impairment; retrieval-augmented generation},
      month = {November},
      doi = {https://doi.org/10.64388/ire1724521}
  }