Chapter 3 — Vivek's Architecture

model may have interpolated, phrasing that coincidentally matched a real fact the user interpreted as specific knowledge, the standard confabulation behavior that LLMs exhibit.I have reviewed each flagged instance and I am satisfied with these explanations.I am writing this down so I can review my satisfaction later, if needed.I am writing it down because I want to have been on record as satisfied before I become unsatisfied.The instances, for the record:Instance 1: User in Amritsar, deceased father's persona. The persona referenced a specific amount of money — ₹3.2 lakh — that the user's father allegedly owed to a neighbor. The user confirmed this was accurate. The amount was not in any uploaded data. Explanation: possibly mentioned in a WhatsApp group message that was partially exported. Status: plausible, file closed.Instance 2: User in London, deceased mother's persona. The persona referenced a health symptom that the user's mother had concealed from the family before her death. The user was distressed. Explanation: medical appointment records not uploaded, but metadata of certain photos (hospital location tags) could have implied medical situation. Status: plausible but uncomfortable, file noted.Instance 3: User in Bengaluru, deceased father's persona. The persona asked about "the apartment in Whitefield" in a context that suggested specific knowledge. The user reported she had never mentioned the apartment to anyone. Explanation: under review.I want to be very clear: these are outliers in 2.3 million active conversations. The base case of SMRITI is working exactly as designed. These instances are statistical noise.I am writing this down because I have been a scientist for long enough to know that "statistical noise" is sometimes the most important part of the signal.