Separating Memory and Workflow Effects in Predicting Individual Answers

Abstract

Personalized language agents choose both what to remember about a person and how to use that memory. We separate these choices when predicting unseen answers to known interview questions. On 1,768 tasks from 188 people, a concrete memory built from a verified interview prefix outscores a trait description by 0.0158 (95% whole-person interval [0.0044, 0.0271]). Crossing both memories with one-shot generation and three-answer fusion, fusion lowers concrete-memory scores by 0.0123 ([-0.0189, -0.0056]); prompted and trained selectors do not detectably beat a random candidate. One call on the longer, unrewritten source record outscores every memory condition. Under a limited context budget, OwnWords retrieves the person's sentences with BM25 and answers in one call. It outperforms the written memory on 500 people outside the benchmark (+0.0127, [+0.0037, +0.0217]; an earlier held-out test was inconclusive) and across four budgets on 300 people (mean +0.0218, [+0.0138, +0.0298]), with the latter result repeated on 114 people. It does not detectably outperform recency truncation. These results compare evidence-construction procedures; they do not isolate the effect of verbatim wording. On Twin-2K-500, OwnWords predicts ordinal survey answers more closely than the written memory, but does not improve exact-choice accuracy and lowers it in one of two samples. Interview scores use a model-based content rubric without human ratings, and the original benchmark's participants were seen during development. These results characterize the tested procedures, not a general human-prediction ceiling.