CAPA: A Context-Aware Agent for Continual Personalized Photo Retouching

A continual photo-retouching agent must use personal feedback across images while preserving scene-specific preferences and temporary intent. We introduce Context-Aware Preference Agent (CAPA), which connects feedback use, acquisition, and retention through hierarchical preference states and an axis-level evidence ledger. Candidate contrasts represent each choice relative to the alternatives shown. Responses adapt the current session immediately, while the ledger tracks image, session, and scope information for persistent promotion. Remaining-axis masks let eligible coordinates enter long-term memory while preserving the others for later promotion; each coordinate is consumed once. CAPA with Decision Value (CAPA-DV) uses personal response probabilities and semantic candidate partitions to select questions and follow-ups within an interaction budget. In chronological replay of data from 31 participants, hierarchical memory achieves 44.23% candidate-selection Top-1 accuracy versus 40.26% for flat memory using the same history. In fixed-prefix replay with choice-consistent answers, one-step CAPA-DV reaches 80.14% versus 67.75% for fixed coverage, using 326 versus 452 question exposures. Synthetic tests further characterize answer-noise sensitivity and retention under temporary perturbations. CAPA links current editing intent to comparative evidence and its persistent reuse in a continual personalization workflow.