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Extensive offline evaluation, human calibration, and online A/B tests show that SARA-7B generates more specific, polarity-consistent, and grounded rationales than strong MLLM baselines, while SARA-Ranker improves engagement and reduces negative feedback in production.
Behavior-derived rewards enable MLLMs to generate effective recommendations without user-specific data at inference, outperforming traditional methods.
Existing staypoint detection algorithms falter in noisy environments, but new unsupervised methods show promise for substantial improvements.
Advisor performance paradoxically suffers most when personal AI is used moderately, highlighting the complex strategic interactions introduced by personal AI assistants.
