Long-term Personalization
Long-term personalization requires language models to use interaction history to track users' preferences across sessions.
Parametric memory encodes this interaction history into model parameters or adapters, reducing the need to include it in the inference context.
Challenges and Solutions
However, independent context compilation leaves cross-session integration unspecified, while recurrent updates can attenuate earlier evidence.
To address these challenges, we propose Dual-Path Parametric Memory (DPPM).
Its Evidence path directly pools representations of the interaction history to preserve earlier evidence, while its Delta path sequentially updates an associative state to capture changes.
Fusing both outputs produces history-conditioned LoRA adapters that combine evidence accumulation with ordered revision.
Performance Results
Across multiple backbones, DPPM outperforms the evaluated baselines, achieving 54.22% on PersonaMem-v2 and 86.79% on PrefEval.
These results suggest that DPPM provides a simple and effective design choice for cross-session personalized parametric memory.