PAW-Mem: Predict, Adapt, and Write for Efficient Online Associative Memory

Large language models increasingly need to retain and reuse information across long interaction histories, yet repeatedly carrying the entire history in the context window incurs growing memory and computation costs. Compact online associative memory offers an appealing alternative by compressing the past into a fixed-size recurrent state. Under aggressive compression, however, each persistent update must preserve useful history while incorporating an entire new segment, and intermediate memory states receive only indirect supervision from the final task objective. We introduce PAW-Mem (Predict, Adapt, and Write), an online associative-memory framework that addresses both issues. Predict uses the pre-write memory state to autoregressively predict the current segment, providing Forward Dynamics supervision along the memory trajectory. Adapt maintains a diagonal online scaling state that adjusts the write geometry across memory dimensions. Write then compresses token-level representations and performs one persistent associative update per segment. With a frozen Qwen3-4B backbone, PAW-Mem improves LoCoMo from 46.73 for the original $\delta$-Mem to 50.02 while preserving competitive performance on HotpotQA and IFEval. Controlled ablations show complementary gains from Online Scaling and Forward Dynamics, and experiments with Qwen3-8B indicate that the design transfers across backbone scales.