μWM: Fixed-Budget World Memory for Long-Running Wearable Assistants

Wearable assistants require reliable long-horizon interaction history under strict kilobyte memory constraints, yet existing systems either lose joint spatio-temporal conditions through decoupled counters or incur unbounded storage growth in raw event logs. We introduce $\mu$WM, the first wearable world memory framework tracking joint action-, place-, and time-conditioned interaction history under a strict budget of a few tens of kilobytes. Operating locally on microcontrollers (MCUs), $\mu$WM compiles incoming semantic streams into typed state at write time, prunes duration detail via query-loss allocation while preserving counts and temporal endpoints, and resolves queries directly using deterministic readers with state-directed evidence routing. Evaluated across 14 query families, $\mu$WM achieves 86.90% accuracy at 13.38 KiB on EPIC-KITCHENS-100, outperforming equal-budget controls while using 122.6$\times$ less storage than SOTA baselines. Furthermore, it recovers 99.1% raw-log fidelity on AMB, maintains full accuracy across 32$\times$ extended ALFRED trajectories within 28.3 KiB, and fits entirely inside 64 KiB Data Tightly-Coupled Memory (DTCM) on an STM32F7 MCU at 153 to 173 mW peak power. These results establish structured state compilation as an efficient, privacy-preserving memory substrate for edge embodied intelligence.