EventMem: Adaptive Event-Centric Memory for Streaming Video Understanding

MLLMs often struggle with real-time reasoning over streaming videos as visual information continuously accumulates over time. A key bottleneck is efficient memory management: preserving long-term context within a compact representation while retaining transient and fine-grained visual evidence. To address these challenges, we propose EventMem, a training-free event-centric memory framework: (1) EventMem discovers structured event units from incoming video streams by temporal-aware adaptive K-means event segmentation (TAKC). (2) it performs temporal segmentaion and aligned frame compression (TSAFM) to each event, reducing redundant visual information while retaining frames. (3) it builds complementary event-residual memory to preserve both long-term context and recent fine-grained evidence, and retrieves relevant historical events and fuses them with short-term evidence for efficient streaming reasoning. Extensive experiments show that EventMem consistently improves streaming video understanding. It improves object search, long-term memory, and short-term memory by 11.71, 6.73, and 3.17 points, respectively, validating the effectiveness of event-aware compression and complementary memory for streaming reasoning.