Solutions for time-sensitive tasks
MemOS is designed with a three-layer optimized architecture for timing inference:
- The underlying timeline engine implements precise event sequencing
- Causal Reasoning Module at the Middle Level
- Upper level temporal pattern recognition component
In practical applications in the field of financial risk control, the system has improved the F1 value for fraud detection from 0.72 to 0.91. Its core technologies include:
- Algorithm for the dynamic computation of the time decay factor
- Event Conflict Detection and Resolution
- Self-discovery mechanisms for periodic patterns
Developers can achieve accurate time annotation down to the millisecond level through the timestamp parameter of the add_memory interface, and the system automatically maintains the causal topology of events.
This answer comes from the articleMemOS: An Open Source System for Enhancing the Memory Capacity of Large Language ModelsThe
May not be reproduced without permission:AI productivity tools " MemOS has a 159% performance advantage in timing inference scenarios
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