Feature
Adaptive Strategy Selection
Not every query benefits from the same retrieval approach. MetaMemory uses multi-armed bandit algorithms — Thompson Sampling and Upper Confidence Bound — to learn which of its 5 retrieval channels works best for each type of query, continuously optimizing based on real usage patterns.
Thompson Sampling
A Bayesian approach that maintains probability distributions over channel effectiveness. Each query samples from these distributions, naturally balancing exploration of less-tested channels with exploitation of known-good ones.
Upper Confidence Bound (UCB)
Provides deterministic optimism in the face of uncertainty. Channels with less data get a confidence bonus, ensuring the system explores broadly before converging on optimal strategies.
5 Retrieval Channels
Semantic, temporal, emotional, keyword, and graph — each channel is specialized for a different access pattern and competes to surface the best memories.
Continuous Learning
Every retrieval is a learning signal. The system tracks which memories were actually useful downstream and feeds that signal back to update channel weights in real time.
5
Retrieval Channels
0.75→1.00
Recall@10 With Learning
0.64→0.88
MRR With Learning
Flat
Static Baseline