Google Titan Memory Framework

description: Google's persistent memory framework that uses a surprise-based learning metric to determine what gets written to long-term memory. The surprise signal (prediction error between expected and actual outcomes) acts as a gradient for memory updates, enabling continuous learning during inference rather than just during training. Memory persists across context windows and sessions.

developer: Google DeepMind

core_mechanism: Surprise metric (prediction error)

surprise_computation: Difference between model's prediction of next information and actual information received

persistence: Across context windows and sessions

key_innovation: Memory module receives gradient updates during INFERENCE, not just training

working_memory: High-capacity, most recent tokens, immediate task execution

contextual_memory: Compressed summaries, key takeaways, prevents context rot

long_term_memory: Indexed historical logs, permanent knowledge, multi-day projects

high_surprise: Write to long-term memory (persistent storage)

low_surprise: Working memory only (may be discarded)

medium_surprise: Contextual memory (compressed summaries)

comparison_to_transformers: Transformers remember everything stochastically; Titan learns to remember only what matters

genesis_gap: Genesis surprise_memory.py returns 0.5 for everything (hardcoded stub)

implementation_blueprint: 1) Predict expected content, 2) Compare prediction vs actual embeddings, 3) Cosine distance = surprise, 4) Route to appropriate memory tier

source: Agentic OS Blueprint Research Synthesis