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episodic_memory_store

episodic_memory_store

Maintain frozen-key episodic memory with random write, random replay sampling, and Euclidean KNN retrieval.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill-Dokumentation

GitHub

name: episodic_memory_store description: Maintain frozen-key episodic memory with random write, random replay sampling, and Euclidean KNN retrieval.

Episodic Memory Store

When To Use

Use this skill when reconstructing the episodic-memory lifelong language learning method from de Masson d'Autume et al. without relying on an original implementation repository.

Inputs

  • Small text examples represented as dictionaries.
  • Deterministic configuration values such as memory probability, replay interval, K, and adaptation steps.

Outputs

  • Auditable Python objects or JSON-serializable dictionaries matching the module contract.
  • Logs that expose mechanism execution rather than only final metrics.

Workflow

  1. Encode keys with a fixed deterministic encoder.
  2. Write examples probabilistically to control memory capacity.
  3. Use random samples for replay and nearest neighbors for local adaptation.

Validation

Run python tests/test_episodic_memory_store.py from this skill directory.

Limitations

The scripts are deterministic proxy utilities for bounded recovery. They do not train BERT or reproduce the full paper-scale datasets.

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