CITATION.cff

cff-version: 1.2.0
message: "If you use MemoryLens in your research, please cite it as below."
type: software
title: "MemoryLens: A Temporal Decay Benchmark for LLM Memory Architectures"
abstract: >
  MemoryLens is an open-source evaluation framework for measuring LLM memory decay
  — how AI memory systems forget personal facts across long conversations. It implements
  eight memory architectures (Naive, RAG, Chunked RAG, Cascading Temporal, Summary,
  Entity, Graph, FAISS), six evaluation metrics (Recall@T, Precision@K, Temporal Drift,
  Contradiction, Memory Noise Ratio, Cascade Efficiency), Ebbinghaus-grounded temporal
  decay with ablation, forgetting-curve fitting, multi-seed statistical validation,
  four domain scenarios, and a dual evaluation pipeline (content-based + LLM
  answer+judge) supporting five provider backends.
authors:
  - family-names: Daftary
    given-names: Neal
    alias: Neal006
    orcid: ""
repository-code: "https://github.com/Neal006/memorylens"
url: "https://github.com/Neal006/memorylens"
license: MIT
version: 0.4.0
date-released: "2026-07-04"
keywords:
  - LLM memory
  - memory decay
  - LLM evaluation
  - RAG evaluation
  - temporal decay
  - Ebbinghaus forgetting curve
  - conversational AI
  - memory benchmark
  - long-term memory
  - retrieval-augmented generation
  - cascading memory
  - LLM benchmark
references:
  - type: article
    title: "RAGAS: Automated Evaluation of Retrieval Augmented Generation"
    authors:
      - family-names: Es
        given-names: Shahul
    year: 2023
    url: "https://arxiv.org/abs/2309.15217"
  - type: article
    title: "MemGPT: Towards LLMs as Operating Systems"
    authors:
      - family-names: Packer
        given-names: Charles
    year: 2023
    url: "https://arxiv.org/abs/2310.08560"