CloneMem is a comprehensive benchmark for evaluating long-term memory capabilities of AI Clones. Unlike existing memory benchmarks that primarily rely on user–agent conversational histories, CloneMem tests whether an AI Clone can integrate non-conversational digital traces drawn from everyday life and use them to consistently track an individual's experiences, emotional changes, and evolving opinions over time.

Figure 1: Illustrative application scenarios of an AI Clone grounded in long-term digital traces, including delegated communication and proactive memory-driven assistance.

• Non-Conversational Digital Traces: Grounded in diaries, social media posts, direct messages, and emails spanning 1-3 years
• Top-Down Data Construction: Hierarchical generation framework ensuring longitudinal coherence from persona to micro-level events
• Multi-Dimensional Evaluation: Assesses tracking of experiences, emotions, and opinions over time
• Diverse Task Types: 8 reasoning categories including factual recall, temporal reasoning, causal/counterfactual reasoning, and unanswerable detection
• Bilingual Support: English and Chinese datasets

Figure 3: Illustrative examples of CloneMem tasks. The left panel shows non-conversational digital traces and their associated ground-truth evidence; the right panel shows example questions and answers for three task types.

git clone https://github.com/AvatarMemory/CloneMemBench.git
cd CloneMem
pip install -e .

The dataset is included in this repository under data/releases/. After cloning, you can directly access the benchmark data. See the Data Format documentation for detailed schema information.

CloneMem/
├── README.md                    # This file
├── configs/                     # Configuration files
├── data/
│   ├── big_five/               # Big Five personality data
│   ├── releases/               # 📦 Released benchmark dataset
│   │   └── README.md           # Data format documentation
│   └── runs/                   # Pipeline run outputs
├── docs/
│   └── README.md               # General documentation
├── outputs/                     # Generated outputs
├── src/
│   ├── clonemem/               # Data generation pipeline
│   │   ├── build/
│   │   │   ├── config/         # Build configurations
│   │   │   ├── core/           # Core data structures
│   │   │   ├── generators/     # LLM-based generators
│   │   │   ├── postprocess/    # Post-processing utilities
│   │   │   ├── prompting/      # Prompt templates
│   │   │   ├── runners/        # Pipeline runners
│   │   │   └── workflows/      # Workflow orchestration
│   │   ├── common/             # Shared utilities
│   │   ├── cli.py              # Command-line interface
│   │   └── README.md           # Data generation guide
│   └── clonemem-eval/          # Evaluation framework
│       ├── eval/
│       │   ├── analysis/       # Metric computation scripts
│       │   ├── eval_amem.py    # A-Mem evaluation
│       │   ├── eval_flat.py    # Flat retriever evaluation
│       │   ├── eval_mem0.py    # Mem0 evaluation
│       │   ├── eval_oracle.py  # Oracle baseline
│       │   ├── run_eval.sh     # Evaluation runner script
│       │   └── run_generation.py
│       └── README.md           # Evaluation guide
├── .env                         # Environment variables
├── .gitignore
└── LICENSE                      # Apache 2.0 License

Our experiments reveal that current memory systems face significant challenges in AI Clone scenarios:

• Simple flat retrieval often outperforms complex abstractive memory systems (A-Mem, Mem0)
• Abstraction helps search but hurts cloning: Summarization and fact extraction act as lossy compression
• Models fall back to narrative priors when evidence is underspecified
• Event logs cannot represent "no decision yet": Activity ≠ state

Results with GPT-4o-mini backbone at k=10

If you find CloneMem useful for your research, please cite our paper:

@misc{hu2026clonemembenchmarkinglongtermmemory,
      title={CloneMem: Benchmarking Long-Term Memory for AI Clones}, 
      author={Sen Hu and Zhiyu Zhang and Yuxiang Wei and Xueran Han and Zhenheng Tang and Huacan Wang and Ronghao Chen},
      year={2026},
      eprint={2601.07023},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2601.07023}, 
}

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.