# ScrapMem: A Bio-inspired Framework for On-device Personalized Agent Memory via Optical Forgetting

arXiv: https://arxiv.org/abs/2605.03804 · HTML: https://mlsanity.com/paper/2605.03804/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Agent memory systems, one of 16 directions in LLM agents and tool use.
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 5 May 2026.

## Placed in

- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 19 of 19)

- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory](https://mlsanity.com/paper/2504.19413/) (2025)
- [From RAG to Memory: Non-Parametric Continual Learning for Large Language Models](https://mlsanity.com/paper/2502.14802/) (2025)
- [DeepSeek-OCR: Contexts Optical Compression](https://mlsanity.com/paper/2510.18234/) (2025)
- [On-Device Language Models: A Comprehensive Review](https://mlsanity.com/paper/2409.00088/) (2024)
- [Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Management](https://mlsanity.com/paper/2510.06727/) (2025)
- [Foundations and Recent Trends in Multimodal Mobile Agents: A Survey](https://mlsanity.com/paper/2411.02006/) (2024)
- [AgentOCR: Reimagining Agent History via Optical Self-Compression](https://mlsanity.com/paper/2601.04786/) (2026)
- [MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning](https://mlsanity.com/paper/2601.21468/) (2026)
- [Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning](https://mlsanity.com/paper/2405.20834/) (2024)
- [According to Me: Long-Term Personalized Referential Memory QA](https://mlsanity.com/paper/2603.01990/) (2026)
- [NextMem: Towards Latent Factual Memory for LLM-based Agents](https://mlsanity.com/paper/2603.15634/) (2026)
- [Agentic Unlearning: When LLM Agent Meets Machine Unlearning](https://mlsanity.com/paper/2602.17692/) (2026)
- [OCR-Memory: Optical Context Retrieval for Long-Horizon Agent Memory](https://mlsanity.com/paper/2604.26622/) (2026)
- [Venus: An Efficient Edge Memory-and-Retrieval System for VLM-based Online Video Understanding](https://mlsanity.com/paper/2512.07344/) (2025)
- [Recall: Empowering Multimodal Embedding for Edge Devices](https://mlsanity.com/paper/2409.15342/) (2024)

## Built on by (most cited first, 3 of 3)

- [LycheeMemory V2: Efficient Long-Term Memory for LLM Agents via Semantic Segment-Level Consolidation](https://mlsanity.com/paper/2608.12990/) (2026)
- [Graph-Based Personalized Memory for LLM Agents: Representation, Evolution, Retrieval, and Evaluation](https://mlsanity.com/paper/2609.08599/) (2026)
- [HGP:An on-device personalized agent memory via hybrid graph storage](https://mlsanity.com/paper/2610.10071/) (2026)

## Abstract (arXiv)

Long-term personalized memory for LLM agents is challenging on resource-limited edge devices due to high storage costs and multimodal complexity. To address this, we propose ScrapMem, a framework that integrates multimodal data into "Scrapbook Page." ScrapMem introduces Optical Forgetting, an optical compression mechanism that progressively reduces the resolution of older memories, lowering storage cost while suppressing low-value details. To maintain semantic consistency, we construct an Episodic Memory Graph (EM-Graph) that organizes key events into a causal-temporal structure. Extensive experiments on the multimodal ATM-Bench showcase that ScrapMem provides three main benefits: (1) strong performance, achieving a new state-of-the-art with a 51.0% Joint@10 score; (2) high storage efficiency, reducing memory usage by up to 93% via optical forgetting; and (3) improved recall, increasing Recall@10 to 70.3% through structured aggregation. ScrapMem offers an effective and storage-efficient solution for on-device long-term memory in multimodal LLM agents.