# FadeMem: Biologically-Inspired Forgetting for Efficient Agent Memory

arXiv: https://arxiv.org/abs/2601.18642 · HTML: https://mlsanity.com/paper/2601.18642/

## Key facts

- Cited by 14 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 17 of its 27 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Placed in

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

## Builds on (most cited first, 17 of 17)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://mlsanity.com/paper/2303.08896/) (2023)
- [Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory](https://mlsanity.com/paper/2504.19413/) (2025)
- [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024)
- [Beyond Goldfish Memory: Long-Term Open-Domain Conversation](https://mlsanity.com/paper/2107.07567/) (2021)
- [Memory OS of AI Agent](https://mlsanity.com/paper/2506.06326/) (2025)
- [Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy](https://mlsanity.com/paper/2312.10549/) (2023)
- [Multimodal Multi-Agent Empowered Legal Judgment Prediction](https://mlsanity.com/paper/2601.12815/) (2026)
- [Neural Brain: A Neuroscience-inspired Framework for Embodied Agents](https://mlsanity.com/paper/2505.07634/) (2025)

## Built on by (most cited first, 14 of 14)

- [TOKI: A Bitemporal Operator Algebra for Contradiction Resolution in LLM-Agent Persistent Memory](https://mlsanity.com/paper/2606.06240/) (2026)
- [User as Code: Executable Memory for Personalized Agents](https://mlsanity.com/paper/2606.16707/) (2026)
- [Oblivion: Self-Adaptive Agentic Memory Control through Decay-Driven Activation](https://mlsanity.com/paper/2604.00131/) (2026)
- [FSFM: A Biologically-Inspired Framework for Selective Forgetting of Agent Memory](https://mlsanity.com/paper/2604.20300/) (2026)
- [ConMem: Structured Memory-Guided Adaptation in Training-Free Multi-Agent Systems](https://mlsanity.com/paper/2606.08702/) (2026)
- [MemChain: Learning Interpretable Memory Traces for Memory-Augmented LLM Agents](https://mlsanity.com/paper/2607.24097/) (2026)
- [ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems](https://mlsanity.com/paper/2604.23878/) (2026)
- [Profile-Graph Memory for LLM Agents: Implicit Cross-Entity Traversal through Narrative Profiles](https://mlsanity.com/paper/2607.19359/) (2026)
- [The Horizon Gap: Planning, Memory, Execution, Training, and Evaluation for Long-Horizon LLM Agents](https://mlsanity.com/paper/2608.06663/) (2026)
- [What Should an Agent Forget? Separating What Is Stored from What Is Used](https://mlsanity.com/paper/2609.10263/) (2026)
- [Caching for the Future: Scrub Jay Episodic Memory Principles for Agent Memory Systems](https://mlsanity.com/paper/2608.04746/) (2026)
- [Reputation as Community Memory for the Agentic Web](https://mlsanity.com/paper/2609.19502/) (2026)
- [Remember by Asking: Retrieval-Induced Memory Evolution for LLM Agents](https://mlsanity.com/paper/2609.34438/) (2026)
- [Agent-Controlled Forgetting for Tool-Using Agents: Reversible Context Curation in Practice](https://mlsanity.com/paper/2610.10590/) (2026)

## Abstract (arXiv)

Large language models deployed as autonomous agents face critical memory limitations, lacking selective forgetting mechanisms that lead to either catastrophic forgetting at context boundaries or information overload within them. While human memory naturally balances retention and forgetting through adaptive decay processes, current AI systems employ binary retention strategies that preserve everything or lose it entirely. We propose FadeMem, a biologically-inspired agent memory architecture that incorporates active forgetting mechanisms mirroring human cognitive efficiency. FadeMem implements differential decay rates across a dual-layer memory hierarchy, where retention is governed by adaptive exponential decay functions modulated by semantic relevance, access frequency, and temporal patterns. Through LLM-guided conflict resolution and intelligent memory fusion, our system consolidates related information while allowing irrelevant details to fade. Experiments on Multi-Session Chat, LoCoMo, and LTI-Bench demonstrate superior multi-hop reasoning and retrieval with 45% storage reduction, validating the effectiveness of biologically-inspired forgetting in agent memory systems.