mlsanityData to 7 Oct 2026

FadeMem: Biologically-Inspired Forgetting for Efficient Agent Memory

arXiv 2601.18642 ↗First posted 26 Jan 2026cs.AI cs.CLcited by 13

On the map: LLM agents and tool use → Agent memory systems

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.

Key facts. Cited by 13 papers in the map (data through 7 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.

Builds on

17 of 27 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Memory OS of AI Agent30 May
  2. 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory28 Apr
  3. 2024A Survey on the Memory Mechanism of Large Language Model based Agents21 Apr
  4. 2024Evaluating Very Long-Term Conversational Memory of LLM Agents27 Feb

Other references · 13

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  5. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  6. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  7. 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,830
  8. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
  9. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  10. 2021Beyond Goldfish Memory: Long-Term Open-Domain Conversationcited by 266
  11. 2023Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomycited by 24
  12. 2026Multimodal Multi-Agent Empowered Legal Judgment Predictioncited by 14
1 more
  1. 2025Neural Brain: A Neuroscience-inspired Framework for Embodied Agentscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 13.

13 papers.

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  1. 2026TOKI: A Bitemporal Operator Algebra for Contradiction Resolution in LLM-Agent Persistent Memorycited by 7
  2. 2026User as Code: Executable Memory for Personalized Agentscited by 7
  3. 2026Oblivion: Self-Adaptive Agentic Memory Control through Decay-Driven Activationcited by 3
  4. 2026FSFM: A Biologically-Inspired Framework for Selective Forgetting of Agent Memorycited by 3
  5. 2026ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systemscited by 1
  6. 2026ConMem: Structured Memory-Guided Adaptation in Training-Free Multi-Agent Systemscited by 1
  7. 2026Profile-Graph Memory for LLM Agents: Implicit Cross-Entity Traversal through Narrative Profilescited by 1
  8. 2026MemChain: Learning Interpretable Memory Traces for Memory-Augmented LLM Agentscited by 1
  9. 2026The Horizon Gap: Planning, Memory, Execution, Training, and Evaluation for Long-Horizon LLM Agentscited by 1
  10. 2026Caching for the Future: Scrub Jay Episodic Memory Principles for Agent Memory Systemscited by 0
  11. 2026What Should an Agent Forget? Separating What Is Stored from What Is Usedcited by 0
  12. 2026Reputation as Community Memory for the Agentic Webcited by 0
1 more
  1. 2026Remember by Asking: Retrieval-Induced Memory Evolution for LLM Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026SF-AMS: Strategic Forgetting for Structured Memory in LLM Agent
  2. 2026SuperLocalMemory V3.3: The Living Brain -- Biologically-Inspired Forgetting, Cognitive Quantization, and Multi-Channel Retrieval for Zero-LLM Agent Memory Systems
  3. 2026AMA: Adaptive Memory via Multi-Agent Collaboration
  4. 2026AdMem: Advanced Memory for Task-solving Agents
  5. 2026Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers
  6. 2026BMAM: Brain-inspired Multi-Agent Memory Framework
  7. 2026Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents
  8. 2026Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations