✨MIRIX: Multi-Agent Memory System for LLM-Based Agents

📝 Summary:
MIRIX is a modular multi-agent memory system for LLM-based agents that integrates diverse memory types and a dynamic framework. It significantly enhances memory capabilities for multimodal and long-form conversations. MIRIX achieves superior performance on challenging benchmarks, outperforming ex...

🔹 Publication Date: Published on Jul 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.07957
• PDF: https://arxiv.org/pdf/2507.07957
• Project Page: https://mirix.io/
• Github: https://github.com/Mirix-AI/MIRIX

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#LLM #MultiAgentSystems #AISystems #MemorySystems #AI

✨HaluMem: Evaluating Hallucinations in Memory Systems of Agents

📝 Summary:
HaluMem is a new benchmark that evaluates memory hallucinations in AI systems by localizing them to specific stages: extraction, updating, and question answering. It uses large human-AI interaction datasets. Findings show current systems accumulate hallucinations during extraction and updating, w...

🔹 Publication Date: Published on Nov 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.03506
• PDF: https://arxiv.org/pdf/2511.03506
• Github: https://github.com/MemTensor/HaluMem

✨ Datasets citing this paper:
• https://huggingface.co/datasets/IAAR-Shanghai/HaluMem

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#AIHallucinations #AIAgents #MemorySystems #LLM #AIResearch

✨AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents

📝 Summary:
This paper bridges the gap between human memory systems and AI agent memory design. It synthesizes interdisciplinary knowledge, comparing biological and artificial memory mechanisms, reviewing benchmarks, and exploring security and future directions.

🔹 Publication Date: Published on Dec 29, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23343
• PDF: https://arxiv.org/pdf/2512.23343
• Github: https://github.com/AgentMemory/Huaman-Agent-Memory

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#AI #CognitiveNeuroscience #MemorySystems #AutonomousAgents #BrainInspiredAI

✨Distilling Feedback into Memory-as-a-Tool

📝 Summary:
This framework converts transient critiques into retrievable guidelines using a file-based memory system and agent tools. It enables LLMs to achieve test-time refinement performance with significantly reduced inference costs.

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05960
• PDF: https://arxiv.org/pdf/2601.05960
• Github: https://github.com/vicgalle/feedback-memory-as-a-tool

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#LLMs #AIAgents #MemorySystems #AIResearch #MachineLearning