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MemInsight: Autonomous Memory Augmentation for LLM Agents
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MemInsight: Autonomous Memory Augmentation for LLM Agents
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Large language model (LLM) agents have evolved to intelligently process information, make decisions, and interact with users or tools. A key capability is the integration of long-term memory capabilities, enabling these agents to draw upon historical interactions and knowledge. However, the growing memory size and need for semantic structuring pose significant challenges. In this work, we propose an autonomous memory augmentation approach, MemInsight, to enhance semantic data representation and retrieval mechanisms. By leveraging autonomous augmentation to historical interactions, LLM agents are shown to deliver more accurate and contextualized responses. We empirically validate the efficacy of our proposed approach in three task scenarios; conversational recommendation, question answering and event summarization. On the LLM-REDIAL dataset, MemInsight boosts persuasiveness of recommendations by up to 14%. Moreover, it outperforms a RAG baseline by 34% in recall for LoCoMo retrieval. Our empirical results show the potential of MemInsight to enhance the contextual performance of LLM agents across multiple tasks.
Forward citations
Cited by 16 Pith papers
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A robotic agent operating system with source-grounded graph memory and split-wise self-evolution improves long-horizon embodied task success and memory QA scores over baseline controllers.
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MemRouter: Memory-as-Embedding Routing for Long-Term Conversational Agents
A lightweight supervised router using frozen-LLM embeddings for memory admission decisions outperforms LLM-based memory managers in both F1 score and latency on the LoCoMo benchmark.
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APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI
APEX-MEM uses property graphs with temporal events, append-only storage, and an agentic retrieval system to reach 88.88% accuracy on LOCOMO QA and 86.2% on LongMemEval, outperforming prior session-aware methods.
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ReCreate: Reasoning and Creating Domain Agents Driven by Experience
ReCreate automatically creates and adapts domain LLM agents by turning past interaction experiences into scaffold edits via reasoning and hierarchical abstraction.
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Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents
AgeMem unifies long-term and short-term memory management in LLM agents by exposing memory operations as learnable tool actions trained via three-stage progressive reinforcement learning, outperforming baselines on lo...
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Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Memory-R1 uses PPO and GRPO to train a Memory Manager (ADD/UPDATE/DELETE/NOOP) and Answer Agent that together outperform baselines on long-context QA benchmarks after training on only 152 examples.
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ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory
A hierarchical robotic Agent OS with source-grounded multi-modal graph memory and split-gated self-evolution improves long-horizon embodied execution and memory QA over single-controller and prior memory baselines.
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Bridging Inference-Time Scaling and Episodic Memory with Action-Centric Graphs
Graph-based action memory with dual-stream TD learning improves best-of-N inference scaling for LLM agents, reporting +20.81% success / +6.17% progress over vanilla baselines.
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Exploring the Topology and Memory of Consensus: How LLM Agents Agree, Fragment, or Settle When Forming Conventions
Simulations of 16 LLM agents in a naming game on 8 topologies show memory depth interacts with network structure to flip coordination speed and increase fragmentation in centralized networks.
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HyperMem: Hypergraph Memory for Long-Term Conversations
HyperMem is a hypergraph memory architecture that groups related conversation episodes and facts via hyperedges and reports 92.73% LLM-as-a-judge accuracy on the LoCoMo benchmark.
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Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents
AgeMem trains LLM agents to manage both long-term and short-term memory through tool calls using a three-stage reinforcement-learning curriculum, reporting gains on five long-horizon benchmarks.
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From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs
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From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms
LLM agent memory is organized into Storage (preserving trajectories), Reflection (refining them), and Experience (abstracting into reusable knowledge) stages driven by needs for long-range consistency, dynamic adaptat...
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The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.
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