HiMem: Hierarchical Long-Term Memory for LLM Long-Horizon Agents

• URL: http://arxiv.org/abs/2601.06377v1
• Date: Sat, 10 Jan 2026 01:26:01 GMT
• Title: HiMem: Hierarchical Long-Term Memory for LLM Long-Horizon Agents
• Authors: Ningning Zhang, Xingxing Yang, Zhizhong Tan, Weiping Deng, Wenyong Wang,
• Abstract summary: HiMem is a hierarchical long-term memory framework for long-horizon dialogues.<n>It supports memory construction, retrieval, and dynamic updating during sustained interactions.<n>Results show HiMem consistently outperforms representative baselines in accuracy, consistency, and long-term reasoning.
• Score: 3.9396865837159822
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although long-term memory systems have made substantial progress in recent years, they still exhibit clear limitations in adaptability, scalability, and self-evolution under continuous interaction settings. Inspired by cognitive theories, we propose HiMem, a hierarchical long-term memory framework for long-horizon dialogues, designed to support memory construction, retrieval, and dynamic updating during sustained interactions. HiMem constructs cognitively consistent Episode Memory via a Topic-Aware Event--Surprise Dual-Channel Segmentation strategy, and builds Note Memory that captures stable knowledge through a multi-stage information extraction pipeline. These two memory types are semantically linked to form a hierarchical structure that bridges concrete interaction events and abstract knowledge, enabling efficient retrieval without sacrificing information fidelity. HiMem supports both hybrid and best-effort retrieval strategies to balance accuracy and efficiency, and incorporates conflict-aware Memory Reconsolidation to revise and supplement stored knowledge based on retrieval feedback. This design enables continual memory self-evolution over long-term use. Experimental results on long-horizon dialogue benchmarks demonstrate that HiMem consistently outperforms representative baselines in accuracy, consistency, and long-term reasoning, while maintaining favorable efficiency. Overall, HiMem provides a principled and scalable design paradigm for building adaptive and self-evolving LLM-based conversational agents. The code is available at https://github.com/jojopdq/HiMem.

Related papers

• From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents [78.30630000529133]
  We propose MM-Mem, a pyramidal multimodal memory architecture grounded in Fuzzy-Trace Theory.<n> MM-Mem memory structures hierarchically into a Sensory Buffer, Episodic Stream, and Symbolic.<n>Experiments confirm the effectiveness of MM-Mem on both offline and streaming tasks.
  arXiv  Detail & Related papers  (2026-03-02T05:12:45Z)
• AMA: Adaptive Memory via Multi-Agent Collaboration [54.490349689939166]
  We propose Adaptive Memory via Multi-Agent Collaboration (AMA), a novel framework that leverages coordinated agents to manage memory across multiple granularities.<n>AMA significantly outperforms state-of-the-art baselines while reducing token consumption by approximately 80% compared to full-context methods.
  arXiv  Detail & Related papers  (2026-01-28T08:09:49Z)
• MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization [57.17751568928966]
  We propose MetaMem, a framework that augments memory systems with a self-evolving meta-memory.<n>During meta-memory optimization, MetaMem iteratively distills transferable knowledge utilization experiences across different tasks.<n>Extensive experiments demonstrate the effectiveness of MetaMem, which significantly outperforms strong baselines by over 3.6%.
  arXiv  Detail & Related papers  (2026-01-27T04:46:23Z)
• Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning [55.251697395358285]
  Large language models (LLMs) are increasingly deployed as intelligent agents that reason, plan, and interact with their environments.<n>To effectively scale to long-horizon scenarios, a key capability for such agents is a memory mechanism that can retain, organize, and retrieve past experiences.<n>We propose CompassMem, an event-centric memory framework inspired by Event Theory.
  arXiv  Detail & Related papers  (2026-01-08T08:44:07Z)
• TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents [31.793564520129348]
  We present TiMem, a temporal-hierarchical memory framework that organizes conversations through a Temporal Memory Tree (TMT)<n>TiMem is characterized by three core properties: (1) temporal--hierarchical organization through TMT; (2) semantic-guided consolidation that enables memory integration across hierarchical levels without fine-tuning; and (3) complexity-aware memory recall that balances and efficiency across queries of varying complexity.
  arXiv  Detail & Related papers  (2026-01-06T09:24:19Z)
• EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning [42.339841548168565]
  Large Language Models (LLMs) are increasingly deployed as long-term interactive agents, yet their limited context windows make it difficult to sustain coherent behavior over extended interactions.<n>We introduce EverMemOS, a self-organizing memory operating system that implements an engram-inspired lifecycle for computational memory.<n>EverMemOS achieves state-of-the-art performance on memory-augmented reasoning tasks.
  arXiv  Detail & Related papers  (2026-01-05T14:39:43Z)
• Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents [57.38404718635204]
  Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows.<n>Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components.<n>We propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent's policy.
  arXiv  Detail & Related papers  (2026-01-05T08:24:16Z)
• CogMem: A Cognitive Memory Architecture for Sustained Multi-Turn Reasoning in Large Language Models [21.427373172124167]
  Large language models (LLMs) excel at single-turn reasoning but often lose accuracy and coherence over extended, multi-turn interactions.<n>We introduce CogMem, a memory-augmented LLM architecture that supports sustained iterative reasoning through structured, persistent memory.<n> Experiments on TurnBench show that this layered design mitigates reasoning failures, controls context growth, and improves consistency across extended reasoning chains.
  arXiv  Detail & Related papers  (2025-12-16T06:01:08Z)
• MemVerse: Multimodal Memory for Lifelong Learning Agents [35.218549149012844]
  We introduce MemVerse, a model-agnostic, plug-and-play memory framework.<n>MemVerse bridges fast parametric recall with hierarchical retrieval-based memory.<n>It enables scalable and adaptive multimodal intelligence.
  arXiv  Detail & Related papers  (2025-12-03T10:06:14Z)
• Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory [89.65731902036669]
  Evo-Memory is a streaming benchmark and framework for evaluating self-evolving memory in large language model (LLM) agents.<n>We evaluate over ten representative memory modules and evaluate them across 10 diverse multi-turn goal-oriented and single-turn reasoning and QA datasets.
  arXiv  Detail & Related papers  (2025-11-25T21:08:07Z)
• Evaluating Long-Term Memory for Long-Context Question Answering [100.1267054069757]
  We present a systematic evaluation of memory-augmented methods using LoCoMo, a benchmark of synthetic long-context dialogues annotated for question-answering tasks.<n>Our findings show that memory-augmented approaches reduce token usage by over 90% while maintaining competitive accuracy.
  arXiv  Detail & Related papers  (2025-10-27T18:03:50Z)
• LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory [68.97819665784442]
  We introduce LongMemEval, a benchmark designed to evaluate five core long-term memory abilities of chat assistants.<n>LongMemEval presents a significant challenge to existing long-term memory systems.<n>We present a unified framework that breaks down the long-term memory design into three stages: indexing, retrieval, and reading.
  arXiv  Detail & Related papers  (2024-10-14T17:59:44Z)
• Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning [64.93848182403116]
  Current deep-learning memory models struggle in reinforcement learning environments that are partially observable and long-term. We introduce the Stable Hadamard Memory, a novel memory model for reinforcement learning agents. Our approach significantly outperforms state-of-the-art memory-based methods on challenging partially observable benchmarks.
  arXiv  Detail & Related papers  (2024-10-14T03:50:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.