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

Ningning Zhang, Xingxing Yang, Zhizhong Tan, Weiping Deng, Wenyong Wang

arXiv:2601.06377v1cs.AI

TL;DR

Long-horizon conversational agents still struggle with fragmented, semantically unstable, and non-evolving memory during sustained interactions. HiMem addresses this gap with linked episodic and knowledge memories, hierarchical retrieval, and conflict-aware updating, and reports consistent gains in accuracy, temporal reasoning, open-domain understanding, and efficiency. The paper also identifies dependence on underlying LLM judgment and one-shot segmentation as scope limitations.

  • Problem

    Existing long-term memory systems struggle with semantic drift, memory fragmentation, inadequate hierarchy, and limited self-evolution during sustained interactions.

  • Method

    HiMem links Episode Memory for contextual events with Note Memory for stable knowledge, using aligned construction, hierarchical retrieval, and conflict-aware Memory Reconsolidation.

  • Results

    HiMem consistently outperforms existing methods in accuracy, temporal reasoning, and open-domain understanding while maintaining a robust balance between knowledge coverage and system efficiency.

  • Takeaways & Limitations

    The results support treating long-horizon agent memory as a dynamic, multi-level system coupled to retrieval and usage rather than a static external store.

  • Takeaways & Limitations

    HiMem depends on the underlying LLM for segmentation, extraction, conflict detection, and evidence evaluation, and one-shot segmentation may limit expressiveness in extremely long or interleaved dialogues.

Abstract

from arXiv · show

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.

1 Introduction

HiMem addresses long-horizon memory failures by linking episodic context with abstract knowledge and supporting conflict-aware updates. The framework combines cognitively aligned construction mechanisms with retrieval and self-evolution, and experiments report improved long-horizon reasoning.

  • Existing long-horizon agents struggle to preserve, organize, and use information scattered across extended dialogues and sessions.
  • Current memory systems face semantic misalignment, inadequate hierarchy, and predominantly static updates.
  • HiMem proposes a hierarchy connecting concrete interaction events with abstract knowledge, unified semantic alignment, and conflict-aware continual updating.
  • Episode Memory uses Topic-Aware Event–Surprise Dual-Channel Segmentation, while Note Memory captures stable knowledge through multi-stage extraction.
  • HiMem introduces conflict-aware Memory Reconsolidation to support long-term self-correction and evolution during sustained interactions.
  • Experiments report that principled hierarchical design and dynamic updating improve long-horizon reasoning performance.

2 Methodology

HiMem builds a hierarchical memory system from episodic interaction records and abstracted knowledge, then retrieves and updates these representations through evidence-sensitive procedures. Its construction preserves contextual fidelity while its routing, reconsolidation, and optional forgetting mechanisms target efficiency and continual evolution.

  • HiMem integrates episodic interaction records with abstracted knowledge representations in a modular hierarchical architecture.
  • The framework transforms raw dialogue into structured memory through event segmentation, knowledge extraction, and semantic alignment.
  • Episode Memory stores non-overlapping, fine-grained episodes containing identifiers, timestamps, topics, summaries, metadata, and dialogue segments.
  • A boundary is introduced when either a topical shift or salient discontinuity occurs, using an OR rule to combine the two signals.
  • Note Memory extracts facts, preferences, and profiles, while multi-stage processing performs extraction, implicit-information identification, deduplication, coreference resolution, and temporal normalization.
  • Hybrid retrieval combines both memory layers, whereas best-effort retrieval queries Note Memory first and falls back to Episode Memory when evidence is insufficient.
  • Reconsolidation requires insufficient Note Memory evidence followed by adequate Episode Memory support, then applies ADD, UPDATE, or DELETE operations after comparison.
  • Episode Memory is immutable and appended chronologically, while adaptive forgetting optionally controls memory size and retrieval efficiency.

3 Experiments

The experiments evaluate HiMem on long-horizon multi-session dialogues using multi-dimensional quality and efficiency measures under controlled comparisons. The protocol covers diverse reasoning demands, standardized model settings, and repeated trials with explicit reporting conventions.

  • LoCoMo contains multi-session dialogues averaging approximately 600 turns and around 16K tokens, spanning up to 32 interaction stages.
  • The benchmark evaluates Single-Hop, Multi-Hop, Temporal Reasoning, and Open-Domain questions, excluding Adversarial questions from quantitative evaluation.
  • Evaluation uses GPT-Score as the primary semantic correctness and consistency measure alongside F1-based assessment.
  • Efficiency is measured with retrieval-only latency and token consumption, excluding LLM inference and response generation from latency.
  • Comparisons include Mem0, SeCom, and A-MEM, representing structured factual, event-segmented, and time-aware memory paradigms.
  • Baselines are selected for long-horizon dialogue compatibility, reproducibility, and comparable unified-agent evaluation budgets.
  • All methods use GPT-4o-mini, identical decoding configurations, a shared embedding model, and three independent trials with fixed prompts.
  • Main baseline comparisons report mean±std, while auxiliary analyses and ablations report mean values only.

4 Results and Analyses

HiMem consistently outperforms representative baselines across LoCoMo reasoning categories, with especially strong gains on Multi-Hop and Temporal Reasoning. Ablations show that Episode Memory, Note Memory, Knowledge Alignment, and Memory Reconsolidation contribute distinct aspects of performance.

  • Main Results: HiMem consistently outperforms all baselines across almost all LoCoMo categories, especially Multi-Hop and Temporal Reasoning.The benchmark covers Single-Hop, Multi-Hop, Temporal Reasoning, and Open-Domain questions.
  • Memory Components: Removing Episode Memory causes pronounced degradation, particularly on Multi-Hop and Temporal Reasoning, because detailed event-level evidence supports long reasoning chains.
  • Memory Components: Removing Note Memory produces a smaller but consistent drop, indicating that structured knowledge accelerates localization and stabilizes semantic anchors.Episode Memory and Note Memory therefore provide asymmetric but complementary benefits.
  • Knowledge Alignment: Knowledge Alignment strongly benefits Note Memory but slightly harms Episode Memory, showing that extracted knowledge is more dependent on unified semantic alignment.Note Memory lacks the raw dialogue context retained by Episode Memory and is therefore more sensitive to implicit semantics and coreference.
  • Memory Self-Evolution: 5.85% improvement in Note Memory and 0.28% overall gain result when Memory Self-Evolution is enabled through conflict-aware reconsolidation.During best-effort retrieval, retrieved Episode Memory supplies evidence for targeted supplementation when Note Memory fails self-validation.

5 Discussion

HiMem’s discussion frames long-term memory as a hierarchical, revisable system rather than a static store. Its complementary memory layers, conflict-aware updates, and retrieval strategies jointly address fidelity, semantic consistency, and efficiency.

  • Hierarchical Memory: Episode Memory preserves fine-grained contextual evidence, while Note Memory consolidates stable knowledge into compact representations that reduce retrieval cost.Removing either component degrades performance, supporting a balance between information fidelity and abstraction.
  • Memory Updating: Conflict-aware Memory Reconsolidation distinguishes overlapping, extending, and contradictory information and applies differentiated updates to maintain semantic consistency.The discussion rejects purely similarity-driven or append-only updating for long-term interactions.
  • Memory Updating: Memory Self-Evolution gains arise from a conservative feedback loop linking retrieval failure, episodic evidence inspection, and targeted knowledge supplementation.
  • Retrieval: Hierarchical retrieval is also an efficiency mechanism: abstract knowledge is retrieved first, with descent to concrete events only when necessary.The hybrid and best-effort strategies expose information that can drive further memory evolution while balancing accuracy and computational cost.

6 Conclusion

HiMem integrates hierarchical memory construction, retrieval, and conflict-aware updating to support long-horizon dialogue. Experiments show consistent gains in accuracy, temporal reasoning, open-domain understanding, and efficiency.

  • HiMem integrates event-level experiences with knowledge-level abstractions through hierarchical memory construction, retrieval, and dynamic updating.
  • Conflict-aware Memory Reconsolidation enables continuous correction and evolution of stored knowledge through retrieval feedback.
  • Episode Memory preserves contextual events, while Note Memory consolidates stable facts and user-specific attributes into compact representations.
  • HiMem consistently outperforms existing methods across accuracy, temporal reasoning, and open-domain understanding in long-horizon conversational scenarios.
  • Retrieval analyses show a robust balance between knowledge coverage and system efficiency.
  • The framework offers a practical paradigm for integrating cognitive theories into scalable, interpretable, and self-evolving LLM agents.

Limitations

HiMem’s limitations concern dependence on underlying LLM judgments, one-shot segmentation, conservative evolution triggers, and evaluation mainly in single-user text interactions. These boundaries motivate stronger robustness, expressivity, proactive updating, and broader evaluation.

  • HiMem’s memory construction and updating quality depends on the underlying LLM’s semantic and pragmatic judgments.
  • Noisy inputs, metaphorical language, and cross-cultural pragmatic variation may reduce segmentation and knowledge-extraction accuracy.
  • One-shot segmentation is efficient and controllable but may inadequately represent hierarchical or recursive event structures in extremely long or interleaved dialogues.
  • Retrieval-failure or insufficient-evidence triggers promote stability but may leave latent inconsistencies or outdated knowledge uncorrected.
  • Future work must improve adaptability and generalization while preserving structural clarity and interpretability.
  • HiMem’s experiments are mainly confined to single-user, text-based interactions, limiting evidence about multi-user, multimodal, and richer social settings.

Ethical Considerations

HiMem’s ethical considerations center on privacy, knowledge integrity, bias, transparency, and the broader analytical framing of memory-system design. Persistent profiling and self-updating require safeguards against misuse, hallucination, unfairness, and uninformed interaction.

  • Persistent storage of user preferences and profiles creates privacy risks requiring privacy-by-design measures such as encryption and GDPR adherence.
  • Memory Reconsolidation may consolidate hallucinated or incorrect information, so high-stakes use requires human-in-the-loop verification.
  • LLM-based memory abstraction may inherit or amplify foundation-model biases in stored Notes.
  • Users should be informed when interacting with agents equipped with long-term memory to support informed consent.
  • The Memory Form–Memory Organization–Memory Operation framework compares systems by representation, organizational structure, and dynamic operations.
  • HiMem shifts memory design toward semantic alignment, hierarchical organization, and conflict-aware reconsolidation across all three dimensions.

A.3 Memory Operation

Memory Operation concerns how systems update and manage memory during use. HiMem addresses prior limitations by making updates conflict-aware and supporting retrieval strategies that trade off recall, latency, and token consumption.

  • MemGPT manages context resources through scheduling and compression rather than directly modifying memory’s semantic structure.
  • Table 5 compares HiMem’s hybrid and best-effort retrieval strategies.
  • Existing systems often add or delete memory content without deciding whether conflicting knowledge should be retained, merged, or revised.
  • HiMem applies Memory Reconsolidation to knowledge-oriented memories, using assimilation or accommodation when semantic conflicts arise.
  • HiMem is characterized as maintaining non-degenerate designs across memory form, organization, and operation.

B Implementation Details

The evaluation fixes model, retrieval, embedding, repetition, and hardware settings across methods. HiMem’s retrieval comparison examines hybrid and best-effort strategies using latency and token consumption.

  • All methods use GPT-4o-mini with temperature=0.0, max_tokens=8192, top-k=10, and all-mpnet-base-v2 representations.
  • Evaluations keep parameters and prompts fixed, run three repetitions, and report mean and standard deviation, with GPT-4o-mini judging GPT-Score.
  • All experiments run on an Apple M4 Max MacBook Pro with 128GB unified memory to control hardware effects on efficiency metrics.
  • HiMem stores Episode Memory in OpenSearch and manages Note Memory representations with Qdrant.
  • Retrieval strategies: Hybrid retrieval queries both memory layers simultaneously, whereas best-effort retrieval queries Note Memory first and descends to Episode Memory when necessary.
  • Retrieval strategies: Hybrid retrieval achieves high accuracy with reduced latency but consumes the most tokens, while best-effort retrieval adds latency through self-evaluation and potential descent.

C.2 Hyperparameter Analysis

Increasing top-k improves retrieval coverage and accuracy until performance plateaus at k ≥10, while latency and token consumption continue increasing. Larger k can therefore add cost without corresponding performance gains.

  • Increasing k improves retrieval coverage and accuracy, but performance plateaus when k ≥10.
  • Retrieval latency and token consumption increase as k grows, reflecting higher retrieval costs.
  • Table 7 compares latency and token consumption across methods, noting that SeCom’s preloaded-data latency is not directly comparable.
  • Excessively large k introduces irrelevant information and unnecessary processing overhead.

C.3 Efficiency Analysis

HiMem’s efficiency and long-horizon performance arise from coordinated choices in memory organization, segmentation, semantic alignment, and self-evolution. The resulting system balances contextual fidelity, retrieval efficiency, and continual correction.

  • Efficiency: HiMem’s latency is not significantly worse than baselines, while lightweight semantic alignment adds minimal computational overhead.
  • Design synergies: The system’s advantages arise from interdependent decisions spanning memory representation, organization, and evolution rather than isolated component improvements.
  • Hierarchical memory: Episode Memory preserves contextual evidence for complex reasoning, while Note Memory accelerates localization and stabilizes semantic anchors.
  • Episode construction: Topic-Aware Event–Surprise Dual-Channel Segmentation outperforms coarse-grained and purely topic-based alternatives by capturing cognitive discontinuities in conversations.
  • Semantic alignment: Knowledge Alignment substantially benefits Note Memory but can degrade Episode Memory, supporting memory-type-aware rather than uniform semantic processing.
  • Self-evolution: Memory Reconsolidation extracts and writes back missing or conflicting knowledge when Episode Memory supplies evidence that Note Memory lacks.
  • Self-evolution: Enabling self-evolution improves Note Memory and overall performance, indicating that retrieval and updating form a feedback loop.
  • System-level validation: Hybrid and best-effort retrieval expose complementary latency and token trade-offs, while performance saturation at small k indicates information-dense memory units.
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