Hierarchical Experience Memory

• Hierarchical Experience Memory is a memory architecture that organizes experiential data into semantically structured levels for efficient reasoning and sample-efficient transfer.
• It employs segmentation, abstraction, and layered index-based retrieval strategies, enabling precise access and scalable performance in long-horizon scenarios.
• Its applications span reinforcement learning, dialogue systems, robotics, and multi-agent collaboration, with empirical results showing significant gains over flat memory systems.

Hierarchical Experience Memory is a class of memory architectures that organize experiential data at multiple abstraction levels, enabling agents—both artificial and biological—to achieve more efficient reasoning, better generalization, and sample-efficient transfer in long-horizon and multi-task scenarios. Unlike flat memory systems that store snapshots or condensed trajectories as undifferentiated objects, hierarchical experience memory decomposes interaction history into explicit, semantically structured levels, such as high-level plans, subgoals, atomic actions, or abstracted knowledge representations. This paradigm is now prominent in LLM-based agents, reinforcement learning agents, multi-agent systems, and neural-symbolic robotics.

1. Structural Taxonomy of Hierarchical Experience Memory

Architectures implementing hierarchical experience memory typically encode experience at two or more heterogeneously granular levels. Leading designs include:

• Two-Level Architectures: For example, H²R (Ye et al., 16 Sep 2025) uses
  • High-level planning memory $\mathcal{M}^H$, storing tuples $(X, G, I_{\text{plan}})$, where $X$ is the task context, $G$ a hindsight-inferred subgoal sequence, and $I_{\text{plan}}$ a set of planning insights.
  • Low-level execution memory $\mathcal{M}^L$ with tuples $(g, \tau, I_{\text{exec}})$, where $g$ is an atomic subgoal, $\tau$ is a fine-grained state-action-observation trajectory, and $I_{\text{exec}}$ comprises execution-specific insights.
• Multi-Level Hierarchies: H-MEM (Sun et al., 23 Jul 2025) generalizes to four layers, structuring memory as Domain, Category, Trace (event/entity), and Episode layers. Nodes at each layer have semantic embeddings and positional indices linking to child memories.
• Tree-Based and Graph-Based Encodings: Memory may also be organized into trees (MemTree (Rezazadeh et al., 2024), H-Emv (Bärmann et al., 2024)) or multi-tier graphs (G-Memory (Zhang et al., 9 Jun 2025)), supporting flexible abstraction, contextual linking, and bi-directional memory traversal.

Table: Representative Hierarchical Memory Structures

System	Memory Representation	Granularity Levels
H²R	Planning/Execution Memories	2 (high-level plan, low-level exec)
H-MEM	Four-layer index+pointer tree	4 (domain → episode)
MemTree	Dynamic summary tree	Variable (schema to detail)
Bi-Mem	Facts, Scenes, Persona	3 (fact → scene → persona)
StackPlanner	Task stack, Experience memory	2 (per-task, cross-task)
G-Memory	Insight, Query, Interaction graphs	3 (insight → query → interaction)

2. Memory Construction and Update Mechanisms

Constructing hierarchical experience memory involves (i) segmentation and abstraction of raw experience, (ii) summarization or distillation at each level, and (iii) efficient update rules to accommodate new experiences.

• Segmentation and Abstraction: Experiences are decomposed via event boundary detection, subgoal inference, or semantic segmentation. For example, HiMem (Zhang et al., 10 Jan 2026) uses a dual-channel segmentation strategy: boundaries are detected by topic shifts or LLM-computed "surprise" scores, segmenting dialogues into episodes, followed by multi-stage information extraction to produce abstracted notes/facts.
• Contrastive Hindsight Reflection: H²R (Ye et al., 16 Sep 2025) applies high-level and low-level hindsight transforms $\varphi_H$ and $\varphi_L$, using both successful and failed trajectories to extract strategic and tactical insights at corresponding granularity.
• Graph Aggregation and Clustering: Bi-Mem (Mao et al., 10 Jan 2026) forms fact-level memories, clusters them into scene-level memories using modularity maximization (label propagation), and finally distills them into a global persona profile. Calibration by a reflective agent ensures consistency across the hierarchy.

Memory insertion often uses pseudocode routines for pipeline-like transformations, explicit memory updates, and dynamic weighting/forgetting mechanisms to prune or reinforce specific memories, as in H-MEM (Sun et al., 23 Jul 2025).

3. Retrieval Strategies: Hierarchical and Associative Search

Hierarchical experience memory fundamentally alters retrieval by enabling multi-stage, focused access:

• Layered, Index-Aware Retrieval: Rather than a flat $\mathcal{O}(N D)$ vector search, architectures like H-MEM (Sun et al., 23 Jul 2025) employ index-based routing. At each layer, vector similarity is used to select top-$k$ candidate nodes, whose children in the next layer are recursively considered, resulting in $\mathcal{O}(L k D)$ retrieval cost where $L$ is the number of levels.
• Parallel or Hybrid Retrieval: H²R (Ye et al., 16 Sep 2025) retrieves independently at high and low levels using task or subgoal embeddings:
  • High-level: $$R_H(c^*) = \arg\max_{m_H^i\in\mathcal{M}^H}\mathrm{sim}_H(c^*, X^i)$$
  • Low-level: $$R_L(c^*) = \arg\max_{m_L^j\in\mathcal{M}^L}\mathrm{sim}_L(c^*, g^j)$$
• Associative and Spreading Activation: Bi-Mem (Mao et al., 10 Jan 2026) extends hierarchical retrieval with bi-directional association:
  • Query-to-fact/scene/persona similarity scoring.
  • Spreading activation: fact→scene and scene→fact relations are followed, and composite activation scores are computed for ranking.

This leads to more efficient, relevant, and contextually precise retrieval, essential for compositional reasoning and long-horizon modeling.

4. Practical Applications Across Modalities and Agent Types

Hierarchical experience memory is implemented in a variety of agent design contexts:

• Multi-Task LLM Agents: H²R (Ye et al., 16 Sep 2025) and EHC (Qiao et al., 28 May 2025) demonstrate that decoupling planning from execution yields higher success rates and better generalization on benchmarks such as AlfWorld and PDDLGame, outperforming both memory-less agents (ReAct) and monolithic memory (ExpeL).
• Long-Term Dialogue Agents: H-MEM (Sun et al., 23 Jul 2025), MemTree (Rezazadeh et al., 2024), and HiMem (Zhang et al., 10 Jan 2026) enable open-ended dialogue reasoning and memory-augmented retrieval-augmented generation (RAG). H-MEM achieves +14.98 F1 and +12.77 BLEU-1 over baseline on the LoCoMo dialogue benchmark.
• Robotic Control: MemER (Sridhar et al., 23 Oct 2025) uses a two-level memory (keyframe selection and subtask instruction) to allow LLM-VLM agents to execute minute-long manipulation tasks with high success, efficient scaling, and low annotation requirements.
• Multi-Agent Collaboration: Systems like StackPlanner (Zhang et al., 9 Jan 2026) and G-Memory (Zhang et al., 9 Jun 2025) use hierarchical memory for active task stack management, reusable coordination experience, and bi-directional insight-query-interaction retrieval—leading to large improvements in collaborative reasoning.
• Dynamic Tool Construction and Transfer: SMITH (Liu et al., 12 Dec 2025) formalizes procedural, semantic, and episodic memory; this enables systematic cross-task experience sharing and dynamic tool creation, with competitive performance on the GAIA curriculum.

5. Quantitative Impact and Empirical Findings

Empirical studies consistently show superior performance from hierarchical memory architectures relative to both flat and monolithic memory systems, often with reduced computational overhead:

• Success Rates: H²R improves AlfWorld test success from 46.3% (ReAct) to 75.9% and PDDLGame from 66.7% to 80.5% (Ye et al., 16 Sep 2025).
• Long-Term Reasoning F1/BLEU: H-MEM (Sun et al., 23 Jul 2025) shows average improvements of +14.98 F1, +12.77 BLEU-1, with multi-hop gains of ~+21 F1.
• Efficiency: H-MEM’s index-based routing keeps retrieval latency <100 ms even as memory scales to millions of episodes, compared to >400 ms for flat memory.
• Ablations: Success drops are sizable when hierarchy is ablated. For example, removing high-level memory in H²R yields –27.7 points; removing low-level yields –19.4 (Ye et al., 16 Sep 2025).
• Robotics: MemER (Sridhar et al., 23 Oct 2025) boosts object search retrievals from 47 to 59 out of 60, and reduces task execution latency versus long-history baselines.
• Dialogue Scaling: HiMem (Zhang et al., 10 Jan 2026) and MemTree (Rezazadeh et al., 2024) demonstrate scalable performance (token cost $\mathcal{O}(\log N)$ or less) while maintaining or improving answer accuracy as dialogue episodes lengthen.

6. Theoretical and Algorithmic Insights

Hierarchical experience memory confers several robust advantages identified in quantitative and algorithmic analyses:

• Abstraction and Decoupling: Decoupling between strategic (plan, intent) and tactical (execution) knowledge prevents mutual contamination and supports targeted transfer (Ye et al., 16 Sep 2025).
• Right-Granularity Retrieval: Fine-grained subgoal or event memories guide execution details, while high-level schemas expedite planning (Sun et al., 23 Jul 2025, Sridhar et al., 23 Oct 2025).
• Contrastive and Event-Aware Distillation: Hindsight reflection, event boundary detection, and multi-stage semantic abstraction provide both robustness (e.g., to distractors) and generalization beyond training lengths (Lampinen et al., 2021, Mao et al., 10 Jan 2026).
• Efficiency and Scalability: By limiting similarity matching to selected subtrees or pointer-linked indices, retrieval remains computationally tractable even for long agent lifetimes (Sun et al., 23 Jul 2025, Bärmann et al., 2024, Rezazadeh et al., 2024).
• Self-Evolution and Conflict Resolution: Some systems (HiMem (Zhang et al., 10 Jan 2026), Bi-Mem (Mao et al., 10 Jan 2026)) implement conflict-aware reconsolidation or bidirectional calibration (inductive and reflective), allowing the memory to self-refine over time.

Common ablations show marked drops in performance without hierarchical structure, underscoring the necessity of multi-level organization for temporally and contextually extended tasks.

7. Symbolic and Biological Inspirations

Hierarchical experience memory also draws on cognitive and neurobiological models:

• Symbolic Hierarchies: Fuzzy Description Logic (DL) frameworks (Buoncompagni et al., 2024) can formally encode hierarchies with “store, retrieve, consolidate, forget” operations, yielding robust, transparent, and noise-resistant symbolic graphs amenable to further reasoning and human inspection.
• Neural and Self-Organizing Models: Layered neural models (0905.2125) self-organize local-to-global representations with parts-based feature vocabularies, long-separated time-scales (fast activity vs. slow plasticity), and lateral/top-down interactions—mirroring principles of competition, recurrence, and homeostatic balance seen in biological cortex.

Such connections demonstrate that hierarchical experience memory unifies algorithmic, neural, and symbolic treatments of memory, enabling adaptive, high-capacity, and interpretable systems for real-world reasoning and interaction.

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Hierarchical experience memory thus represents a convergence of abstraction, algorithmic efficiency, and empirical efficacy, providing a scalable substrate for experience-driven reasoning in both artificial agents and cognitive systems (Ye et al., 16 Sep 2025, Sun et al., 23 Jul 2025, Kelly et al., 2021, Zhang et al., 10 Jan 2026, Rezazadeh et al., 2024, Liu et al., 12 Dec 2025, Zhang et al., 9 Jun 2025, Sridhar et al., 23 Oct 2025, Zhang et al., 9 Jan 2026, Mao et al., 10 Jan 2026, Buoncompagni et al., 2024, 0905.2125, Bärmann et al., 2024, Lampinen et al., 2021, Qiao et al., 28 May 2025).