Hierarchical Expert Prompt (HEP)

Updated 7 November 2025

Hierarchical Expert Prompt (HEP) is a modular framework that organizes expert-derived cues into multi-level prompts for specialized, structured learning.
HEP leverages dynamic routing and orthogonality constraints to optimize prompt selection, enabling effective adaptation and improved generalization across tasks.
HEP delivers robust performance in NLP, vision-language, and graph learning by decomposing complex problems into manageable sub-tasks through hierarchical expert integration.

A Hierarchical Expert Prompt (HEP) is a prompt-based learning mechanism that leverages a multi-level, modular organization of expert-provided or expert-learned context cues—referred to as "prompts"—to inject specialization, structure, and knowledge into machine learning models, particularly LLMs, graph neural networks, and vision-LLMs. The HEP paradigm synchronizes multiple forms of specialization (by domain, task granularity, or modality) with a dynamic routing or selection strategy to decompose problem spaces, optimize adaptation, and robustly transfer knowledge across tasks or domains.

1. Concept and Theoretical Foundations

The HEP concept is grounded in the idea of partitioning a problem space into subregions or subproblems, each addressed by a dedicated “expert”—such as a prompt, an expert subnetwork, or a specialized template. The HEP framework orchestrates prompt construction and utilization in a layered or modular hierarchy:

Modularization: Each expert or prompt encodes knowledge specific to a subdomain, data manifold, hierarchy level, modality, or task.
Structure-aware Routing: Inputs are dynamically matched to one or multiple experts using learned or rule-based routing strategies, often leveraging structural or semantic information.
Orthogonality/Regularization: Expert prompts or components are regularized to minimize redundancy (e.g., soft orthogonality constraints), maximizing diversity and specialization.
Functional Composition: The combination of expert outputs is governed by aggregation rules, such as weighted voting, probabilistic mixtures, or consensus mechanisms.

HEP instantiations are closely related to bounded rationality and information-theoretic meta-learning frameworks (Hihn et al., 2019), mixture-of-expert architectures, and hierarchy-guided prompting from human cognition and organizational workflows (Liu et al., 30 May 2024, Budagam et al., 18 Jun 2024).

2. Methodological Instantiations

HEP methodologies appear in numerous domains, each leveraging hierarchical decomposition and expert specialization according to the task structure:

a) Mixture-of-Experts with Prompt Tuning

GMoPE introduces per-expert prompt vectors in GNNs, with each expert specializing via unique prompts and dynamic structure-aware routing (Wang et al., 5 Nov 2025). Soft orthogonality constraints encourage prompt diversity, preventing expert collapse.
MEPT extends this to deep transformer networks, using per-layer mixtures of router and shared expert prompts, with sparse top-1 activation for manifold adaptive tuning (Zeng et al., 31 Aug 2025). This allows for dynamic selection of neural pathways best-suited to heterogeneous or drifting data distributions.

b) Explicit Hierarchical Prompt Structuring

Hierarchical Prompt Tuning (HPT, HPT++): Prompts are stratified into low-, high-, and global-level components, capturing structured linguistic (entity-relationship graphs), semantic summary, and global context for vision-LLM adaptation (Wang et al., 2023, Wang et al., 27 Aug 2024). Relationship-guided attention and multi-granularity prompt generation further refine generalization and transfer.
Ingredient Recognition: Multi-level expert prompt tuning across coarse, medium, and fine ingredient hierarchies enables efficient adaptation and evaluation at all concept granularities (Gui et al., 14 Apr 2025).

c) Hierarchical Multi-Agent and Expert Workflow

HEP for Sequential Decision-Making: In complex settings such as real-time strategy games, HEP organizes prompts to separate expert tactical knowledge from hierarchical prioritization of actions, yielding LLMs capable of defeating the "Elite" agent in TextStarCraft II (Li et al., 16 Feb 2025).
Hierarchical Multi-Agent Workflow (HMAW): LLMs emulate organizational roles (e.g., CEO, Manager, Worker), passing optimized guidance down a layered pipeline for zero-shot prompt optimization (Liu et al., 30 May 2024).
Error-tolerant Interactive Systems: In high-stakes/ambiguous environments (e.g., medical image segmentation or Med-VQA), HEP frameworks decompose reasoning into multiple stages: initial specialist rationale, verification, and consensus by a prompt-driven expert committee (Gao et al., 23 Jun 2025, Liu et al., 18 Dec 2024).

d) Prompt-Driven Continual Learning and Knowledge Fusion

Continual Learning: H-Prompts hierarchically structure prompts into class, task, and general levels, coordinating Bayesian alignment, cross-task knowledge excavation, and generalized knowledge synthesis to mitigate catastrophic forgetting (Zuo et al., 21 Jan 2024).
Knowledge Fusion: Hierarchy-oriented prompts embedded with index system relationships guide LLMs in entity-term alignment under few-shot learning constraints (Lu et al., 2023).

3. Technical Mechanisms

HEP systems employ several algorithmic constructs and regularizations:

Mechanism	Technical Role	Typical Formulations/Examples
Prompt Vector Specialization	Expert conditioning, domain/structure encoding	Per-expert vectors, per-layer embeddings (Wang et al., 5 Nov 2025, Zeng et al., 31 Aug 2025)
Dynamic Routing	Selects experts/prompts based on input properties	Loss-based soft/hard routing, entropy/confidence weighting
Orthogonality or Diversity	Prevents expert collapse, encourages specialization	Soft orthogonality loss, mutual information maximization (Wang et al., 5 Nov 2025)
Hierarchical Aggregation	Layered fusion of knowledge or outputs	Weighted voting, consensus reasoning (Liu et al., 18 Dec 2024, Gao et al., 23 Jun 2025)
Relationship-Guided Attention	Encodes structural/graph knowledge	Additive/multiplicative bias in self-attention (Wang et al., 2023, Wang et al., 27 Aug 2024)
Cross-Scale/Multi-Granularity Fusion	Shares features between hierarchy levels	Knowledge mappers, layer proxies (Zheng et al., 20 Jul 2025)

Mathematically, most HEP architectures are formalized by loss functions that combine expert- or prompt-specific task/objective losses with diversity constraints and aggregate output predictions by weighted attention or certainty.

4. Empirical Evidence and Applications

HEP-based systems have consistently demonstrated strong empirical performance across modalities:

Graph Learning: GMoPE outperforms state-of-the-art graph prompt and MoE baselines on link prediction, classification, and transfer; in some tasks exceeding even full-parameter fine-tuning—while adapting with <1% of the parameters (Wang et al., 5 Nov 2025).
Multimodal Recognition: HPT and HPT++ show superior accuracy and generalization on vision-language tasks, especially for unseen classes and cross-domain settings, driven by explicit structured prompt hierarchies (Wang et al., 2023, Wang et al., 27 Aug 2024).
Natural Language Processing: Mixture-of-prompts methods (MoP) achieve a pairwise win rate of 81% against earlier prompt search approaches via expert clustering and instruction optimization (Wang et al., 28 Jun 2024).
Decision-Making and Control: HEP pipelines for LLM agents achieve new records in long-horizon environments like TextStarCraft II, enabling previously unattainable expert-level play (Li et al., 16 Feb 2025).
Continual and Few-Shot Learning: Hierarchical prompt partitioning yields significant reductions in forgetting and boosts in average accuracy under rehearsal-free continual learning protocols (Zuo et al., 21 Jan 2024), as well as few-shot entity alignment in biomedical domains (Lu et al., 2023).
Interactive Reasoning and Segmentation: Hierarchical consensus frameworks (e.g., SafeClick, MedCoT) transform fundamentally prompt-dependent processes into robust, committee-driven systems, increasing accuracy and interpretability in clinical contexts (Gao et al., 23 Jun 2025, Liu et al., 18 Dec 2024).

5. Limitations, Challenges, and Future Directions

While HEP systems provide systematic advances over single-level or monolithic prompting approaches, significant challenges and open questions remain:

Expert Assignment and Routing: The determination of optimal expert assignment or routing remains an open problem—current schemes are typically based on loss, structure, or semantic similarity. Adaptive, context-aware, or data-driven routing remains a research frontier.
Expert Collapse and Redundancy: Despite orthogonality constraints, experts may converge in function on homogeneous or insufficiently partitioned data. Further joint optimization of partitioning and specialization is necessary.
Scalability and Overhead: Increasing the number of experts or hierarchy levels can result in computational and memory overhead during both training and inference. Dynamic pruning or conditional computation schemes may be required in large-scale HEPs.
Interpretability: Although HEPs facilitate more auditable and modular reasoning, the decision rationale of expert selection and integration can still lack transparency and may require additional tools for explanation and debugging.
Integration with Retrieval and Causal Reasoning: Prompt structures that encode explicit causal chains, monotonicity, or factual dependencies (e.g., expert mental models for hallucination prevention (Kovalerchuk et al., 13 Sep 2025)) represent a promising area for robust, fact-grounded HEPs.
Unification Across Modalities: Cross-modal and multi-agent HEPs (e.g., HiCroPL for vision-language (Zheng et al., 20 Jul 2025)) suggest broad applicability but also raise issues of representational alignment, knowledge flow, and inter-modality specialization.

6. Relation to Broader Hierarchical and Expert-Based Paradigms

The HEP paradigm generalizes and extends earlier approaches in expert systems, bounded rational meta-learning (Hihn et al., 2019), mixture-of-expert networks, hierarchical reinforcement learning, and prompt ensemble learning. HEP is distinguished by:

The explicit structuring of expert knowledge via learnable or template-based prompts at varying granularity or modality.
The integration of diverse specialization, either through parameter-efficient adaptation (prompt tuning) or dynamic modularity (MoE, consensus mechanisms).
The design of architecture-agnostic interfaces, enabling plug-and-play adaptation of pretrained foundation models (language, vision-language, or graph models) to a wide range of downstream tasks.

In contemporary research, HEP is being formalized as both an architectural and methodological principle for scalable, generalizable, and interpretable AI systems, with state-of-the-art results and growing adoption in both academic and real-world deployments (Wang et al., 5 Nov 2025, Gui et al., 14 Apr 2025, Wang et al., 2023, Gao et al., 23 Jun 2025, Liu et al., 18 Dec 2024).