Domain-Independent Multi-Agent Framework

• Domain-Independent Multi-Agent Framework (DIMF) is a modular system that enables heterogeneous agents to collaboratively solve tasks across varied domains without rewriting core logic.
• The framework employs pluggable interfaces, message-driven workflows, and configurable pipelines to support rapid domain adaptation and scalable multi-agent coordination.
• DIMF integrates abstract learning models, distributed planning, and privacy-preserving mechanisms to enhance generalization, efficiency, and robustness in multi-agent operations.

A Domain-Independent Multi-Agent Framework (DIMF) denotes a class of architectures, algorithms, and system-design principles that enable collections of heterogeneous agents to collaborate autonomously and efficiently across arbitrary domains, without requiring hard-coded logic, workflow, or structural revisions when adapted to new tasks or environments. Such frameworks abstract away domain-specific details, providing modular interfaces, configurable coordination mechanisms, and generalizable agent models, thereby supporting scalability, reusability, and rapid adaptation to diverse multi-agent problem instances.

1. Core Architectural Paradigms

Domain-independent multi-agent frameworks are grounded in architectural choices that abstract both agent logic and inter-agent coordination. The paradigms span decentralized peer-to-peer message-passing (e.g., Matrix (Wang et al., 26 Nov 2025)), hierarchical agent hierarchies (e.g., HEnRY (Lacavalla et al., 2024)), modular state–action planning (e.g., FMAP (Torreño et al., 2015), type-based planning (Zhu et al., 13 Feb 2025)), and neural orchestration (e.g., MetaOrch (Agrawal et al., 3 May 2025)). Across these, the unifying elements include:

• Abstract Agent Roles: Roles are specified at the configuration level; agent implementations are role-agnostic, with logic provided via plug-in modules or user subclassing (Matrix, FMAP).
• Message-driven Control and Data Flow: Tasks are represented as serializable message objects containing both control state and intermediate results, routing through agents according to flexible transition rules (Matrix).
• Distributed, Stateless Coordination: Orchestration is executed via distributed queues and decentralized scheduling (Matrix), or by ephemeral mediator agents in layered hierarchies (HEnRY).
• Pluggable Interfaces: Frameworks expose base classes (e.g., AgentActor, Orchestrator) with abstract methods for user-defined logic and message schemas.
• Configurable Pipelines: Domain adaptation is achieved by instantiating new agent types, transition rules, or message fields in a configuration file or meta-model—core code remains unchanged.

This abstraction supports application across collaborative synthetic data generation, trajectory prediction, reinforcement learning, cooperative perception, and distributed planning without architectural modification.

2. Agent Lifecycle, Scheduling, and Workflow Reconfigurability

DIMFs mandate modular and robust task progression and agent orchestration:

• Message-Driven Workflow Execution: In frameworks like Matrix, each task advances solely through the propagation of the Orchestrator message, which is updated at each agent and enqueued to the next role’s inbound queue. Heavy computation is offloaded to external scalable services, minimizing agent complexity (Wang et al., 26 Nov 2025).
• Hierarchical Control: HEnRY layers interactions among digital twins, facilitators, domain agents, and ephemeral mediators, organizing agent collaboration as a four-stage FSM (initialize context, solicit proposals, parallel refinement, aggregation) (Lacavalla et al., 2024).
• Refinement Planning and Privacy: FMAP agents collaboratively expand partial-order plans, maintaining privacy of private/partial state variables by sending only the relevant (or obfuscated) refinement projections to peers, preserving correctness and soundness in distributed plan search (Torreño et al., 2015).
• Reconfigurability: New workflows and tasks are supported by declarative updates to agent role lists, resource requirements, and transition logic—practically, this is realized via YAML (HEnRY), Hydra config (Matrix), or AUML meta-model (component-oriented approaches (Maalal et al., 2012)).

Load balancing is typically performed stochastically for scalability, with per-role actor instances sampled uniformly unless latency-aware pickers are required.

3. Abstracted Learning Models and Cross-Domain Transfer

DIMFs support generalization and knowledge re-use through unified learning representations:

• Scenario-Independent State Abstraction: In MARL frameworks, local and global observations are embedded into fixed-size, domain-independent feature tensors (e.g., Influence Maps), which feed into shared policy networks, enabling transfer learning across scenarios (Nipu et al., 2024).
• Curriculum Transfer Learning (CTL): Agents are trained sequentially on tasks of increasing complexity, with weight transfer and fine-tuning schedules, thereby accelerating convergence in new domains and supporting intra- and inter-agent knowledge transfer.
• Causal Disentanglement for Trajectory Prediction: AdapTraj explicitly models domain-invariant and domain-specific features for both focal and neighboring agents, employing orthogonality-enforcing losses and aggregator modules to ensure robust generalization under domain shift (Qian et al., 2023).
• Domain-Independence through Plug-and-Play Modules: Pluggable expert modules and aggregators enable integration with new backbone architectures and unseen domains without retraining the core logic, as demonstrated in AdapTraj (Qian et al., 2023).

Empirical results consistently report significant improvements in sample efficiency, generalization to held-out domains, and robustness to unseen scenarios (e.g., sample speedups of 2–2.5× in SMAC tasks (Nipu et al., 2024)).

4. Distributed Planning, Coordination, and Privacy

Planning-centric DIMFs decouple planning from domain specifics through state-space and protocol abstraction:

• State-Variable and Partial-Order Models: FMAP models world state via discrete variables and enforces cooperation through refinement planning over causal-link partial-order plans. All refinement and coordination protocols are domain-agnostic (Torreño et al., 2015).
• Novel Heuristics: The h_{DTG} heuristic leverages static Domain Transition Graphs and frontier states, independent of domain semantics.
• Distributed Coordination and Soundness: Agents exchange only the minimal necessary plan views, and threats are detected even under partial/private state observability, ensuring completeness and soundness.
• Spectrum of Approximate and Exact Planners: Type-based planner frameworks unify POMDP, MDP, QMDP, lookahead search, and bandit-based MCTS as points in a (depth, heuristic) space, allowing practitioners to choose the computational regime appropriate to problem scale (Zhu et al., 13 Feb 2025). This spectrum remains domain-independent, assuming only generic simulation and Bayesian update oracles.

In all such frameworks, core communication, coordination, and search algorithms are generic; only the action sets and initial variable instantiations reflect a particular problem instance.

5. Cross-Domain Cooperative Perception and Feature Alignment

Multi-agent perception under heterogeneous model assumptions is addressed by DIMFs that align and fuse representations across agents:

• Plug-and-Play Adapters: Lightweight modules such as MPDA's learnable feature resizer and sparse cross-domain transformer align intermediate encoder features before sensor fusion, circumventing constraints on model architecture uniformity (Xu et al., 2022).
• Adversarial and Statistical Alignment: Domain adaptation is performed by minimizing detection losses and adversarial cross-entropy or MMD-based statistical consistency penalties, ensuring that features from any source domain can be fused losslessly into ego feature space (MPDA (Xu et al., 2022), FDA (Li et al., 2024)).
• Privacy-Preserving and Sensor-Agnostic Design: Systems like FDA learn to compensate for and align features across independently trained agent encoders, requiring only intermediate outputs, and leave raw sensor data decentralized, preserving data privacy (Li et al., 2024).

Empirical results report absolute gains exceeding 8% AP in vehicle detection and large improvements when bridging cross-encoder and cross-data distribution gaps (Xu et al., 2022, Li et al., 2024).

6. Modularity, Extensibility, and Operational Best Practices

Domain-independence is operationalized via several meta-engineering practices:

• Component-Oriented Meta-Models: Library catalogs of generic agent types (reactive, communicative, cognitive, adaptive, intentional, rational) are defined in AUML and instantiated project-by-project through model-driven architecture transformations, supporting strict PIM/PSM separation (Maalal et al., 2012).
• Extensible APIs: Modular APIs enable dynamic agent registration, profile querying, task dispatch, and feedback ingestion, supporting continuous extensibility in multi-domain task settings (MetaOrch (Agrawal et al., 3 May 2025)).
• Declarative Configuration and Automated Code Generation: Complete MAS applications are generated from PIM meta-models and configuration descriptors, solidifying the separation of domain and implementation detail.
• Design Best Practices:
  • Organize agent hierarchies to mirror domain structure (HEnRY).
  • Use declarative templates for deployment scaling and resource optimization.
  • Ensure that all inter-agent protocols and event handling are modeled at a generic communication/interface abstraction.
  • Plan for elastic scaling and data provenance from the outset.

In summary, a Domain-Independent Multi-Agent Framework provides a systematized and extensible foundation for collaborative agentic workflows, abstracting domain logic into modular, configurable, and theoretically-sound protocols. Leading frameworks across synthetic data generation (Wang et al., 26 Nov 2025), collaborative planning (Torreño et al., 2015), transfer RL (Nipu et al., 2024), trajectory prediction (Qian et al., 2023), perception (Xu et al., 2022), orchestrated decision-making (Agrawal et al., 3 May 2025), and dialogue systems (Feng et al., 20 May 2025) have rigorously demonstrated the scalability, flexibility, and efficacy of the domain-independence paradigm in contemporary multi-agent systems research.