Modular Evaluation Architecture

• Modular Evaluation Architecture is a design approach that partitions complex systems into loosely coupled modules with defined interfaces for scalable and reproducible evaluation.
• It employs techniques like game-theoretic Shapley Value attribution and exhaustive configuration sweeps to quantify both individual and synergistic module contributions.
• This architecture supports diverse applications—from quantum benchmarking to LLM agent pipelines—by enabling plug-and-play module integration and rigorous performance aggregation.

Modular Evaluation Architecture refers to the systematic design, composition, and assessment of complex systems where functionality is partitioned across loosely coupled, well-defined modules. This approach underpins scalable, interpretable, and reproducible evaluation across machine learning, AI agent pipelines, quantum benchmarking, network simulation, hierarchical composite systems, and multi-agent architectures. Key principles include interface standardization, component-level metrics, rigorous aggregation, game-theoretic attribution (Shapley Value), and domain-specific decoupling, as evidenced in frameworks such as CapaBench, SimBricks, FreeEval, OmniEvalKit, Auto-Eval Judge, QED-C modular quantum benchmarking, and hierarchical modular system evaluation (Yang et al., 1 Feb 2025, Li et al., 2020, Yu et al., 9 Apr 2024, Zhang et al., 9 Dec 2024, Bhonsle et al., 7 Aug 2025, Patel et al., 9 Oct 2025, Levin, 2013).

1. Fundamental Principles of Modular Evaluation

Modular evaluation architectures are predicated on decomposing complex workflows into minimally interdependent modules, each with a narrowly defined interface and responsibility. Evaluation proceeds both at the module level (individual capability metrics, attribution) and at the system level (global performance via structured aggregation). Standardization of interfaces is essential: in quantum benchmarking, distinct modules handle problem generation, circuit execution, and results analysis, communicating via type-defined protocols and algebraic contracts (Patel et al., 9 Oct 2025). For agentic LLM systems, modules for planning, reasoning, action execution, and reflection interface via repeated calls but are implemented so that any can be replaced independently (Yang et al., 1 Feb 2025).

A core feature is the ability to assess marginal and synergistic effects of each module. Techniques include exhaustive configuration sweeps, plug-and-play registration APIs, and game-theoretic value attribution (notably the Shapley Value for cooperative game modeling). Rigorous fidelity, reproducibility, and efficiency demands—such as those in FreeEval and OmniEvalKit—drive the adoption of unified abstraction layers, declarative pipeline configuration, and functional modular APIs (Yu et al., 9 Apr 2024, Zhang et al., 9 Dec 2024).

2. Interface Design and Component Decoupling

Robust modular evaluation architectures specify strict contracts at module boundaries, enabling seamless substitution, local optimization, and scalable integration of disparate backends, simulators, or analysis engines. QED-C quantum benchmarking formalizes interfaces for problem generation (pure function emitting gate sequences and metadata), execution (calls to hardware or simulator, returning measurement distributions), and analysis (fidelity, plot generation) (Patel et al., 9 Oct 2025). SimBricks enforces narrow “waist” interfaces (PCIe and Ethernet), wrapping best-of-breed simulators for hosts, devices, and networks and synchronizing via timestamped message-passing and efficient slack-based protocols (Li et al., 2020).

Decoupling is managed via runtime orchestrators or DAG-based execution engines, as in OmniEvalKit’s Static Builder and Dynamic Data Flow Engine (Zhang et al., 9 Dec 2024). Modular agent pipelines for LLMs instantiate discrete module templates (e.g., Planning, Reasoning) and permit targeted replacement with alternate models for isolated capability assessment (Yang et al., 1 Feb 2025). Each interface ensures that module logic and orchestration policy remain encapsulated, facilitating direct module-by-module evaluation and future extensibility.

3. Attribution, Aggregation, and Evaluation Metrics

Modular attribution assigns quantitative scores to modules based on their marginal effect on task success, handling both independent and combinatorial contributions. CapaBench employs a cooperative game-theoretic approach using the Shapley Value: for n modules, all 2ⁿ configurations (combining test variants and defaults) are evaluated, and the marginal contribution of each module across all insertion orders is aggregated (Yang et al., 1 Feb 2025). Empirical findings reveal domain-dependent module importance—Reasoning dominates in cognitive tasks, Action Execution in precision domains, Reflection is typically low-impact unless external guidance is present.

In hierarchical modular system evaluation, local component quality is assessed using a variety of scales (quantitative, ordinal, multicriteria, poset-like), then transformed and integrated up the hierarchy via weighted sums, Pareto-layering, outranking methods, or poset fusion (Levin, 2013). For quantum architectures, fidelity metrics and execution times are computed per circuit and then mapped to overall performance figures by natural transformation, independent of backend (Patel et al., 9 Oct 2025). In multi-agent frameworks, modular aggregation involves per-checklist assessments, hierarchical composition, and threshold-based verdict generation (e.g., unanimous or majority rules) (Bhonsle et al., 7 Aug 2025).

4. Scalability, Extensibility, and Domain Adaptation

One primary benefit of modular evaluation architectures is extensibility. New benchmarks, metrics, or modalities can be integrated by implementing or registering corresponding module interfaces, requiring no modification to core orchestration logic. OmniEvalKit demonstrates this via plugin-based registration, scalable support for 100+ models and 50+ datasets (Zhang et al., 9 Dec 2024). QED-C allows instant inclusion of new quantum APIs (Qiskit, CUDA-Q, Cirq, pyGSTi) by adapter classes adhering to the ICircuitExecutor interface, thus reducing ecosystem fragmentation and optimization bottlenecks (Patel et al., 9 Oct 2025). FreeEval’s dynamic/interactive module API accommodates debate, peer-review, and multi-agent scoring with no pipeline rewrite (Yu et al., 9 Apr 2024).

However, scalability is conditioned on computational complexity. For exhaustive module attribution (e.g., full Shapley sweeps), cost grows exponentially with module count, necessitating approximate sampling for n ≫ 4 (Yang et al., 1 Feb 2025). Practical frameworks mitigate overhead via batch processing, caching, lazy loading, and concurrent worker management (Zhang et al., 9 Dec 2024).

5. Domain-Specific Architectures and Application Cases

Modular evaluation has proven especially impactful across several domains:

• LLM Agents: CapaBench and Modular Agentic Planner establish distinct modules for planning, reasoning, execution, and reflection, each separately prompted and evaluated. Marginal attributions via Shapley Value guide capability investment—modules ranked highly consistently deliver maximal performance gains when combined (Yang et al., 1 Feb 2025, Webb et al., 2023).
• Quantum Benchmarking: QED-C divides into problem generation, execution, analysis; supports dynamic-circuit and quantum RL variants with zero interface modification (Patel et al., 9 Oct 2025). Modular designs enable hardware-agnostic evaluation and seamless migration to new APIs and analysis engines.
• Network Simulation: SimBricks demonstrates scalable “stitching” of component simulators (CPUs, NICs, network fabric) via stable interfaces and conservative synchronization, supporting 1000+ node testbeds (Li et al., 2020).
• Hierarchical Systems: HMMD-style poset evaluation supports vector and interval-multiset metrics, integrating compatibility constraints and custom aggregation operators (Levin, 2013).
• Multi-Agent Evaluation: Auto-Eval Judge and LLM-powered evaluation frameworks employ piping of specialized agents, implementing criterion generation, artifact parsing, verification, and verdict fusion for stepwise, domain-independent assessment (Bhonsle et al., 7 Aug 2025, Wang et al., 13 Aug 2025).

6. Limitations, Open Challenges, and Future Directions

Current modular evaluation architectures encounter scalability barriers in exhaustive attribution, subjective metric reliability (especially in experience-oriented modules), cross-modal nuances lost in translation, and domain adaptation requiring re-annotation. Reflection modules in LLM agent pipelines display limited immediate gain, potentially masking longer-term benefits in curriculum learning or interactive correction (Yang et al., 1 Feb 2025). Dynamic circuit compilation in quantum workflows remains an open integration challenge (Patel et al., 9 Oct 2025), and formal proofs of universal backend–analysis commutativity in abstract frameworks are outstanding.

Suggested research directions include adaptive prompting, hierarchical structure exploitation (to reduce combinatorial cost), reinforcement-learning–based meta-agents for conflict resolution, active learning for incremental module enhancement, integration of raw multimodal features, and formalization of algebraic module interaction. Evolving modular architecture standards will shape evaluation reliability and system optimization across increasingly heterogeneous, large-scale AI, quantum, and agentic domains.

7. Synthesis and Impact

The modular evaluation architecture paradigm delivers interpretability, maintainability, and prescriptive optimization leverage by transparently partitioning capabilities, employing quantitative attribution, and enforcing standardized interfaces. Whether employing Shapley Value analysis for LLM agent pipelines (Yang et al., 1 Feb 2025), algebraic contracts in quantum benchmarking (Patel et al., 9 Oct 2025), or hierarchical aggregation in composite systems (Levin, 2013), modular evaluation enables principled assessment and rapid adaptation in the face of domain complexity and technological change. This framework underpins the current generation of high-impact benchmarks and evaluation toolkits in AI, quantum computing, and networked systems, supporting reproducible, robust, and scalable research.