Whole-Process Modular Evaluation

• Whole-process modular evaluation is an approach that decomposes complex systems into independent modules with defined inputs and outputs for transparent assessments.
• It uses structured sub-processes for artifact parsing, step validation, and aggregation to yield a comprehensive, reproducible verdict.
• This method underpins applications in AI benchmarking, program analysis, and network simulation by ensuring systematic and traceable evaluations.

Whole-process modular evaluation refers to an end-to-end methodology for decomposing, analyzing, and aggregating measurements of composite (modular) systems, such that each component or phase of the system is evaluated in its own context and the results are systematically integrated into a holistic verdict. Unlike ad hoc or single-output evaluation protocols, whole-process modular approaches emphasize transparency, extensibility, and reproducibility by factoring the entire evaluation pipeline into well-defined, interoperable modules. This paradigm is pervasive in modern agentic AI benchmarking, hierarchical systems analysis, context-sensitive program analysis, and large-scale simulation frameworks (Bhonsle et al., 7 Aug 2025, Yu et al., 9 Apr 2024, Li et al., 2020, Levin, 2013).

1. Foundational Principles and Architectures

Whole-process modular evaluation is grounded in a strict factorization of the evaluation pipeline: every phase—input construction, system invocation, intermediate artifact parsing, scoring, meta-evaluation, and final judgment—is encapsulated as an independent module with clearly defined inputs and outputs (Yu et al., 9 Apr 2024). In AI agent evaluation, for instance, the process typically comprises:

• Task decomposition (criteria generation): the overall task $T$ is decomposed into a sequence of sub-tasks or checklist items $\{t_1, ..., t_n\}$, each precisely specifying an explicit requirement (Bhonsle et al., 7 Aug 2025).
• Artifact parsing/extraction: system outputs (logs, traces, intermediate artifacts) are segmented and indexed, with relevant “proof” snippets extracted for each requirement (Bhonsle et al., 7 Aug 2025).
• Step validation: each (requirement, artifact) pair is validated, often using specialized logic for different subtypes (e.g., factual vs. reasoning vs. coding) (Bhonsle et al., 7 Aug 2025), or via an LLM- or rule-based check (Yu et al., 9 Apr 2024).
• Aggregation: individual verdicts or scores are combined according to explicit rules—typically conjunctive, disjunctive, or via utility aggregators—to yield the overall system assessment.

This modular pattern extends naturally across domains. In context-sensitive program analysis, for example, the pipeline comprises per-module fixpoint computations, inter-module propagation, and staged integration of analysis results (Garcia-Contreras et al., 2018). In network/system simulation, component simulators (hosts, devices, networks) are joined by precise APIs, and their outputs are synchronized and merged in a deterministic, scalable fashion (Li et al., 2020).

2. Formalization, Notation, and Workflow

The core formalism underlying whole-process modular evaluation is a hierarchical, compositional mapping:

• The global task/system $T$ is factored as $T \rightarrow \{t_1, ..., t_n\}$.
• For each sub-task $t_i$, there exists a validation function $V_i(o_i, r_i, p_i) \to \{0,1\}$, where $o_i$ is the system’s output fragment, $r_i$ is the reasoning trace, and $p_i$ the extracted proof or artifact (Bhonsle et al., 7 Aug 2025).
• Aggregation is performed by $E = f(V_1, ..., V_n)$, with $E \in \{0,1\}$ under an all-or-nothing (conjunctive) regime: $E = 1$ only if $V_i=1$ for all $i$ (Bhonsle et al., 7 Aug 2025).

In broader modular system contexts, each module $S_i$ is assigned a local score $e_i$ in a chosen assessment scale (quantitative, ordinal, multicriteria), and the integration into total system score is carried out via an explicit mapping (utility sum, Pareto front, poset composition, etc.) (Levin, 2013).

Pipelines are typically specified declaratively; for instance, FreeEval implements a full-stack modular pipeline as a YAML/JSON config file listing ordered steps (dataset load, inference, scoring, meta-eval), each as a class implementing a standard interface (preprocess, run, postprocess) (Yu et al., 9 Apr 2024).

3. Core Modules and Their Roles in Representative Frameworks

A broad survey of recent research identifies several canonical modules that appear across whole-process modular evaluations, each responsible for a distinct sub-phase:

Framework	Key Modules	Integration/Output
Auto-Eval Judge (Bhonsle et al., 7 Aug 2025)	Criteria Generator, Artifact Parser, Retriever, Criteria Check Composer (C3), Verdict Generator	Binary task completion verdict, justifications
FreeEval (Yu et al., 9 Apr 2024)	Dataset, Step, Config (dynamic/static eval, meta-eval, LLM-invoke)	Final metrics/visualizations, step-by-step trace
SimBricks (Li et al., 2020)	Host/Device/Network Simulators, Synchronization, API adapters	End-to-end system/experiment output
Modular Analysis (Garcia-Contreras et al., 2018)	Local Analyzer, Boundary Propagation, Pruning/Worklist Scheduler	Program-wide analysis graph
Hierarchical Systems (Levin, 2013)	Local assessment, scale transformation, integration/fusion	Global system ranking/poset

Each module is designed for isolation, transparency, and easy replacement or extension.

4. Evaluation Metrics, Aggregation, and Reliability

The success of whole-process modular evaluation depends on explicit, reproducible metrics at each stage. Common classes include:

• Binary & multiclass classification (accuracy, precision, recall, specificity): e.g., agentic task completion (Bhonsle et al., 7 Aug 2025), LLM pass/fail (Yu et al., 9 Apr 2024).
• Component-level/step scores: checklists with yes/no verdicts per criterion, logical/coding/factual question types, or low/high ordinal/interval grades (Bhonsle et al., 7 Aug 2025, Levin, 2013).
• Meta-evaluation: human annotation alignment, data contamination detection (Min-K% probability, average loss), bias and variance analysis (Yu et al., 9 Apr 2024).
• Collapse/specialization metrics (for modular neural networks): module utilization, misrouting, and alignment (e.g., collapse-avg, s_align) (Mittal et al., 2022).
• Integration strategies: all-or-nothing (strict), additive (utility), Pareto, poset lattice, or median-like multiset aggregation (Bhonsle et al., 7 Aug 2025, Levin, 2013).

The pipeline design is tightly coupled to reliability and reproducibility. Modular, configuration-driven execution (e.g., all experiment logic in plain-text config, explicit API boundaries, byte-level caching of LLM calls in FreeEval) guarantees that results are traceable and re-creatable under identical settings (Yu et al., 9 Apr 2024).

5. Applications and Empirical Validation

Whole-process modular evaluation frameworks have been demonstrated across multiple domains:

• Agentic Foundation Model Evaluation: Auto-Eval Judge outperforms LLM-as-a-Judge baselines on GAIA and BigCodeBench, exhibiting up to +10.5pp accuracy in human alignment via explicit sub-task criteria and modular aggregation (Bhonsle et al., 7 Aug 2025).
• LLM Benchmark Automation: FreeEval enables unified, efficient, and contamination-aware evaluation of open and proprietary LLMs, supporting meta-evaluation and dynamic/interactive tasks (Yu et al., 9 Apr 2024).
• Network System Simulation: SimBricks scales to 1,000+ hosts, integrating device-, host-, and network-level simulators via shared modular protocols, enabling cycle-accurate, end-to-end network systems evaluation (Li et al., 2020).
• Context-Sensitive Program Analysis: Modular incremental algorithms realize up to 10× speedups and 30–60% memory savings via per-module fixpoint iteration and global boundary propagation (Garcia-Contreras et al., 2018).
• Hierarchical System Design and Improvement: Integrated use of modular evaluation (scales, aggregation, poset formulation) drives system-level diagnosis and optimization via combinatorial models (knapsack, multiple-choice, Steiner extension) (Levin, 2013, Levin, 2013).

6. Best Practices, Limitations, and Design Recommendations

Best practice guidelines synthesized from recent frameworks include:

• Encapsulation: define strict interfaces per module and pass context via a shared state object or API (Yu et al., 9 Apr 2024).
• Transparency: specify all pipeline stages, dataset splits, scoring thresholds, and hyperparameters declaratively.
• Stepwise Decomposition: align sub-task decomposition with explicit requirements or capabilities rather than monolithic decision functions. Coverage of criteria sets correlates with evaluation precision (Bhonsle et al., 7 Aug 2025).
• Rigorous Aggregation: employ aggregation strategies that enforce strict (all-or-nothing) or interpretable utility functions to maintain fidelity with human judgment (Levin, 2013, Bhonsle et al., 7 Aug 2025).
• Extensibility/Composability: support easy replacement of scoring/validation modules, swap-in of new meta-evaluation methods, and expansion to new domains (Yu et al., 9 Apr 2024).
• Monitoring and Meta-evaluation: incorporate contamination and bias detection, and human or external alignment checks as first-class steps in the process (Yu et al., 9 Apr 2024).

Identified challenges include module collapse and specialization sub-optimality in neural systems (Mittal et al., 2022), difficulty in capturing global context across non-orthogonal modules, and the necessity for principled scale transformation and integration to preserve monotonicity and interpretability (Levin, 2013).

7. Theoretical Significance and Future Directions

Whole-process modular evaluation is theoretically motivated by the recognition that composite systems, whether agentic, analytic, or engineered, defy one-shot “end-to-end” evaluation due to task complexity, data leakage risks, lack of transparency, or the potential for module-level optimization failure (Mittal et al., 2022, Yu et al., 9 Apr 2024). Its rise reflects demand for extensible, human-aligned, and efficient frameworks as AI systems and modular architectures proliferate.

A plausible implication is that future work will extend these modular pipelines to hierarchical settings (multi-level decomposition), richer meta-evaluation (adversarial robustness, fairness), and automated learning of optimal aggregation/fusion strategies. The modular evaluation paradigm also poses foundational questions for explainability and emergent specialization in AI and engineered systems.