LLM-based SLR Evaluation Copilot

• LLM-based SLR Evaluation Copilot is an integrated intelligent system that automates and enhances systematic literature review evaluations using specialized multi-agent LLM architectures.
• It employs dedicated agents for protocol validation, methodological assessment, topic relevance, and duplicate detection to ensure strict adherence to guidelines like PRISMA.
• Empirical studies show an 84% agreement with expert annotations, demonstrating its scalability, reliability, and potential for streamlined interdisciplinary research.

A LLM-based SLR Evaluation Copilot is an integrated intelligent system that automates, standardizes, and enhances systematic literature review (SLR) evaluation, leveraging agentic LLM architectures to assess review quality across protocol, methodological, and topical criteria. These systems are designed to assist researchers in efficiently aggregating, validating, and scoring SLRs by incorporating interpretability, scalability, and adherence to formal guidelines such as PRISMA. The copilot’s core workflow fuses multi-agent LLM routines, database retrieval, and human-in-the-loop feedback to deliver rigorous, domain-agnostic assessments.

1. Agentic System Architecture and Operational Flow

The copilot is built on a Multi-Agent System (MAS) architecture in which dedicated LLM agents specialize in key dimensions of SLR evaluation (Mushtaq et al., 21 Sep 2025). Principal agent roles include Protocol Validation, Methodological Assessment, Topic Relevance Analysis, and Duplicate Detection. The agents process input SLR documents in parallel, applying distinct analytical pipelines:

• Protocol Validation Agent: Verifies alignment with review standards, ensuring documentation of eligibility, rationale, and search method.
• Methodological Assessment Agent: Examines the structural rigor and experimental validity of the review.
• Topic Relevance Agent: Assesses if review outcomes are consistent with stated research questions.
• Duplicate Detection Agent: Scans scholarly databases to filter redundant studies.

A human-in-the-loop layer receives composite outputs, enabling expert adjustment and ultimate adjudication. The architecture may be formalized by the following pipeline:

$\begin{array}{c} \textbf{SLR Document} \ \downarrow \ \begin{array}{ccc} \text{Protocol Validation} & \text{Method Assessment} & \text{Topic Relevance} \ \downarrow & \downarrow & \downarrow \ \multicolumn{3}{c}{\text{Duplicate Detection across Databases}} \ \downarrow \ \textbf{Composite Evaluation (with Expert Feedback)} \end{array} \end{array}$

This design enables modular, scalable, and interpretable SLR evaluation workflows.

2. Analytical Metrics, Evaluation Methodology, and Guideline Adherence

The copilot automates evaluation by mapping its analytical routines directly onto formal guidelines, notably the PRISMA standard (Mushtaq et al., 21 Sep 2025). Each agent applies task-specific metrics:

• Protocol Validation: PRISMA-based checklist verification.
• Methodological Assessment: LLM-driven analysis of research quality markers; typically involves criterion-referenced scoring mechanisms.
• Topic Relevance: Semantic similarity and question alignment assessment.
• Duplicate Detection: Retrieval-module leveraging cross-database query and overlap resolution.

These metrics are computed primarily by fine-tuned LLMs, which have undergone supervised refinement on labeled datasets reflecting PRISMA and related standards. Output consistency is enforced using deterministic scoring, allowing direct comparison with expert human annotation.

3. Empirical Performance, Robustness, and Domain Coverage

An initial empirical paper found that the LLM-based copilot delivered an agreement rate of 84% with expert PRISMA annotations across five published SLRs spanning diverse scientific domains (Mushtaq et al., 21 Sep 2025). Output robustness is demonstrated by the copilot’s consistent performance in the face of significant variation in review structure and subject matter. The system is able to maintain scoring accuracy and analytical coverage by abstracting its evaluations away from discipline-specific heuristics to domain-agnostic semantic understanding—thus supporting interdisciplinary workflows.

4. Scalability, Automation, and Domain-Agnostic Adaptation

A salient feature of the MAS copilot is its scalability and adaptability. By assigning agents to high-level evaluation tasks, the framework supports rapid reconfiguration for different disciplines or review formats with minimal retraining. The agents process SLRs using abstracted semantic logic rather than embed domain-specific rules, enabling scalability and application in interdisciplinary projects. Challenges remain in calibrating LLM models to subtleties of jargon and ensuring consistent mapping of evaluation criteria; continuous expert oversight is necessary but can be streamlined through feedback loops in the system.

5. Future Directions and Continuous Improvement

The paper indicates several avenues for future advancement (Mushtaq et al., 21 Sep 2025):

• Enhanced Model Tuning: Fine-tuning agents on broader and more heterogeneous datasets; improving reference coverage for domain adaptation.
• Agent Coordination: Research into more sophisticated inter-agent decision fusion to reduce manual oversight and enrich evaluation granularity.
• Optimized Feedback Loops: Development of advanced human-in-the-loop modules, facilitating better interface for expert critique and iterative system improvement.
• Integration of Additional Review Protocols: Systematically incorporating new guideline standards or review types (e.g., meta-analyses, scoping reviews).

This suggests accelerating progress in scaling automated SLR assessment and strengthening consistency and interpretability in large-scale evidence aggregation.

6. Impact on Evidence Synthesis and Research Workflows

The introduction of LLM-based MAS copilots into SLR evaluation has practical consequences for evidence synthesis. By automating critical, previously labor-intensive review tasks while maintaining guideline adherence and analytic rigor, such systems reduce bottlenecks for researchers synthesizing vast literature. The explicit, structured agentic design ensures transparency in scoring and decision-making, promoting both reproducibility and standardization in interdisciplinary research environments. The high observed agreement rate (84% with domain expert PRISMA scores) signals the feasibility of deploying these systems for scalable review quality verification (Mushtaq et al., 21 Sep 2025).

7. Limitations and Expert Oversight Requirements

While the MAS copilot demonstrates promise, challenges persist in ensuring nuanced evaluation across highly specialized domains and maintaining recall on subtle qualitative markers. Domain calibration is essential for accurate scoring, and human-expert engagement remains necessary for validating controversial or ambiguous review elements. The copilot’s outputs are subject to refinement through expert feedback, especially in cases where domain-specific context may elude semantic models. Continuous improvement through expanded dataset coverage and agent refinement is recommended.

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LLM-based SLR Evaluation Copilots, especially those utilizing a multi-agent architecture, exemplify the next stage in scalable, rigorous, and interpretable systematic review assessment. By formalizing agent specialization, grounding evaluation in established protocols, and integrating expert review, these systems have the capacity to streamline interdisciplinary workflows, increase consistency, and elevate the quality of evidence synthesis across domains (Mushtaq et al., 21 Sep 2025).