## 5 Discussion
The significant variation among models on complex attributes highlights a critical consideration for future multi-agent system (MAS) design [65; 66; 67] by selecting complementary models to leverage their individual strengths effectively. For example, models  
Figure 4: Heatmap of pairwise model agreement. (**a**) Binary field (brain stimulation used). This panel shows the agreement between models for the binary field “brain_stimulation_used”. The consistently high agreement scores (0.99–1.00) indicate excellent reliability across all model pairs when identifying the presence or absence of brain stimulation in clinical trials. (**b**) Categorical field (primary type). This panel displays agreement for the categorical field “primary_type”, which classifies the type of brain stimulation used. Agreement scores range from 0.82 to 0.95 demonstrated good but lower agreement compared to binary fields, and reflected the increased complexity of categorical classification. (**c**) Numeric field (intensity). This panel illustrates agreement for the numeric field “intensity” (stimulation strength). The notably lower agreement scores (0.62–0.77) highlight the challenge models face in consistently extracting precise numeric values from clinical trial descriptions. like DeepSeek-Distill [68] showed high accuracy in extracting numeric parameters such as intensity and duration, whereas models such as Meta Llama excelled in identifying multi-arm trial designs or detailed anatomical descriptions [69; 70]. These insights underscore the value of a combined approach in future MAS development, tailored to specific data extraction tasks to optimize accuracy and consistency. Leveraging these model capabilities can significantly advance aging research by providing clearer insights into current clinical trials, enabling medical professionals to better understand aging processes and variability across different populations [71; 72; 73]. Furthermore, optimized LLM-based systems could greatly improve patient education [74; 75], providing access not only to foundational knowledge but also to current and promising clinical trials. This approach could facilitate better communication among patients, researchers, and medical providers, thereby enhancing the overall effectiveness of aging interventions [76]. A practical implication for aging research is that multi-agent LLM pipelines can significantly enhance the current understanding of aging processes by systematically identifying the current state of clinical trials and highlighting variations across different populations [77]. Clinically tuned LLM systems could greatly improve patient education, as they can incorporate up-to-date clinical trial data and provide accessible, detailed information to patients, researchers, and medical providers alike. This comparative analysis not only establishes benchmarks for LLM performance in clinical trial extraction, but also emphasizes the ongoing necessity of human oversight to reconcile ambiguous outputs and address limitations inherent in current LLM technology [78], including susceptibility to misinterpretation and hallucination [14; 79; 80]. While RAG strategies offer promising enhancements for accuracy, implementing RAG with extensive clinical trial datasets is not always practical or feasible. Consequently, future research should focus on optimizing foundational LLMs through specialized prompt engineering and domain-specific ontologies [81]. A deeper understanding of foundational model performance will guide effective RAG design choices, ensuring that multi-agent systems developed are precise, practical, and well suited to advanced clinical applications and precision medicine. Recognizing these ethical and practical imperatives, we implemented an explicit human-in-the-loop framework within our multi-LLM ensemble pipeline. Although our approach is not a true multi-agent system where agents dynamically collaborate, but rather an ensemble of independently operated LLMs, structured human oversight remains integral.