arXiv:2511.03023v1 Announce Type: new
Abstract: Open data repositories hold potential for evidence-based decision-making, yet are inaccessible to non-experts lacking expertise in dataset discovery, schema mapping, and statistical analysis. Large language models show promise for individual tasks, but end-to-end analytical workflows expose fundamental limitations: attention dilutes across growing contexts, specialized reasoning patterns interfere, and errors propagate undetected. We present PublicAgent, a multi-agent framework that addresses these limitations through decomposition into specialized agents for intent clarification, dataset discovery, analysis, and reporting. This architecture maintains focused attention within agent contexts and enables validation at each stage. Evaluation across five models and 50 queries derives five design principles for multi-agent LLM systems. First, specialization provides value independent of model strength–even the strongest model shows 97.5% agent win rates, with benefits orthogonal to model scale. Second, agents divide into universal (discovery, analysis) and conditional (report, intent) categories. Universal agents show consistent effectiveness (std dev 12.4%) while conditional agents vary by model (std dev 20.5%). Third, agents mitigate distinct failure modes–removing discovery or analysis causes catastrophic failures (243-280 instances), while removing report or intent causes quality degradation. Fourth, architectural benefits persist across task complexity with stable win rates (86-92% analysis, 84-94% discovery), indicating workflow management value rather than reasoning enhancement. Fifth, wide variance in agent effectiveness across models (42-96% for analysis) requires model-aware architecture design. These principles guide when and why specialization is necessary for complex analytical workflows while enabling broader access to public data through natural language interfaces.
Expert Commentary: PublicAgent Framework for Multi-Agent Language Models
The advent of large language models has shown promising potential for various tasks, including dataset discovery and analysis. However, as pointed out in the article, end-to-end analytical workflows using such models can present challenges due to attention dilution, specialized reasoning patterns, and error propagation.
The PublicAgent framework offers a novel approach to address these limitations by decomposing the workflow into specialized agents for different tasks such as intent clarification, dataset discovery, analysis, and reporting. This multi-agent architecture helps maintain focused attention within specific contexts and allows for validation at each stage of the workflow.
One of the key insights derived from the evaluation of PublicAgent across different models and queries is the importance of specialization in improving the effectiveness of the overall system. The results show that even the strongest model benefits from specialized agents, with high agent win rates regardless of model scale.
The division of agents into universal (discovery, analysis) and conditional (report, intent) categories is another crucial design principle highlighted in the study. Universal agents exhibit consistent effectiveness, while conditional agents show varying performance depending on the model used.
Furthermore, the evaluation results underscore the critical role of each agent in the workflow, with catastrophic failures occurring when essential agents are removed. This emphasizes the necessity of a well-balanced and specialized architecture for complex analytical workflows.
The findings also suggest that the benefits of the architectural design of the PublicAgent framework persist across different levels of task complexity, indicating the value of efficient workflow management rather than reasoning enhancement.
Overall, the principles derived from the evaluation of the PublicAgent framework provide valuable insights into the importance of specialization in multi-agent language models for complex analytical workflows. By leveraging these design principles, researchers and practitioners can enhance the accessibility of public data through natural language interfaces, enabling more effective and efficient decision-making processes.