Scholarlink

This study explores a paradigm shift in AI system design for academic tasks by comparing centralized GPT-4o architectures with decentralized networks of specialized lightweight language models. Focusing on scholarly workflows including literature synthesis, citation formatting, technical translation, and code generation, we demonstrate how a hierarchical multi-agent framework significantly reduces computational costs while maintaining task accuracy. Our experiments reveal that distributed systems using GPT-4o mini achieve 6.8 token efficiency and 98% cost reduction compared to monolithic GPT-4o implementations, while simultaneously eliminating contextual interference errors inherent in unified architectures. The proposed supervisor-subordinate agent structure not only enhances economic feasibility but also improves result consistency through task isolation, particularly evident in citation formatting (73% vs. 68% accuracy) and code generation tasks. These findings challenge the conventional preference for high-parameter models, advocating instead for optimized task partitioning strategies in resource-constrained academic environments. Practical implementations demonstrate 30 — 40% time savings in research preparation workflows, positioning distributed AI architectures as sustainable solutions for institutional adoption.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Cost-Efficient Distributed AI Architectures: Lightweight LLM Agents vs GPT-4o Monoliths in Academic Workflows

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Roman V. Dushkin,
  • Valentin V. Klimov,
  • Yevgeny A. Anzin,
  • Kirill R. Dushkin,
  • Vladimir A. Zvorygin

摘要

This study explores a paradigm shift in AI system design for academic tasks by comparing centralized GPT-4o architectures with decentralized networks of specialized lightweight language models. Focusing on scholarly workflows including literature synthesis, citation formatting, technical translation, and code generation, we demonstrate how a hierarchical multi-agent framework significantly reduces computational costs while maintaining task accuracy. Our experiments reveal that distributed systems using GPT-4o mini achieve 6.8 token efficiency and 98% cost reduction compared to monolithic GPT-4o implementations, while simultaneously eliminating contextual interference errors inherent in unified architectures. The proposed supervisor-subordinate agent structure not only enhances economic feasibility but also improves result consistency through task isolation, particularly evident in citation formatting (73% vs. 68% accuracy) and code generation tasks. These findings challenge the conventional preference for high-parameter models, advocating instead for optimized task partitioning strategies in resource-constrained academic environments. Practical implementations demonstrate 30 — 40% time savings in research preparation workflows, positioning distributed AI architectures as sustainable solutions for institutional adoption.