HiVA: Self-organized Hierarchical Variable Agent via Goal-driven Semantic-Topological Evolution

Autonomous agents play a crucial role in advancing Artificial General Intelligence, enabling problem decomposition and tool orchestration through Large Language Models (LLMs). However, existing paradigms face a critical trade-off. On one hand, reusable fixed workflows require manual reconfiguration upon environmental changes; on the other hand, flexible reactive loops fail to distill reasoning progress into transferable structures. We introduce Hierarchical Variable Agent (HiVA), a novel framework modeling agentic workflows as self-organized graphs with the Semantic-Topological Evolution (STEV) algorithm, which optimizes hybrid semantic-topological spaces using textual gradients as discrete-domain surrogates for backpropagation. The iterative process comprises Multi-Armed Bandit-infused forward routing, diagnostic gradient generation from environmental feedback, and coordinated updates that co-evolve individual semantics and topology for collective optimization in unknown environments. Experiments on dialogue, coding, Long-context Q&A, mathematical, and agentic benchmarks demonstrate improvements of 5-10% in task accuracy and enhanced resource efficiency over existing baselines, establishing HiVA's effectiveness in autonomous task execution.

Authors

• Jinzhou Tang Sun Yat-Sen University University of California, San Diego
• jusheng zhang
• Qinhan Lv Sun Yat-Sen University
• Sidi Liu Sun Yat-Sen University The Hong Kong University of Science and Technology (Guangzhou)
• Jing Yang National University of Defense Technology
• Chengpei Tang SUN YAT-SEN UNIVERSITY
• Keze Wang School of Computer Science and Engineering, Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing

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