HiVA: Self-organized Hierarchical Variable Agent via Goal-driven Semantic-Topological Evolution
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912559859236864 |
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| author | Tang, Jinzhou Zhang, Jusheng Lv, Qinhan Liu, Sidi Yang, Jing Tang, Chengpei Wang, Keze |
| author_facet | Tang, Jinzhou Zhang, Jusheng Lv, Qinhan Liu, Sidi Yang, Jing Tang, Chengpei Wang, Keze |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00189 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HiVA: Self-organized Hierarchical Variable Agent via Goal-driven Semantic-Topological Evolution Tang, Jinzhou Zhang, Jusheng Lv, Qinhan Liu, Sidi Yang, Jing Tang, Chengpei Wang, Keze Artificial Intelligence Multiagent Systems 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. |
| title | HiVA: Self-organized Hierarchical Variable Agent via Goal-driven Semantic-Topological Evolution |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2509.00189 |