Glia: A Human-Inspired AI for Automated Systems Design and Optimization
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912999746306048 |
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| author | Hamadanian, Pouya Karimi, Pantea Nasr-Esfahany, Arash Noorbakhsh, Kimia Chandler, Joseph ParandehGheibi, Ali Alizadeh, Mohammad Balakrishnan, Hari |
| author_facet | Hamadanian, Pouya Karimi, Pantea Nasr-Esfahany, Arash Noorbakhsh, Kimia Chandler, Joseph ParandehGheibi, Ali Alizadeh, Mohammad Balakrishnan, Hari |
| contents | Can AI autonomously design mechanisms for computer systems on par with the creativity and reasoning of human experts? We present Glia, an AI architecture for networked systems design that uses large language models (LLMs) in a human-inspired multi-agent workflow. Each agent specializes in reasoning, experimentation, and analysis, collaborating through an evaluation framework that grounds abstract reasoning in empirical feedback. Unlike prior ML-for-systems methods that optimize black-box policies, Glia generates interpretable designs and exposes its reasoning. When applied to a distributed GPU cluster for LLM inference, it produces new algorithms for request routing, scheduling, and auto-scaling that perform at human-expert levels in significantly less time, while yielding novel insights into workload behavior. Our results suggest that combining reasoning LLMs with structured experimentation, an AI can produce creative and understandable designs for complex systems problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_27176 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Glia: A Human-Inspired AI for Automated Systems Design and Optimization Hamadanian, Pouya Karimi, Pantea Nasr-Esfahany, Arash Noorbakhsh, Kimia Chandler, Joseph ParandehGheibi, Ali Alizadeh, Mohammad Balakrishnan, Hari Artificial Intelligence Computation and Language Distributed, Parallel, and Cluster Computing Can AI autonomously design mechanisms for computer systems on par with the creativity and reasoning of human experts? We present Glia, an AI architecture for networked systems design that uses large language models (LLMs) in a human-inspired multi-agent workflow. Each agent specializes in reasoning, experimentation, and analysis, collaborating through an evaluation framework that grounds abstract reasoning in empirical feedback. Unlike prior ML-for-systems methods that optimize black-box policies, Glia generates interpretable designs and exposes its reasoning. When applied to a distributed GPU cluster for LLM inference, it produces new algorithms for request routing, scheduling, and auto-scaling that perform at human-expert levels in significantly less time, while yielding novel insights into workload behavior. Our results suggest that combining reasoning LLMs with structured experimentation, an AI can produce creative and understandable designs for complex systems problems. |
| title | Glia: A Human-Inspired AI for Automated Systems Design and Optimization |
| topic | Artificial Intelligence Computation and Language Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2510.27176 |