Computer Science > Artificial Intelligence
arXiv:2603.09049 (cs)
[Submitted on 10 Mar 2026]
Title:EPOCH: An Agentic Protocol for Multi-Round System Optimization
View a PDF of the paper titled EPOCH: An Agentic Protocol for Multi-Round System Optimization, by Zhanlin Liu and 2 other authors
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Abstract:Autonomous agents are increasingly used to improve prompts, code, and machine learning systems through iterative execution and feedback. Yet existing approaches are usually designed as task-specific optimization loops rather than as a unified protocol for establishing baselines and managing tracked multi-round self-improvement. We introduce EPOCH, an engineering protocol for multi-round system optimization in heterogeneous environments. EPOCH organizes optimization into two phases: baseline construction and iterative self-improvement. It further structures each round through role-constrained stages that separate planning, implementation, and evaluation, and standardizes execution through canonical command interfaces and round-level tracking. This design enables coordinated optimization across prompts, model configurations, code, and rule-based components while preserving stability, reproducibility, traceability, and integrity of evaluation. Empirical studies in various tasks illustrate the practicality of EPOCH for production-oriented autonomous improvement workflows.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.09049 [cs.AI] |
| (or arXiv:2603.09049v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09049
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View a PDF of the paper titled EPOCH: An Agentic Protocol for Multi-Round System Optimization, by Zhanlin Liu and 2 other authors
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