EPOCH: An Agentic Protocol for Multi-Round System Optimization
arXiv:2603.09049v1 Announce Type: new 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 evaluati
arXiv:2603.09049v1 Announce Type: new 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.
Executive Summary
The article proposes EPOCH, an agentic protocol for multi-round system optimization in heterogeneous environments. EPOCH organizes optimization into two phases: baseline construction and iterative self-improvement, structure each round through role-constrained stages, and standardizes execution through canonical command interfaces and round-level tracking. Empirical studies demonstrate EPOCH's practicality in production-oriented autonomous improvement workflows. This contribution addresses the need for a unified protocol to manage tracked multi-round self-improvement, improving stability, reproducibility, traceability, and integrity of evaluation. EPOCH's design enables coordinated optimization across various components, facilitating autonomous agents to improve prompts, code, and machine learning systems iteratively. The empirical studies' results illustrate the protocol's effectiveness in various tasks, indicating its potential to enhance autonomous improvement workflows.
Key Points
- ▸ EPOCH is an engineering protocol for multi-round system optimization in heterogeneous environments.
- ▸ EPOCH organizes optimization into two phases: baseline construction and iterative self-improvement.
- ▸ The protocol structures each round through role-constrained stages, standardizing execution and tracking.
Merits
Unified Protocol Design
EPOCH addresses the need for a unified protocol to manage tracked multi-round self-improvement.
Improved Optimization
EPOCH enables coordinated optimization across prompts, model configurations, code, and rule-based components.
Enhanced Evaluation
EPOCH preserves stability, reproducibility, traceability, and integrity of evaluation.
Demerits
Scalability Limitations
The protocol may face scalability challenges in handling increasingly complex optimization tasks.
Dependence on Task-Specific Design
EPOCH's effectiveness may depend on the specific task design and components involved.
Expert Commentary
EPOCH represents a significant contribution to the field of autonomous optimization, addressing the need for a unified protocol to manage tracked multi-round self-improvement. The protocol's design enables coordinated optimization across various components, facilitating autonomous agents to improve prompts, code, and machine learning systems iteratively. While EPOCH shows promise, its scalability and task-specific design limitations should be carefully considered. The empirical studies' results illustrate the protocol's effectiveness in various tasks, but further research is needed to fully understand its implications and potential applications.
Recommendations
- ✓ Future research should focus on addressing EPOCH's scalability limitations and exploring its application in more complex optimization tasks.
- ✓ Developers and practitioners should carefully consider EPOCH's task-specific design and adapt the protocol to their specific needs and context.