Learning to Choose: An Empowerment-Guided Multi-Agent System with semantic communication for Adaptive Method Selection
arXiv:2605.30042v1 Announce Type: new Abstract: Automating scientific computing workflows requires more than generating executable code: autonomous systems must also select appropriate computational strategies, implement them faithfully, and ensure that the resulting outcomes remain causally attributable to the decisions that produced them. In multi-agent pipelines, this process is particularly fragile, as small inconsistencies between agent intentions and actions can lead to semantic drift, where the eventually executed procedure no longer reflects the originally selected strategy, thereby co
The rapid development and deployment of multi-agent AI systems are exposing critical challenges in managing their behaviors and ensuring reliable outcomes, making this research timely.
This work directly addresses the fragility of multi-agent AI pipelines, which if unsolved, would hinder the broad adoption and reliability of autonomous systems in complex applications.
The focus on empowerment-guided adaptive method selection and semantic communication within multi-agent systems could lead to more robust, trustworthy, and scalable AI agents.
- · AI software developers
- · Automation companies
- · Industries adopting autonomous agents
- · Organizations with brittle AI deployments
- · Systems with high semantic drift
- · Workflow automation using non-agentic AI
Improved reliability and efficiency for autonomous systems in scientific computing and other complex domains.
Accelerated integration of AI agents into critical infrastructure and enterprise workflows due to enhanced trustworthiness.
New regulatory frameworks and certification processes for agentic AI systems focusing on interpretability and causality.
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Read at arXiv cs.AI