Learning to Choose: An Empowerment-Guided Multi-Agent System with semantic communication for Adaptive Method Selection

Researchers introduce a multi-agent framework that combines contextual bandits with semantic checkpoints to prevent 'semantic drift' in automated scientific computing workflows. The system ensures that computational strategies selected by AI agents are faithfully executed and remain causally attributable throughout multi-agent pipelines, improving convergence and robustness in adaptive decision-making.

This research addresses a fundamental challenge in autonomous scientific computing: maintaining semantic integrity across multi-agent systems. When multiple specialized LLM agents coordinate to solve computational problems, small inconsistencies between intended actions and executed procedures can compound into significant errors that corrupt downstream evaluation and adaptation. The work directly tackles this vulnerability through explicit semantic checkpoints that preserve action-outcome fidelity.

The research builds on established frameworks like ATHENA and the concept of empowerment, extending them with practical mechanisms for inter-agent communication and self-healing execution loops. The combination of contextual bandits—a proven approach for adaptive decision-making—with structured communication protocols creates a system that learns which computational strategies work best while verifying that those strategies are actually implemented as intended.

The implications extend beyond academic research into automated scientific discovery and industrial applications requiring reliable autonomous systems. As organizations increasingly deploy AI agents for complex computational workflows, the ability to prevent semantic drift becomes operationally critical. Incorrect implementation of selected strategies can lead to wasted computational resources, invalid conclusions, and cascading failures in dependent systems.

The framework demonstrates measurable improvements in policy convergence, robustness, and adaptation to novel problems compared to systems lacking semantic consistency mechanisms. This suggests a design principle applicable across autonomous systems that coordinate multiple agents. Organizations developing multi-agent pipelines for scientific computing, data analysis, or research automation should consider similar safeguards. The research establishes that reliable autonomous learning requires not only choosing good actions but verifying their faithful execution throughout the entire computational pipeline.

• →Multi-agent systems require explicit semantic checkpoints to prevent drift between intended and executed computational strategies.
• →Combining contextual bandits with structured inter-agent communication improves adaptive decision-making in scientific workflows.
• →Unchecked semantic drift degrades policy learning and robustness, but the proposed framework demonstrates measurable convergence improvements.
• →The framework integrates LLM agents, grounded code generation, and self-healing execution loops for reliable autonomous scientific computing.
• →Preserving causal attributability between decisions and outcomes is essential for downstream evaluation and adaptation in multi-agent pipelines.