🤖 AI Summary
This work addresses the challenge of semantic drift in multi-agent scientific computing, which often leads to inconsistencies between policy selection and execution outcomes, thereby undermining causal traceability and adaptive learning. To mitigate this issue, the paper proposes a multi-agent system that integrates contextual bandits, a structured semantic communication protocol, and a semantic checkpointing mechanism, embedding the principle of empowerment into the decision-making process to preserve semantic consistency along action–outcome chains. The framework leverages large language model–driven specialized agents, code-generation verification, and a self-repairing execution loop. Evaluated on sensitivity analysis and uncertainty quantification tasks, the approach significantly enhances policy convergence, robustness, and generalization to novel scenarios, effectively suppressing semantic drift.
📝 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 corrupting downstream evaluation and adaptation. In this work, motivated by the ATHENA framework (Toscano et al., 2025; Toscano et al., 2026) and the concept of empowerment (Yiu et al., 2025), we introduce a multi-agent framework that combines contextual bandits with structured inter-agent communication and, most importantly, semantic checkpoints that preserve action-outcome fidelity throughout the pipeline. The system integrates specialized large language model (LLM) agents, grounded code generation, and self-healing execution loops within an adaptive decision-making architecture. Interpreting the framework through the lens of empowerment, we show that reliable autonomous learning requires not only identifying high-quality actions, but also preserving the integrity of their propagation across agents. Using sensitivity analysis and uncertainty quantification workflows as representative case studies, we demonstrate that unchecked semantic drift degrades policy learning, whereas the proposed framework improves convergence, robustness, and adaptation to novel problem contexts. These results suggest a broader design principle for scientific multi-agent systems: adaptive decision-making must be coupled with explicit mechanisms that guarantee semantic consistency and reliable information flow across the computational pipeline.