Empowerment-Guided Multi-Agent System Prevents Semantic Drift

Summary

This paper tackles a fundamental problem in multi-agent AI systems: semantic drift. When multiple LLM agents collaborate on a task—especially in scientific computing—small inconsistencies between what an agent intends to do and what it actually does can compound across pipeline stages. The result is that the final executed procedure no longer reflects the originally selected strategy, corrupting downstream evaluation and adaptation.

The authors propose a framework that combines contextual bandits (for adaptive method selection) with semantic checkpoints (to verify action-outcome fidelity at each agent handoff). They ground this in empowerment theory, which provides a formal measure of how much control an agent has over future outcomes. Using sensitivity analysis and uncertainty quantification as test cases, they show that their framework significantly improves convergence, robustness, and adaptation to novel problem contexts compared to baselines without semantic checkpoints.

Key Contributions

• Formalization of semantic drift in multi-agent scientific computing pipelines, showing how it degrades policy learning
• Empowerment-guided framework that combines contextual bandits with semantic checkpoints to preserve action-outcome fidelity
• Practical architecture with specialized LLM agents, grounded code generation, and self-healing execution loops
• Empirical validation on sensitivity analysis and uncertainty quantification workflows, demonstrating improved convergence and robustness
• Design principle: adaptive decision-making must be coupled with explicit mechanisms guaranteeing semantic consistency across the pipeline

Implications

For Researchers

This paper provides a rigorous framework for studying reliability in multi-agent systems. The empowerment lens offers a formal way to measure and optimize for action-outcome fidelity, which could be applied beyond scientific computing to any domain where causal attribution matters. Researchers should investigate how semantic checkpoints interact with different agent architectures and communication protocols.

For Developers

The key takeaway is practical: insert semantic checkpoints between agent handoffs. Before passing control from one agent to the next, verify that the action taken matches the intended strategy. This can be implemented as a lightweight verification step using a separate LLM or rule-based system. The paper also demonstrates the value of self-healing loops that can retry or correct actions when drift is detected.

For Users

For end users of multi-agent systems—especially in scientific computing, engineering, or any domain requiring reproducibility—this work means more reliable autonomous workflows. Users can trust that the system’s decisions are causally attributable to the original strategy selection, enabling better debugging, auditing, and adaptation to new problems.

References

• https://arxiv.org/abs/2605.30042v1