Decision-Centric Design for LLM Systems
Summary: arXiv:2604.00414v1 Announce Type: new
Abstract: LLM systems must make control decisions in addition to generating outputs: whether to answer, clarify, retrieve, call tools, repair, or escalate. In many current architectures, these decisions remain implicit within generation, entangling assessment and action in a single model call and making failures hard to inspect, constrain, or repair. We propose a decision-centric framework that separates decision-relevant signals from the policy that maps them to actions, turning control into an explicit and inspectable layer of the system. This separation supports attribution of failures to signal estimation, decision policy, or execution, and enables modular improvement of each component. It unifies familiar single-step settings such as routing and adaptive inference, and extends naturally to sequential settings in which actions alter the information available before acting. Across three controlled experiments, the framework reduces futile actions, improves task success, and reveals interpretable failure modes. More broadly, it offers a general architectural principle for building more reliable, controllable, and diagnosable LLM systems.
Introduction to Decision-Centric Framework
Large Language Models (LLMs) have gained significant attention for their ability to generate human-like text. However, the integration of decision-making processes within these systems remains a challenge. The traditional approach often leads to entangled assessments and actions, complicating the debugging and improvement processes.
Key Features of the Proposed Framework
The decision-centric framework introduces several innovative features aimed at enhancing the functionality and reliability of LLM systems. These features include:
- Separation of Decision Signals: By distinguishing decision-relevant signals from the action-mapping policy, the framework provides clearer insights into the decision-making process.
- Modular Improvement: Each component can be improved independently, allowing for targeted optimizations without affecting the entire system.
- Inspectability: The explicit separation of control decisions makes it easier to inspect and diagnose failures, leading to more reliable outcomes.
- Unified Approach: The framework unifies various operational settings, including routing and adaptive inference, thereby streamlining the decision-making process.
- Sequential Decision-Making: The model naturally accommodates sequential actions, enabling it to adapt based on previously gathered information.
Results from Controlled Experiments
In a series of controlled experiments, the decision-centric framework demonstrated notable improvements across various metrics:
- Reduction of Futile Actions: By refining the decision-making process, the framework significantly decreased the number of unnecessary actions taken by the LLM.
- Improved Task Success Rates: The clear delineation of decision pathways led to higher success rates in task completion.
- Interpretable Failure Modes: The framework revealed previously hidden failure modes, allowing developers to address specific issues more effectively.
Conclusion
The decision-centric design framework represents a significant advancement in the development of LLM systems. By focusing on the explicit separation of decision-making components, it enhances the reliability, control, and diagnostic capabilities of these models. As the demand for robust AI systems continues to grow, adopting such frameworks will be crucial in addressing the complexities associated with LLMs.