Academic

Decision-Centric Design for LLM Systems

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 actio

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Wei Sun · · 1 min read · 4 views
#cs.AI #cs.LG

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.

Executive Summary

This article proposes a decision-centric framework for Large Language Model (LLM) systems to improve their reliability, controllability, and diagnosability. The framework separates decision-relevant signals from the policy that maps them to actions, enabling explicit decision-making and modular improvement of each component. Controlled experiments demonstrate that the framework reduces futile actions, improves task success, and reveals interpretable failure modes. The proposed framework offers a general architectural principle for building more reliable LLM systems, with implications for both practical applications and policy considerations.

Key Points

  • ▸ The decision-centric framework separates decision-relevant signals from the policy that maps them to actions.
  • ▸ Controlled experiments demonstrate that the framework improves task success and reduces futile actions.
  • ▸ The framework reveals interpretable failure modes, enabling modular improvement of each component.

Merits

Strength

The proposed framework addresses a critical limitation of current LLM architectures by separating decision-making from output generation, enabling explicit control and diagnosability.

Strength

The framework's modular design facilitates improvement of individual components, promoting more targeted and efficient development of LLM systems.

Demerits

Limitation

The framework's effectiveness may be contingent on the quality and accuracy of the decision-relevant signals, which could be a challenging aspect to address in practical applications.

Expert Commentary

The proposed decision-centric framework represents a significant step forward in the development of more reliable and controllable LLM systems. By separating decision-relevant signals from the policy that maps them to actions, the framework enables explicit control and modular improvement of each component. While the framework's effectiveness may be contingent on the quality and accuracy of the decision-relevant signals, the controlled experiments demonstrate its potential to improve task success and reduce futile actions. The framework's implications for both practical applications and policy considerations are substantial, and it is likely to have a lasting impact on the development and deployment of LLM systems.

Recommendations

  • ✓ Further research is needed to fully explore the potential of the proposed framework, including investigations into its effectiveness in more complex and dynamic scenarios.
  • ✓ The development of more robust and accurate decision-relevant signals is essential to realizing the full benefits of the framework, and may require significant advances in areas such as natural language processing and machine learning.

Sources

Original: arXiv - cs.AI

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