Separating Diagnosis from Control: Auditable Policy Adaptation in Agent-Based Simulations with LLM-Based Diagnostics
arXiv:2603.22904v1 Announce Type: new Abstract: Mitigating elderly loneliness requires policy interventions that achieve both adaptability and auditability. Existing methods struggle to reconcile these objectives: traditional agent-based models suffer from static rigidity, while direct large language model (LLM) controllers lack essential traceability. This work proposes a three-layer framework that separates diagnosis from control to achieve both properties simultaneously. LLMs operate strictly as diagnostic instruments that assess population state and generate structured risk evaluations, while deterministic formulas with explicit bounds translate these assessments into traceable parameter updates. This separation ensures that every policy decision can be attributed to inspectable rules while maintaining adaptive response to emergent needs. We validate the framework through systematic ablation across five experimental conditions in elderly care simulation. Results demonstrate that e
arXiv:2603.22904v1 Announce Type: new Abstract: Mitigating elderly loneliness requires policy interventions that achieve both adaptability and auditability. Existing methods struggle to reconcile these objectives: traditional agent-based models suffer from static rigidity, while direct large language model (LLM) controllers lack essential traceability. This work proposes a three-layer framework that separates diagnosis from control to achieve both properties simultaneously. LLMs operate strictly as diagnostic instruments that assess population state and generate structured risk evaluations, while deterministic formulas with explicit bounds translate these assessments into traceable parameter updates. This separation ensures that every policy decision can be attributed to inspectable rules while maintaining adaptive response to emergent needs. We validate the framework through systematic ablation across five experimental conditions in elderly care simulation. Results demonstrate that explicit control rules outperform end-to-end black-box LLM approaches by 11.7\% while preserving full auditability, confirming that transparency need not compromise adaptive performance.
Executive Summary
This article proposes a novel three-layer framework that separates diagnosis from control to achieve adaptability and auditability in agent-based simulations for mitigating elderly loneliness. Large language models (LLMs) are used as diagnostic instruments, while deterministic formulas translate their assessments into traceable parameter updates. The framework is validated through systematic ablation across five experimental conditions, demonstrating that explicit control rules outperform end-to-end black-box LLM approaches by 11.7% while preserving full auditability. This approach has significant implications for policy design, as transparency and adaptability are no longer mutually exclusive. The authors' innovative use of LLMs and deterministic formulas offers a promising solution for complex policy problems.
Key Points
- ▸ The proposed framework separates diagnosis from control to achieve adaptability and auditability.
- ▸ LLMs are used as diagnostic instruments, while deterministic formulas translate their assessments into traceable parameter updates.
- ▸ Explicit control rules outperform end-to-end black-box LLM approaches by 11.7% while preserving full auditability.
Merits
Strength in Addressing Complex Policy Problems
The framework's ability to balance adaptability and auditability addresses a significant challenge in policy design, making it a valuable contribution to the field.
Innovative Use of LLMs and Deterministic Formulas
The authors' use of LLMs and deterministic formulas offers a promising solution for complex policy problems, leveraging the strengths of both approaches.
Demerits
Limited Scope of Application
The framework's focus on elderly care simulations may limit its generalizability to other policy domains, requiring further adaptation and testing.
Potential Overreliance on LLMs
The reliance on LLMs as diagnostic instruments may introduce biases or errors, highlighting the need for robust testing and validation.
Expert Commentary
The article presents a significant contribution to the field of policy design, leveraging the strengths of large language models and deterministic formulas to achieve adaptability and auditability. The proposed framework offers a promising solution for complex policy problems, but its limited scope of application and potential overreliance on LLMs require further exploration and testing. As the field continues to evolve, the use of LLMs in policy analysis will become increasingly important, and the authors' innovative approach will likely influence future research and applications.
Recommendations
- ✓ Future research should focus on adapting the framework to other policy domains and exploring its generalizability.
- ✓ Robust testing and validation of the framework, including sensitivity analysis and error estimation, are essential to ensure its reliability and effectiveness.
Sources
Original: arXiv - cs.AI
