Simulate, Reason, Decide: Scientific Reasoning with LLMs for Simulation-Driven Decision Making
arXiv:2606.04505v1 Announce Type: new Abstract: Scientific simulators are increasingly being integrated into LLM-driven systems for high-stakes simulation-driven decision-making. However, existing frameworks primarily use LLMs to generate, calibrate, or execute simulators, treating them as black-box interfaces rather than as structured mechanistic systems that can be reasoned about. As a result, current approaches lack the ability to identify, represent, and reason about the assumptions and mechanisms underlying simulator behavior, limiting transparency, auditability, and decision justificatio
The increasing integration of LLMs with scientific simulators for critical decision-making necessitates a more transparent and auditable approach that goes beyond treating simulators as black boxes.
This research addresses fundamental limitations in current LLM-driven simulation systems, enhancing transparency and auditability, which are crucial for high-stakes decisions and responsible AI development.
The focus shifts from simply using LLMs to generate or execute simulations to enabling them to reason about the underlying mechanisms and assumptions of these simulators, leading to more robust and justifiable outcomes.
- · AI developers focused on transparency and explainability
- · Industries relying on high-stakes simulations (e.g., aerospace, pharmaceuticals)
- · Regulatory bodies seeking auditable AI systems
- · Developers of black-box AI simulation systems
- · Companies with low transparency standards in AI deployment
Improved reliability and trust in AI-driven simulation outcomes across various sectors.
Accelerated adoption of AI in previously risk-averse domains due to enhanced auditability and explainability.
Potential for new regulatory frameworks and industry standards specifically designed for transparent and explainable AI in simulation-driven decision making.
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Read at arXiv cs.AI