Simulate, Reason, Decide: Scientific Reasoning with LLMs for Simulation-Driven Decision Making

• URL: http://arxiv.org/abs/2606.04505v1
• Date: Wed, 03 Jun 2026 06:36:51 GMT
• Title: Simulate, Reason, Decide: Scientific Reasoning with LLMs for Simulation-Driven Decision Making
• Abstract summary: MechSim is a mechanism-grounded neuro-symbolic reasoning framework for executable scientific simulators.<n>Our framework represents simulators through a shared structured schema capturing assumptions, variables, mechanism dependencies, and execution traces.<n>We evaluate our approach across multiple high-stakes domains and show that it improves mechanism-level explanation quality, simulator analysis, and downstream decision-making reliability.
• Score: 50.05403024674141
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 justification. We introduce MechSim, a mechanism-grounded neuro-symbolic reasoning framework for executable scientific simulators. Unlike prior neuro-symbolic approaches that primarily reason over static symbolic structures, MechSim enables LLM agents to reason about the mechanisms, assumptions, and execution behavior of scientific simulators. Our framework represents simulators through a shared structured schema capturing assumptions, variables, mechanism dependencies, and execution traces. On top of this representation, LLM agents operate as constrained reasoning engines that generate structured, evidence-grounded explanations linking simulator outcomes to their underlying mechanisms. We evaluate our approach across multiple high-stakes domains and show that it improves mechanism-level explanation quality, simulator analysis, and downstream decision-making reliability.

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