Simulate, Reason, Decide: Scientific Reasoning with LLMs for Simulation-Driven Decision Making
Quick Take
MechSim introduces a neuro-symbolic reasoning framework that enhances LLM-driven scientific simulators by enabling structured reasoning about their mechanisms and assumptions, improving decision-making reliability in high-stakes scenarios.
Key Points
- MechSim allows LLM agents to reason about simulator mechanisms and assumptions.
- The framework improves transparency and auditability in simulation-driven decisions.
- It enhances explanation quality and reliability in high-stakes domains.
- Simulators are represented through a structured schema capturing key dependencies.
- Evaluation shows significant improvements in decision-making outcomes.
Article Content
From source RSS / original summaryarXiv: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 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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