Controlled Agentic Planning & Reasoning for Mechanism Synthesis
This work addresses the problem of automating mechanism design for engineers, though it is incremental as it builds on existing LLM and simulation methods.
The paper tackles automated planar mechanism synthesis by developing a dual-agent LLM-based reasoning framework that converts natural-language task descriptions into symbolic constraints and simulation code, achieving up to 90% improvement on individual tasks through iterative refinement with critic feedback.
This work presents a dual-agent \ac{llm}-based reasoning framework for automated planar mechanism synthesis that tightly couples linguistic specification with symbolic representation and simulation. From a natural-language task description, the system composes symbolic constraints and equations, generates and parametrises simulation code, and iteratively refines designs via critic-driven feedback, including symbolic regression and geometric distance metrics, closing an actionable linguistic/symbolic optimisation loop. To evaluate the approach, we introduce MSynth, a benchmark of analytically defined planar trajectories. Empirically, critic feedback and iterative refinement yield large improvements (up to 90\% on individual tasks) and statistically significant gains per the Wilcoxon signed-rank test. Symbolic-regression prompts provide deeper mechanistic insight primarily when paired with larger models or architectures with appropriate inductive biases (e.g., LRM).