Show HN: Symbolic Circuit Distillation: prove program to LLM circuit equivalence (github.com)

A recent development in mechanistic interpretability, titled "Symbolic Circuit Distillation," presents an innovative method for automatically extracting human-readable algorithms from neuron-level circuit graphs in transformer models. This approach addresses the labor-intensive challenge of converting complex circuit patterns into clear, executable algorithms. By treating the pruned circuits as black-box teachers, the method employs a small ReLU surrogate network that matches the circuit's behavior on a constrained input domain. Through a template-guided domain-specific language (DSL), the system synthesizes candidate programs and uses SMT-based equivalence checking to assure that these programs function identically to the original circuit within specified parameters. This advancement is significant for the AI/ML community as it automates a critical bottleneck in the interpretability of neural networks, facilitating the translation of dense, intricate circuits into verified algorithms without extensive manual intervention. The empirical validation of this technique on tasks such as bracket counting and quote-type tracking shows not only that it can accurately recover known algorithmic motifs but also highlights latent failure modes often overlooked by conventional circuit analysis methods. By enhancing the efficiency and accuracy of mechanistic interpretability efforts, Symbolic Circuit Distillation has the potential to deepen our understanding of transformer behaviors and improve the transparency of AI systems.