CIRCE predicts LLM task difficulty from constraint structure
CIRCE is a method that uses a cheap LLM to build a Constraint Intermediate Representation which deterministically compiles into a constraint graph, and then applies a GNN encoder to predict whether an LLM will solve a given problem. Evaluated across seven reasoning benchmarks (including SAT, logic, and math) and four LLMs, a randomly initialized GNN improved negative log-likelihood (0.465 → 0.423) and AUROC (0.691 → 0.727) versus the strongest baselines reported.