Evaluating LLM-Generated Solver Configurations under Controlled Search Budgets

Large language model (LLM) workflows can generate solver configurations that improve optimization performance. A strong configuration establishes artifact quality, while attribution to the proposal mechanism requires comparison with alternative search under a common evaluation procedure and budget. We introduce PACER (Proposer Ablation for Configuration Evaluation under Matched Resources), a protocol that fixes scoring, validation selection, held-out testing, and solver-call accounting while varying the configuration-search method and validation budget. On the easy generalized independent set problem (GISP-easy), a remeasured historical LLM-generated IBM ILOG CPLEX configuration improves the held-out primal-dual integral (PDI) by $34.3$. An independent eight-trial snapshot yields 2 of 15 conventional-search endpoints above this reference. Along the nested searches, the count increases from 5 of 15 after 25 trials to 11 of 15 after 60 trials. Replays of six configurations retained from the historical workflow across three problem families improve both PDI and wall-clock time in every point estimate. An ablation of the easy combinatorial-auction artifact shows that fixed CPLEX settings account for nearly all of its measured PDI gain. These results show how search opportunity changes the interpretation of an LLM-generated solver configuration.