Spread and Scale: What Determines Whether Test-Time Budget Allocation Pays

The paper investigates when reallocating a fixed test‑time budget toward harder instances improves solution quality for neural combinatorial optimization solvers. Through pre‑registered experiments on three solvers and two hard‑workload constructions for the traveling salesman problem, it finds that the key deciding factor is the variation in instance difficulty within a workload, not the average difficulty. A budget‑aware policy that first spends part of the budget to gauge instance difficulty recovers most of the potential improvement, though not all, when the cost of this information is included.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.