Generalization of verifier-guided adaptive test-time inference beyond mathematics

Determine whether the verifier-guided adaptive test-time inference framework for mathematical reasoning—comprising iterative trajectory generation with per-problem tool selection, compute strategy selection, and process reward model (PRM)-based step and trajectory scoring—generalizes to other non-mathematical domains; identify the domain-adaptive verification signals and broader evaluation protocols required to enable and validate such generalization.

Background

The paper proposes a verifier-guided adaptive test-time inference framework that treats reasoning as iterative trajectory generation and selection. For each problem, the agent plans, selects tools (e.g., Chain-of-Thought, self-reflection, numeric or logical verifiers), chooses a compute strategy (Best-of-N, beam search, or lookahead), and generates candidate trajectories. A process reward model (PRM) provides step-level correctness signals to guide pruning and expansion during generation and to select the final trajectory across iterations.

Empirical results demonstrate substantial gains in mathematical reasoning benchmarks (MATH-500, AIME24, AMO-Bench). However, the authors explicitly state that extending the approach to other domains is unresolved and likely requires domain-adaptive verification signals and broader evaluation, highlighting an open problem in generalization beyond mathematics.