AI RESEARCH

Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization

arXiv CS.AI

ArXi:2605.06219v1 Announce Type: new This paper studies test-time aggregation, an approach that generates multiple reasoning traces and aggregates them into a final answer. Most existing methods rely on evaluation signals collected from candidate traces in isolation or answer frequencies, while ignoring comparative interactions among candidates. We propose Joint Consistency (JC), formulated as a constrained Ising-type energy minimization problem, where independent evaluation signals act as external fields and pairwise comparisons act as interactions.