通过能量最小化统一测试时聚合，提升大模型推理准确性。
Joint Consistency: A Unified Test-Time Aggregation Framework via Energy Minimization
- 将多个推理路径的评分与相互比较建模为能量系统
- 在数学和代码推理任务上显著优于现有方法
- 适合需要高可靠推理的场景，如自动评测与决策支持
本文研究测试时聚合，即生成多个推理路径并整合为最终答案的方法。现有方法多依赖孤立候选路径的评估信号或答案频率，忽略路径间的比较互动。我们提出联合一致性（JC），将其形式化为带有约束的伊辛型能量最小化问题，其中独立评估信号作为外场，成对比较作为相互作用。JC 提供了一个统一的测试时聚合框架，可涵盖投票与加权聚合等方法作为特例。交互矩阵的设计基于大模型作为评判者进行的比较，且在答案同质性假设下具有理论解释。此外，我们设计了高效的近似策略，使大规模测试时聚合中的交互建模成为可能。在数学与代码推理基准上的实验表明，JC 在不同任务、评判模型、推理路径预算及生成设置下均持续优于现有基线。
原文摘要 · Abstract (English)
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. JC provides a unified framework for test-time aggregation that subsumes existing voting and weighted aggregation methods as special cases. Our construction of the interaction matrix leverages LLM-as-a-judge comparisons, and admits a theoretical interpretation under answer-level homogeneity assumptions. Moreover, we develop an efficient approximation strategy that makes interaction modeling practical for large-scale test-time aggregation. Experiments on math and code reasoning benchmarks show that JC consistently outperforms existing baselines across tasks, judge models, trace budgets, and trace-generation settings.
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