arXiv：cs.CL· Xinchen Xiao·· 3 小时前AI 评分36
Route-Verify-Vote：面向混合域推理的过程条件化自一致性方法
Route-Verify-Vote: Procedure-Conditioned Self-Consistency for Mixed-Domain Reasoning
AI 导读
Route-Verify-Vote（RVV）是一个无需更新模型参数的过程条件化自一致性框架，通过域标签选择推理程序、逐项验证约束并投票聚合完整答案集，在 SCoRE 2026 官方测试集上以每题 16 个采样答案集投票达到 74.6% 的精确集合准确率。自适应 RVV 提升至 77.3%，结合多模型域路由后达到 79.4%，最终系统在参赛系统中排名第二。
正文
Abstract:Compositional generalization remains challenging when language models must combine familiar reasoning operations in unfamiliar ways. The Scenario-Based Commonsense Reasoning Evaluation (SCoRE) 2026 tests this ability on three mixed domains absent from training and requires models to identify the complete set of correct options for each question.
We introduce Route-Verify-Vote (RVV), a framework for procedure-conditioned self-consistency that uses language models without parameter updates. Route uses the provided domain label to select a reasoning procedure that guides the model in representing and applying the relevant constraints. Verify prompts the model to assess each option against those constraints. Vote aggregates complete answer sets and allocates additional samples to questions with a small vote-count margin between the two most frequent sets. Samples for each question follow the same domain-specific procedure.
On the official test set, voting over 16 sampled answer sets per question achieves an exact-set accuracy of 74.6%. Adaptive RVV reaches 77.3%, and combining models on selected domain routes raises accuracy to 79.4%. The final system ranked second among participating systems. These results support domain-specific reasoning procedures and answer-set disagreement as useful tools for allocating inference-time computation in mixed-domain reasoning.
| Comments: | 12 pages, 8 figures, and 8 tables. Accepted for oral presentation at CCL26-Eval |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.08814 [cs.AI] |
| (or arXiv:2610.08814v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08814 arXiv-issued DOI via DataCite |
Submission history
From: Xinchen Xiao [view email]
[v1]
Wed, 23 Sep 2026 16:14:38 UTC (353 KB)
来源：arXiv：cs.CL · arxiv.org