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arXiv · 2609.17004

Symmetry-Aware Likelihood-Orbit Aggregation for Selective Left-Right Claim Verification

Zhouzhi Xiong·Chuxi Zhang·Weizhen He·Yi Chen·Qi Li·Donglian Qi
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Abstract

Frozen vision-language models (VLMs) remain unreliable on fine-grained left-right claims, and raw claim likelihoods need not reliably rank verification errors. After a horizontal-reflection intervention is fixed, how should its induced likelihood measurements be combined into a selective verification signal? We introduce Relation-Orbit, a closed-form contrast with no learned fusion parameters that assigns eight normalized likelihoods to query-supporting and counterfactual roles determined by reflection, inverse relation, and entity exchange. A claim is asserted only when the signed contrast exceeds a threshold selected on held-out data using pointwise Clopper-Pearson upper confidence bounds. On VSR and GQA across four frozen VLMs, Relation-Orbit yields higher mean test coverage at a 10% selective-risk calibration target than an all-eight Orbit-Max baseline in all eight dataset-backbone settings; gains over a nearly abstain-all one-sided intervention score are reported separately. A separate LLaVA-1.5/COCO evaluation, reduced-orbit controls, and a two-sided partition diagnostic further characterize the structural advantage.

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Zhouzhi Xiong, Chuxi Zhang, Weizhen He, Yi Chen, Qi Li, Donglian Qi. 2026-09-15. Symmetry-Aware Likelihood-Orbit Aggregation for Selective Left-Right Claim Verification. https://arxiv.org/abs/2609.17004

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