验证学习理论：AI 辅助判断中核查与保持的过程模型
Verification Learning Theory: A Process Model of Checking and Retention in AI-Assisted Judgment
AI 导读
arXiv 论文提出验证学习理论（VLT），对“评估声明—获得正确独立反馈”的形式化建模，将感知的 AI 助手可靠性与证据核查关联，并将核查历史与规则使用的保持关联。其严格形式预测：即使未观察到助手出错，感知到其可出错也能改善延迟的无辅助判断，而当核查本已被要求执行时该优势消失。论文通过仿真考察参数依赖，并与加工深度等理论比较，提出可检验的实验设计。
正文 · 原文
Abstract:Accurate AI assistance can improve immediate judgments while reducing opportunities to practise the operations needed for independent performance. Verification Learning Theory (VLT) formalises this possibility for claim-evaluation practice followed by correct, independent, rule-specific feedback. The model links perceived assistant reliability to evidence checking and links the history of those checks to retained rule use. Its strict form assumes that task demands and perceived reliability influence learning only through checking, with equal learning per genuine check. It therefore predicts that perceived fallibility can improve delayed unaided judgment even without observed assistant errors, and that this advantage disappears when checking is already required and performed. Rule-specific gains and forecasts from checking histories provide further tests. Simulations examine the consequences and parameter dependence of these commitments; they do not constitute empirical validation. Comparisons with depth-of-processing and feedback-encoding accounts identify where predictions diverge and where the accounts remain observationally equivalent. The framework specifies experiments that can test the proposed mechanism and separates retention of practised rules from the broader problem of recognising which rule applies to a new claim.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.37875 [cs.AI] |
| (or arXiv:2610.37875v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37875 arXiv-issued DOI via DataCite |
Submission history
From: Christoph Trattner [view email]
[v1]
Tue, 27 Oct 2026 19:18:18 UTC (2,057 KB)
来源：arXiv cs.AI · arxiv.org