Autoregressive generative models play a key role in various language tasks, especially for modeling and evaluating long text sequences.While recent methods leverage stochastic representations to better capture sequence dynamics, encoding both temporal and structural dependencies and utilizing such information for evaluation remains challenging.In this work, we observe that fitting transformer-based model embeddings into a stochastic process yields ordered latent representations from originally unordered model outputs.Building on this insight and prior work, we theoretically introduce a novel likelihood-based evaluation metric BB-ScoreV2.Empirically, we demonstrate that the stochastic latent space induces a "clustered-totemporal ordered" mapping of language model representations in high-dimensional space, offering both intuitive and quantitative support for the effectiveness of BBScoreV2.Furthermore, this structure aligns with intrinsic properties of natural language and enhances performance on tasks such as temporal consistency evaluation (e.g., Shuffle tasks) and AIgenerated content detection.

The Resource Public Key Infrastructure (RPKI) is a system to add security to the Internet routing. In recent years, the publication of Route Origin Authorization (ROA) objects, which bind IP prefixes to their legitimate origin ASN, has been rapidly increasing. However, ROAs are effective only if the routers use them to verify and filter invalid BGP announcements, a process called Route Origin Validation (ROV).