This paper describes our submission to the BEA 2026 shared task 1 on vocabulary item difficulty prediction in multilingual settings. We investigated whether transformer-based representations learned directly from item content can support the prediction of vocabulary item difficulty across different L1 groups. Our approach adopted a multilingual BERT-based architecture, specifically the mmBERT, with representation augmentation at both the layer and token levels, followed by a multi-task cascade learning that incorporates part-of-speech information as an auxiliary structural signal. Results showed that multi-task mmBERT consistently outperforms the shared-task XLM-RoBERTa baseline across languages, while gains from more complex aggregation are not uniform. The findings showed that strong multilingual representations provide a competitive foundation for vocabulary item difficulty prediction, while the benefits of additional architectural complexity depend on the language and training setting.

Using the Facets Model for Severity and Centrality, we analyzed two human raters and 10 LLMs across four ASAP-SAS prompts. LLM centrality was task-dependent, and few-shot prompting reduced centrality inconsistently. Omitting scale-use differences altered severity estimates (r=.47), while MFRM fit diagnostics were harder to interpret in heterogeneous LLM rater pools.

Automated short-answer scoring should reflect substantive content rather than linguistic form. Using controlled rewrites of 743 ASAP-SAS responses, we compare six fine-tuned encoders and 11 LLMs. Encoder scores increased with linguistic complexity, especially for lower-scoring responses, whereas LLMs showed heterogeneous patterns, revealing model-specific construct-irrelevant scoring signals.

This study presents a causal approach for evalu- ating automated generation of reading passages for early readers. It consists of estimating the average treatment effect of a candidate model, then conditional average treatment effects with causal forests. We apply it to evaluate a fine- tuned model for a digital literacy platform