This paper describes our submissions to the TSAR 2025 Shared Task on Readability-Controlled Text Simplification. We present a comparative study of three architectures a rule-based baseline, a heuristic-driven expert system, and a zero-shot generative T5 pipeline with a semantic guardrail. Our analysis shows a trade-off between the controllability of rule-based systems and the fluency of generative models. In this zero-shot setting, simpler, confined systems achieved superior meaning preservation scores compared to the more powerful but less predictable generative model. We present a diagnostic failure analysis on system outputs, illustrating how different architectures result in distinct error patterns such as under-simplification, information loss via heuristics, and semantic drift.

This paper presents rankedCOMET, a lightweight per-language-pair calibration applied to the publicly available Unbabel/wmt22-comet-da model that yields a competitive Quality Estimation (QE) system for the WMT 2025 shared task. This approach transforms raw model outputs into per-language average ranks and min–max normalizes those ranks to [0,1], maintaining intra-language ordering while generating consistent numeric ranges across language pairs. Applied to 742,740 test segments and submitted to Codabench, this unsupervised post-processing enhanced the aggregated Pearson correlation on the preliminary snapshot and led to a 5th-place finish. We provide detailed pseudocode, ablations (including a negative ensemble attempt), and a reproducible analysis pipeline providing Pearson, Spearman, and Kendall correlations with bootstrap confidence intervals.