Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently. However, many recent approaches rely on large cloud-based models, which are difficult to deploy in clinical environments due to privacy constraints and computational requirements. In this work, we investigate how far grounded EHR question answering can be pushed when restricted to a single notebook. We participate in all four subtasks of the ArchEHR-QA 2026 shared task and evaluate several approaches designed to run on commodity hardware. All experiments are conducted locally without external APIs or cloud infrastructure. Our results show that such systems can achieve competitive performance on the shared task leaderboards. In particular, our submissions perform above average in two subtasks, and we observe that smaller models can approach the performance of much larger systems when properly configured. These findings suggest that privacy-preserving EHR QA systems running fully locally are feasible with current models and commodity hardware. The source code is available at https://github.com/ibrahimey/ArchEHR-QA-2026.

The quality of annotated data is crucial for Machine Learning models, particularly in word sense annotation in context (Word-in-Context, WiC). WiC datasets often show significant annotator disagreement, and information is lost when creating gold labels through majority or median aggregation. Recent work has addressed this by incorporating disagreement data through new label aggregation methods. Modeling disagreement is important since real-world scenarios often lack clean data and require predictions on inherently difficult samples. Disagreement prediction can help detect complex cases or to reflect inherent data ambiguity. We aim to model different aspects of ordinal Word-in-Context annotations necessary to build a more human-like model: (i) the aggregated label, which has traditionally been the modeling aim, (ii) the disagreement between annotators, and (iii) the aggregated noise label which annotators can choose to exclude data points from annotation. We find that disagreement and noise are impacted by various properties of data like ambiguity, which in turn points to data uncertainty.

We asked task participants to solve two subtasks given a pair of word usages: Ordinal Graded Word-in-Context Classification (OGWiC) and Disagreement in Word-in-Context Ranking (DisWiC). The tasks take a different view on modeling of word meaning by (i) treating WiC as an ordinal classification task, and (ii) making disagreement the explicit detection aim (instead of removing it). OGWiC is solved with relatively high performance while DisWiC proves to be a challenging task. In both tasks, the dominating model architecture uses independently optimized binary Word-in-Context models.