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BoundInk introduces a writer-conditioned framework for online handwriting generation that explicitly models inter-character boundaries to improve cursive continuity and spatial alignment, outperforming existing methods with significant reductions in error and high human preference.
This paper introduces boundary-aware self-distillation to improve LLM safety refusal by reducing false refusals on benign prompts while maintaining genuine refusals through controlled data composition.
This paper presents UCSC NLP's systems for SemEval-2026 Task 10 (PsyCoMark), addressing conspiracy marker extraction using boundary-aware span extraction with RoBERTa, and document-level conspiracy classification with label smoothing. The systems ranked 7th in subtask 1 and 12th in subtask 2.