Patient-facing medical LLMs are typically evaluated for clinical knowledge and safety, but clinically correct answers can still fail patients by missing fear, mishandling uncertainty, or offering emotionally unsafe reassurance. We introduce MedEQ, a benchmark for clinical emotional intelligence in medical communication. MedEQ includes 413 clinically grounded consultation scenarios across 51 specialties and 7,336 expert-refined rubric items over 21 core dimensions. These expert-validated rubrics operationalize emotional cue recognition, empathic adequacy, ethical emotional regulation, and affective safety, enabling diagnostic evaluation rather than a single holistic empathy score. Using an LLM-as-judge rubric pipeline, we find that strong contemporary models often handle surface compassion, safety, and response structure, yet remain weak on deeper emotional inference, personalization, mixed emotions, cultural context, and real-time responses to illness-related distress. We further conduct a proof-of-concept rubric-guided reinforcement learning study, showing that MedEQ-derived rubric signals can transfer to an independent emotional intelligence benchmark: rubric-guided training of Qwen3-32B improves EQ-Bench performance from 42.42 to 81.49 while preserving or slightly improving standard medical benchmark scores. MedEQ therefore makes clinical emotional intelligence measurable and decomposable, and provides evidence that expert-refined rubrics can support the evaluation and improvement of patient-facing medical AI.

While Large Language Models (LLMs) have increasingly assisted in historical tasks such as text processing, their capacity for professional-level historical reasoning remains underexplored. Existing benchmarks primarily assess basic knowledge breadth or lexical understanding, failing to capture the higher-order skills—such as evidentiary reasoning—that are central to historical research. To fill this gap, we introduce ProHist-Bench, a novel benchmark anchored in the Chinese Imperial Examination (Keju) system—a comprehensive microcosm of East Asian political, social, and intellectual history spanning over 1,300 years. Developed through deep interdisciplinary collaboration, ProHist-Bench features 400 challenging, expert-curated questions across eight dynasties, accompanied by 10,891 fine-grained evaluation rubrics. Through a rigorous evaluation of 18 LLMs, we reveal a significant proficiency gap: even state-of-the-art LLMs struggle with complex historical research questions. We hope ProHist-Bench will facilitate the development of domain-specific reasoning LLMs, advance computational historical research, and further uncover the untapped potential of LLMs. We release ProHist-Bench at https://github.com/inclusionAI/ABench/tree/main/ProHist-Bench.

Recent works have proposed methods of generating synthetic data automatically for unsupervised Grammatical Error Correction (GEC). Although a large amount of synthetic data is generated at a low cost, it is unrealistic and of poor quality. The copying phenomenon of synthetic data prevents GEC models from learning the semantic knowledge of contextual language. In this paper, we design an instruction format and use the masking strategy in both an erroneous sentence and the corresponding instruction consistently to alleviate the impact of the copy phenomenon. We also propose a novel approach, InstructGEC, which integrates the knowledge of grammatical detection into GEC models with instruction tuning to address the low-quality issue. Experiments are conducted on English and Chinese GEC datasets and results demonstrate that our method outperforms state-of-the-art unsupervised GEC methods.