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arXiv:2603.00917v1 Announce Type: new Abstract: Small open-source language models are gaining attention for low-resource healthcare settings, but their reliability under different prompt phrasings remains poorly …
arXiv:2603.00923v1 Announce Type: new Abstract: Interlinear glossed text (IGT) creation remains a major bottleneck in linguistic documentation and fieldwork, particularly for low-resource morphologically rich languages. …
arXiv:2603.00924v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used for medical entity extraction, yet their confidence scores are often miscalibrated, limiting safe …
arXiv:2603.00925v1 Announce Type: new Abstract: Effective mathematics education requires identifying and responding to students' mistakes. For AI to support pedagogical applications, models must perform well …
arXiv:2603.00941v1 Announce Type: new Abstract: Evaluating ASR systems for Indian languages is challenging due to spelling variations, suffix splitting flexibility, and non-standard spellings in code-mixed …
arXiv:2603.01009v1 Announce Type: new Abstract: Over the past years, Automated Essay Scoring (AES) systems have gained increasing attention as scalable and consistent solutions for assessing …
arXiv:2603.01042v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable success in general-purpose reasoning. However, they still struggle to understand and reason about …
arXiv:2603.01059v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have enabled increasingly capable chatbots. However, most existing systems focus on single-user settings …
arXiv:2603.01070v1 Announce Type: new Abstract: Solving complex geometric problems inherently requires interleaved reasoning: a tight alternation between constructing diagrams and performing logical deductions. Although recent …
arXiv:2603.00010v1 Announce Type: new Abstract: Transit Network Design is a well-studied problem in the field of transportation, typically addressed by solving optimization models under fixed …
arXiv:2603.00037v1 Announce Type: new Abstract: Diffusion models have been used for probabilistic time series forecasting and show strong potential. However, fixed noise schedules often produce …
arXiv:2603.00039v1 Announce Type: new Abstract: LLM-as-a-judge ensembles are the standard paradigm for scalable evaluation, but their aggregation mechanisms suffer from a fundamental flaw: they implicitly …