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arXiv:2602.15325v1 Announce Type: new Abstract: Foundation models for agriculture are increasingly trained on massive spatiotemporal data (e.g., multi-spectral remote sensing, soil grids, and field-level management …
arXiv:2602.15384v1 Announce Type: new Abstract: Web agents based on large language models have demonstrated promising capability in automating web tasks. However, current web agents struggle …
arXiv:2602.15391v1 Announce Type: new Abstract: Large Language Models (LLMs) deployed in production environments face a fundamental safety-utility trade-off either a strict filtering mechanisms prevent harmful …
arXiv:2602.15403v1 Announce Type: new Abstract: Contrary to common belief, common belief is not KD4. If individual belief is KD45, common belief does indeed lose the …
arXiv:2602.15531v1 Announce Type: new Abstract: This work introduces EduEVAL-DB, a dataset based on teacher roles designed to support the evaluation and training of automatic pedagogical …
arXiv:2602.15532v1 Announce Type: new Abstract: The LLM community often reports benchmark results as if they are synonymous with general model capabilities. However, benchmarks can have …
arXiv:2602.15553v1 Announce Type: new Abstract: The Personal AI landscape is currently dominated by "Black Box" Retrieval-Augmented Generation. While standard vector databases offer statistical matching, they …
arXiv:2602.15580v1 Announce Type: new Abstract: When a multimodal Transformer answers a visual question, is the prediction driven by visual evidence, linguistic reasoning, or genuinely fused …
arXiv:2602.15635v1 Announce Type: new Abstract: Cumulative constraints are central in scheduling with constraint programming, yet propagation is typically performed per constraint, missing multi-resource interactions and …
arXiv:2602.15645v1 Announce Type: new Abstract: Foundation models, including vision language models, are increasingly used in automated driving to interpret scenes, recommend actions, and generate natural …
arXiv:2602.15669v1 Announce Type: new Abstract: Current methods for personality control in Large Language Models rely on static prompting or expensive fine-tuning, failing to capture the …
arXiv:2602.15725v1 Announce Type: new Abstract: Large language models achieve strong performance on many complex reasoning tasks, yet their accuracy degrades sharply on benchmarks that require …
