arXiv:2604.00829v3 Announce Type: replace-cross
Abstract: Adapting pretrained language models (LMs) into vision-language models (VLMs) can degrade their native linguistic capability due to representation shift and cross-modal interference introduced d…
arXiv:2510.10930v2 Announce Type: replace
Abstract: Reasoning is not just about solving problems — it is also about evaluating which problems are worth solving at all. Evaluations of artificial intelligence (AI) systems primarily focused on problem s…
arXiv:2510.17881v3 Announce Type: replace
Abstract: Large language models (LLMs) are typically aligned with population-level preferences, despite substantial variation across individual users. We introduce POPI, a user-level personalization framework …
arXiv:2604.24690v1 Announce Type: new
Abstract: 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 bench…
arXiv:2510.18774v4 Announce Type: replace
Abstract: AI is rapidly transforming journalism, but the extent of its use in published newspaper articles remains unclear. We address this gap by auditing a large-scale dataset of 186K articles from online ed…
arXiv:2604.24700v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly deployed, yet their outputs can be highly sensitive to routine, non-adversarial variation in how users phrase queries, a gap not well addressed by existing r…
arXiv:2512.16378v4 Announce Type: replace
Abstract: As Large Language Models (LLMs) expand beyond text, integrating speech as a native modality has given rise to SpeechLLMs, which directly process spoken language and enable speech-to-text translation …
arXiv:2604.24715v1 Announce Type: new
Abstract: Hybrid sequence models that combine efficient Transformer components with linear sequence modeling blocks are a promising alternative to pure Transformers, but most are still pretrained from scratch and …
arXiv:2503.05587v3 Announce Type: replace
Abstract: Robustness has become a critical attribute for the deployment of RAG systems in real-world applications. Existing research focuses on robustness to explicit noise (e.g., document semantics) but overl…
arXiv:2604.23701v1 Announce Type: new
Abstract: Crop disease diagnosis from field photographs faces two recurring problems: models that score well on benchmarks frequently hallucinate species names, and when predictions are correct, the reasoning behi…