arXiv:2511.02230v4 Announce Type: replace-cross
Abstract: KV cache management is essential for efficient LLM inference. To maximize utilization, existing inference engines evict finished requests’ KV cache if new requests are waiting. This policy brea…
arXiv:2512.16455v3 Announce Type: replace-cross
Abstract: The rapid growth of Artificial Intelligence and Machine Learning in scientific research has highlighted a gap between industry-standard MLOps tools and platforms, and the unique requirements of…
arXiv:2605.04085v1 Announce Type: cross
Abstract: Objectives: Large language models (LLMs) are increasingly used for clinical text summarization, yet structured methods to assess associated patient safety risks remain limited. Failure Mode, Effects, a…
arXiv:2605.02740v2 Announce Type: replace-cross
Abstract: Evidence derived from large-scale real-world data (RWD) is increasingly informing regulatory evaluation and healthcare decision-making. Administrative claims provide population-scale, longitudi…
arXiv:2602.03396v3 Announce Type: replace
Abstract: Proprietary large language models (LLMs) embody substantial economic value and are generally exposed only as black-box APIs, yet adversaries can still exploit their outputs to extract knowledge via d…
arXiv:2605.03314v2 Announce Type: replace
Abstract: In single-stream autoregressive interfaces, the same tokens both update the model state and constitute an irreversible public commitment. This coupling creates a silence tax: additional deliberation …
arXiv:2605.04208v1 Announce Type: new
Abstract: Large language models (LLMs) have demonstrated impressive multilingual capabilities for well-resourced languages, yet their performance on low-resource African languages remains poorly understood and lar…
arXiv:2605.04180v1 Announce Type: new
Abstract: Large Language Models exhibit strong reasoning and semantic understanding capabilities but often hallucinate in domains that require expert knowledge, among which fabrications, the generation of factuall…
arXiv:2605.04221v1 Announce Type: new
Abstract: Clinical named entity recognition from dental progress notes is challenging because documentation is highly unstructured, domain-specific, and often privacy-sensitive. We developed a locally deployable f…
arXiv:2605.04070v1 Announce Type: cross
Abstract: Human-AI complementarity, the idea that combining human and AI judgments can outperform either alone, offers a promising pathway toward robust oversight of advanced AI systems. However, whether human-A…