Large language models (LLMs) are widely used in decision-making across diverse domains. Ensuring the generation of safe and reliable responses is critical for the effective deployment of LLM-based applications, particularly in high-stakes domains such as healthcare and finance. Most of these applications typically use carefully crafted prompts to guide response generation; however, the relationship between prompts and the reliability of LLM-generated responses is not yet fully understood. To address this gap, we propose a novel prompt-response concept model that explains the relationship between the amount of task-relevant information (informativeness) provided in the prompt and the LLM-generated response uncertainty by identifying four sources of response uncertainty: prompt underspecification, model quality, task variability, and semantic redundancy. We prove that response uncertainty decreases as prompt informativeness or model quality increases, mirroring the behavior of epistemic uncertainty in probabilistic models. Our experimental results on real-world datasets further validate our proposed model and corroborate the theoretical results.

Sovereign AI deployments often require foundation models to operate under constraints overlooked by standard benchmarks: adherence to legislation, multilingual and multimodal grounding in local contexts, institution-specific safety behaviors, and on-premises deployment on power-constrained infrastructure. We present a production case study detailing the multi-stage engineering pipeline that spans trillion-token-scale continued pretraining, multi-billion-token alignment and post-training for a 123B-parameter natively multimodal foundation model, *Phoenix-VL 1.5 Medium*, optimized for Singapore public-sector use. On our novel localized evaluation suite and behavioral safety framework, the model substantially improves localized multimodal, statutory, and public-safety capability (a +0.118 average gain over the strongest baselines) while retaining broad general capability. We also share empirical analysis from a full production deployment currently serving over 30,000 users and processing ∼1 billion tokens over a two-month period. Ultimately, our findings demonstrate that sovereign AI is best approached as an end-to-end systems problem, where data curation, domain training, institutional alignment, and hardware layout interact and cannot be optimized in isolation.

Retrieval-augmented generation (RAG) based on large language models often falters on narrative documents with inherent temporal structures. Standard unstructured RAG methods rely solely on embedding-similarity matching and lack any general mechanism to encode or exploit chronological information, while knowledge graph RAG (KG-RAG) frameworks collapse every mention of an entity into a single node, erasing the evolving context that drives many queries. To formalize this challenge and draw the community’s attention, we construct ChronoQA, a robust and discriminative QA benchmark that measures temporal, causal, and character consistency understanding in narrative documents (e.g., novels) under the RAG setting. We then introduce Entity-Event RAG (E ²RAG), a dual-graph framework that keeps separate entity and event subgraphs linked by a bipartite mapping, thereby preserving the temporal and causal facets needed for fine-grained reasoning. Across ChronoQA, our approach outperforms state-of-the-art unstructured and KG-based RAG baselines, with notable gains on causal and character consistency queries. E ²RAG therefore offers a practical path to more context-aware retrieval for tasks that require precise answers grounded in chronological information.