Findings support a hybrid paradigm in which AI augments, but does not replace, health economists in value assessment and formulary decision support within managed care settings.
R. Mudumba, A. Modi, Kevin Mayo· Journal of Managed Care & Sp...· 0 citations
A hybrid model, termed SE_LLM_ST, is introduced, which leverages recent advancements in large language models (LLMs) and transformer-based architectures to effectively capture both contextual and sequential information vital for Persian emotion recognition.
Toktam Khatibi, Elham Farahani· SN Computer Science· 0 citations
MEPO-SLM is presented, a framework that reformulates prompt engineering for SLMs as a four-objective Pareto problem over task inaccuracy, and Phi-3-mini and Gemma-2B on English TriviaQA and Arabic medical QA, and TinyLlama-1.1B on TriviaQA only are evaluated.
Yousef K. Sanjalawe, Salam R. Al-E'mari, S. Makhadmeh· Evolutionary Intelligence· 0 citations
A role-specialized Mixture-of-Agents (MoA) that combines medical knowledge retrieval with contrastive similar-patient reasoning is studied, placing role design as a key factor in privacy-constrained, training-free clinical LLM prediction.
Jun Hou, Yi Fang, Xuan Wang· 0 citations
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Mathematics has often been organized around an authorial subject: one person, or a small group, composing proofs through language, notation, and judgment. Large language models, proof assistants, formal libraries, and repositories now make another production unit technically credible: a human-machine assemblage. This a...
SchemaGUI, a template-based benchmark for controllable GUI generation evaluation, synthesizing paired natural language instructions and deterministic function-call references from parameterized interface schemas can generate thousands of deterministically annotated tasks in seconds without human labeling.
Jiarui Dong, Yin Cai, Zhouhong Gu et al.· 0 citations
This work introduces Instruction-Followed Function Calling (IFFC), a novel framework that decouples function-calling logic from the primary LLM and delegates it to a dedicated smaller model operating within the instruction-following paradigm, establishing a new paradigm for reliable, resource-efficient function calling...
Yalda Taheri, Mohammad Hassan Heydari, Erfan Naaman et al.· 0 citations
Dual-Layer Agentic Memory is proposed, a framework that shifts memory management to the write phase through cost-aware epistemic routing and periodic parametric consolidation, allowing the router to adaptively suppress redundant writes as the model's epistemic boundaries evolve.
Wen-Zhi Li, Dong Nie, Ruiyi Lan et al.· 1 citation
Two state-of-the-art multimodal models, Gemma-3 and Qwen-VL, are assessed on their ability to interpret mechanical problem images by eliciting a step-by-step chain of thought (CoT) and a final answer, and final answers are compared to verified solutions to measure accuracy.
Henry Fordjour Ansah, Shreya Banerjee, Pranish Ghimire· 0 citations
NeuroPrefetcher is presented, a storage-backed LLM inference system that exploits that MLP activity during autoregressive decoding has strong temporal locality, and achieves 7.9-12.0x speedup over llama.cpp across constrained memory budgets.
Nobel Dhar, Md Romyull Islam, Xue-Chen Zhang et al.· Proceedings of the Internati...· 0 citations
This work adapts Hugging Face's SmolVLA for Universal Robots lightweight robots, and releases the open-source repository ROS2SmolVLA that implements an interface for ROS 2 to SmolVLA, and makes it applicable for industrial-grade hardware.
Nils Mandischer, Noah Böckmann, Ludwig Holl et al.· 0 citations
A training-free, model-agnostic memory graph that repairs continuity entirely at the prompt-text layer by extracting a multi-dimensional state for every shot, retrieving only causally prior context over the resulting graph, filtering it selectively, and injecting the surviving constraints by natural-language prompt rew...
Jiaqi Liu, Maolin Ran, Xiaoyan Lu et al.· 0 citations
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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