Despite rapid advances in large language models (LLMs), their deployment in the sports and health domain remains high-risk due to the absence of domain-specific evaluation benchmarks. Existing general...
Keepace.ai is a domain‑specialized large language model for sports and health, built on Qwen3.5‑27B through post‑training. It is optimized for Chinese‑language exercise guidance across three tasks: workout plan generation, sports‑and‑health knowledge QA, and personal sports data analysis. Internal rubric‑based evaluations with domain experts show that Keepace outperforms its base model and larger general‑purpose baselines, especially in exercise safety, boundary compliance, structured output completeness, and scientific accuracy.
arXiv:2509. 02594v3 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on their ability to generate high-quality, accurate, situationally aware answers to clinical questions requires going beyond conventional benchmarks to assess how these systems behave in complex, high-stakes clinical scenarios.
arXiv:2606. 28570v1 Announce Type: cross Abstract: Athlete assessment is a critical process for tracking physical progress and identifying elite talent.
arXiv:2606. 18203v1 Announce Type: cross Abstract: The LLM-empowered personal health agents with user health (sensor) metrics have offered a promising pathway to alleviate global disparities in healthcare access.
arXiv:2601.13880v2 Announce Type: replace Abstract: Personalized lifestyle health analysis requires long-horizon, multi-dimensional reasoning over heterogeneous lifestyle signals, and recent advances...
arXiv:2508. 00923v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to answer health-related questions and support healthcare workflows, yet evidence for their safety still relies heavily on static benchmarks that can rapidly become obsolete or be optimized against.
Health‑SCORE is a scalable rubric‑based framework designed to evaluate and train health‑focused large language models (LLMs). It reduces the need for extensive human expertise and development cost while maintaining evaluation quality comparable to human‑created rubrics. The framework also serves as a structured reward signal for reinforcement learning and can be embedded in prompts to enhance response quality via in‑context learning.
arXiv:2606. 05174v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong promise in healthcare applications.
The paper introduces OneMillion-Bench (OMB), a benchmark comprising 400 expert-curated tasks across Law, Finance, Industry, Healthcare, and Natural Science. OMB evaluates language agents on complex, real-world scenarios that require authoritative source retrieval, conflict resolution, domain-specific rule application, and constraint decision-making. The benchmark uses a rubric-based scoring system assessing factual accuracy, logical coherence, practical feasibility, and professional compliance to differentiate agent performance on expert-level problems.
arXiv:2609.24480v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic reasoning: the conv...
arXiv:2507. 02983v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) hold significant promise for transforming digital health by enabling automated medical question answering.