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arXiv:2610.26185v2 Announce Type: replace Abstract: Despite rapid advances in large language models (LLMs), their deployment in the sports and health domain remains high-risk due to the absence of do...
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.
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: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:2601.13880v2 Announce Type: replace Abstract: Personalized lifestyle health analysis requires long-horizon, multi-dimensional reasoning over heterogeneous lifestyle signals, and recent advances...