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: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...
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...
arXiv:2608. 08736v1 Announce Type: new Abstract: Fitness Action Quality Assessment (AQA) is important for intelligent sports training, yet the capabilities of Multimodal Large Language Models (MLLMs) in this setting remain underexplored.
arXiv:2607. 02118v1 Announce Type: new Abstract: Scientific Fitness Coaching (SFC) is typically delivered by human professionals, making it costly and inaccessible to many.
arXiv:2606. 28570v1 Announce Type: cross Abstract: Athlete assessment is a critical process for tracking physical progress and identifying elite talent.
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.
The paper introduces PART, a multimodal predictive framework for tennis that combines physiological, training, sleep, questionnaire, jump, and video data from nine collegiate players to assess overall wellness, injury risk, physical capability, and playing style. Using machine learning and deep learning, PART provides holistic athlete assessments and forecasts specific injury risks to body areas such as elbows and knees. Evaluation shows strong performance in predicting wellness and injury risk, with potential benefits for recreational players who often injure themselves due to poor technique.
arXiv:2607. 16597v1 Announce Type: new Abstract: The rapid digitalisation of elite sport has created new opportunities for integrating artificial intelligence (AI), performance analytics, and decision-support systems into athlete development and competition management.
arXiv:2604. 05360v2 Announce Type: replace-cross Abstract: Gait analysis is essential in post-stroke rehabilitation but remains time-intensive and cognitively demanding, especially when clinicians must integrate gait videos and motion-capture data into structured reports.
MyoMechanix is a multimodal dataset and framework for action quality assessment that incorporates muscle activity and other physiological signals alongside visual data. It contains over 7,500 samples of 20 weight‑loaded actions from 38 subjects, with synchronized RGB video, 3D pose, sEMG, and additional signals. The accompanying Fitness Knowledge Graph structures expert annotations into relationships among actions, phases, key steps, errors, and corrective feedback, enabling compositional scoring and interpretable assessment through the CUBIST engine. The project also introduces MyoMechanix‑AQA, MyoMechanix‑VideoQA, and a novel MyoMechanix‑Video2EMG task, demonstrating that multimodal sensing and structured representations improve performance, interpretability, and error attribution.
The paper introduces a retrieval‑augmented multi‑agent framework that automatically generates instance‑specific evaluation rubrics for medical language models. By retrieving authoritative medical evidence, decomposing it into atomic facts, and combining these with user interaction constraints, the system produces fine‑grained criteria that outperform GPT‑4o on HealthBench and LLMEval‑Med. The generated rubrics also guide response refinement, improving medical LLM output quality by 9.2%.
AI Soccer Analyst is a mixed‑initiative system that guides analysts through six revisable stages—Data Understanding, Problem Definition, Structured Planning, Execution, Evidence‑Grounded Reporting, and Interaction and Refinement—to produce soccer data analyses. A formative study with five analysts shaped design goals around automation, verifiability, human control, and accessibility, while a task‑based evaluation with 16 participants showed that 33 of 48 tasks met completion criteria and participants reported high quality, reliability, and verifiability of outputs. Interaction logs revealed that domain knowledge emerged during clarification, planning, and refinement, demonstrating that stage‑aware human‑AI collaboration can produce inspectable, revisable, and verifiable analyses while keeping domain experts in control.