MoveBench: A Rubric-Based Benchmark for Evaluating Large Language Models in Sports and Health Coaching
To address this limitation, we introduce MoveBench, the first comprehensive benchmarking framework specifically designed for evaluating LLMs in sports and health coaching.
ProofPaper ↗
Key points
- 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-purpose benchmarks (e.g., MMLU and HELM) primarily assess broad knowledge and reasoning capabilities, but fail to capture critical domain requirements such as exercise safety, physiological appropriateness, personalization, and actionable coaching quality.
- The proposed benchmark integrates heterogeneous task formats, including multiple-choice questions (MCQs) and open-ended question answering (OpenQA), grounded in real-world user scenarios and paired with structured rubrics emphasizing safety compliance, scientific validity, and training logic.
- Our work establishes a foundational evaluation standard for sports and health LLM applications and provides a reproducible framework for advancing safe, reliable, and context-aware AI-driven coaching systems.
Sources (1)
- [1]MoveBench: A Rubric-Based Benchmark for Evaluating Large Language Models in Sports and Health CoachingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 11:16 AM
To address this limitation, we introduce MoveBench, the first comprehensive benchmarking framework specifically designed for evaluating LLMs in sports and health coaching.
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.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 19, 2026Learning Who Talks to Whom: Role-Aware Communication Topology Scheduling for Efficient LLM Multi-Agent Collaboration
- Oct 18, 2026Toward Agentic Retrieval-Augmented Generation for Enterprise Retail Workflows over Heterogeneous Data Sources
- Oct 17, 2026A Cross-Domain Evaluation of Prompting Techniques for Large Language Models
- Oct 17, 2026EpochGo: Local-first multi-agent AutoML engine using offline Ollama LLMs and deterministic Scikit-Learn pipelines [P]
- Oct 17, 2026AdamPM: Memory-Efficient LLM Optimization via Moment Pooling
- Oct 16, 2026[Research] 2,788 tokens/s on a single RTX 5090 with a diffusion LLM — Introducing ORL (fully open source)