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Keepace.ai: A Technical Report on a Large Language Model for Sports and Health
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First announced by arXiv: 2026-10-30 · Indexed by Pub-AI: 2026-10-30 UTC
Keepace.ai: A Technical Report on a Large Language Model for Sports and Health
First announced by arXiv: 2026-10-30 · Original record ↗
- arxiv
- 2610.38249
Explicit source versions (1)
- v1 · Date not supplied
- Preprint available
Source date: 2026-10-30 · First detected: 2026-10-30 UTC · Historical baseline
Source evidence (1)
- Official evidence ↗ · RSS guid=oai:arXiv.org:2610.38249v1; arxiv:announce_type=new; item pubDate (official metadata)
Abstract · arxiv
Large language models hold promise for delivering safe, scientifically grounded, and personalized exercise guidance at scale, yet the sports-and-health domain still lacks curated training corpora and rigorous evaluation benchmarks. Keepace.ai is a domain-specialized LLM built on Qwen3.5-27B through post-training, currently optimized for Chinese-language exercise guidance across three tasks: workout plan generation, sports-and-health knowledge QA, and personal sports data analysis. In internal rubric-based evaluations designed with domain experts, Keepace.ai outperforms its base model and larger general-purpose baselines, with the most pronounced improvements in exercise safety, boundary compliance, structured output completeness, and scientific accuracy. This report summarizes the current design, data pipeline, safety-first evaluation framework, and evaluation results of Keepace.ai.