无需训练即可识别新动作，靠文本证据推理传感器数据
ZARA: Training-Free Motion Time-Series Reasoning via Evidence-Grounded LLM Agents
- 用统计知识库把信号转成可验证的自然语言先验
- 在8个基准上对未见人员和数据集表现稳健
- 适合需要快速部署、跨设备通用的动作识别场景
运动传感器时间序列是人体活动识别（HAR）的核心，但传统方法受限于固定活动集，新增行为需昂贵的参数重训练。尽管大语言模型（LLMs）具备开放集推理潜力，但直接应用于数值时间序列常引发幻觉且缺乏依据。为此，我们提出ZARA（零训练活动推理代理），一种知识与检索增强的代理框架，实现无训练推理下的运动时间序列理解。ZARA不依赖黑箱映射，而是将参考数据提炼为统计上可信的文本知识库，将隐含信号模式转化为可验证的自然语言先验。在检索到的证据指导下，ZARA迭代选择判别性线索，并对候选活动进行有依据的推理。在八个基准上的大量实验表明，ZARA在未见个体及跨数据集间表现出强泛化能力，展现出在异构传感器领域间的良好迁移性。这些结果标志着迈向可信赖、即插即用的运动理解迈出关键一步，超越特定数据集的局限。代码已开源：https://github.com/zechenli03/ZARA。
原文摘要 · Abstract (English)
Motion sensor time-series are central to Human Activity Recognition (HAR), yet conventional approaches are constrained to fixed activity sets and typically require costly parameter retraining to adapt to new behaviors. While Large Language Models (LLMs) offer promising open-set reasoning capabilities, applying them directly to numerical time-series often leads to hallucinations and weak grounding. To address this challenge, we propose ZARA (Zero-training Activity Reasoning Agents), a knowledge- and retrieval-augmented agentic framework for motion time-series reasoning in a training-free inference setting. Rather than relying on black-box projections, ZARA distills reference data into a statistically grounded textual knowledge base that transforms implicit signal patterns into verifiable natural-language priors. Guided by retrieved evidence, ZARA iteratively selects discriminative cues and performs grounded reasoning over candidate activities. Extensive experiments on eight benchmarks show that ZARA generalizes robustly to unseen subjects and across datasets, demonstrating strong transferability across heterogeneous sensor domains. These results mark a step toward trustworthy, plug-and-play motion understanding beyond dataset-specific artifacts. Our code is available at https://github.com/zechenli03/ZARA.
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