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BALMS is the first systematic benchmark of LLM-based agentic systems for longitudinal mental health sensing and highlights the need for longitudinal mental health agents that selectively retrieve history, ground temporal evidence, and reason over interpretable behavioral features.
SLIP (Sensor Language-Informed Pretraining), an open-source framework for learning language-aligned representations that generalize across diverse sensor setups, is introduced, offering broad implications for the development of sensor-language models.
TimeThink, a synthetic framework for eliciting compositional timeseries reasoning that employs a reinforcement learning with verifiable rewards (RLVR) training strategy that encourages explicit reasoning, significantly outperforms strong baselines on both synthetic and real-world benchmarks.
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