We introduce SensorLLM, a two-stage framework that enables Large Language Models (LLMs) to perform human activity recognition (HAR) from sensor time-series data. Despite their strong reasoning and generalization capabilities, LLMs remain underutilized for motion sensor data due to the lack of semantic context in time-series, computational constraints, and challenges in processing numerical inputs. SensorLLM addresses these limitations through a Sensor-Language Alignment stage, where the model aligns sensor inputs with trend descriptions. Special tokens are introduced to mark channel boundaries. This alignment enables LLMs to capture numerical variations, channel-specific features, and data of varying durations, without requiring human annotations. In the subsequent Task-Aware Tuning stage, we refine the model for HAR classification, achieving performance that matches or surpasses state-of-the-art methods. Our results demonstrate that SensorLLM evolves into an effective sensor learner, reasoner, and classifier through human-intuitive Sensor-Language Alignment, generalizing across diverse HAR datasets. We believe this work establishes a foundation for future research on time-series and text alignment, paving the way for foundation models in sensor data analysis. Our codes are available at https://github.com/zechenli03/SensorLLM.

Natural language interaction has long served as the primary medium through which humans exchange ideas. A key enabler of this communication is the human capacity for Theory of Mind (ToM)—the ability to infer and align with the mental states of others. ToM is usually modeled as components of desires, beliefs, and intentions. Research in linguistics and psychology has shown that people oftentimes reveal their ToM through pragmatic aspects of language. Considering the advancements in natural language generation and perception that Large Language Models (LLMs) have made in recent years, a critical question arises in relation to ToM: can LLM-powered agents develop similar abilities for inferring mental states during natural language communication? This study investigates the extent to which open-source LLaMA models can represent and retain ToM-related constructs, and whether these internal representations contribute to a coherent mental state modeling in a given conversation. Additionally, we explore the potential for manipulating ToM-related information to generate more aligned responses. Empirical evaluations of LLaMA-3 models (3B and 8B) demonstrate that ToM-informed alignment improves response quality, achieving win rates of 63% and 67%, respectively. These findings suggest that integrating ToM principles can enhance alignment in LLM-based conversational agents. For further details, refer to the [code repository](https://github.com/cruiseresearchgroup/ToM_and_Alignment).