We present a multi-resolution framework for analyzing longitudinal Arabic mental-health discourse drawn from condition-specific Arabic-language X (formerly Twitter) communities. We model each user’s posting history as a calendar-time semantic trajectory and apply the Discrete Wavelet Transform (DWT) along the temporal axis to separate semantic variation occurring over finer and broader time scales. To interpret these temporal components linguistically, we introduce semantic gain, which measures how strongly corpus-derived phrases become aligned with the reconstructed semantic trajectory when information from a given temporal scale is introduced. We evaluate the framework on longitudinal trajectories from BPD-related and bipolar-related X communities using independent temporal-change measures, chronological permutation tests, cross-scale phrase distinctiveness, and matched high- versus low- gain events. Finer-scale DWT components align most strongly with local adjacent semantic change, whereas broader-scale components show greater correspondence with distributional change measured by Maximum Mean Discrepancy (MMD). Our findings suggest that longitudinal mental-health discourse on X exhibits multiscale semantic structure, with different forms of linguistic change emerging over different temporal horizons. Combining wavelet decomposition with phrase-based semantic interpretation provides a principled way to characterize both the temporal scale and linguistic content of discourse change.

Large language model (LLM) agents have evolved to intelligently process information, make decisions, and interact with users or tools. A key capability is the integration of long-term memory capabilities, enabling these agents to draw upon historical interactions and knowledge. However, the growing memory size and need for semantic structuring pose significant challenges. In this work, we propose an autonomous memory augmentation approach, MemInsight, to enhance semantic data representation and retrieval mechanisms. By leveraging autonomous augmentation to historical interactions, LLM agents are shown to deliver more accurate and contextualized responses. We empirically validate the efficacy of our proposed approach in three task scenarios; conversational recommendation, question answering and event summarization. On the LLM-REDIAL dataset, MemInsight boosts persuasiveness of recommendations by up to 14%. Moreover, it outperforms a RAG baseline by 34% in recall for LoCoMo retrieval. Our empirical results show the potential of MemInsight to enhance the contextual performance of LLM agents across multiple tasks.

Wavelet transforms, a powerful mathematical tool, have been widely used in different domains, including Signal and Image processing, to unravel intricate patterns, enhance data representation, and extract meaningful features from data. Tangible results from their application suggest that Wavelet transforms can be applied to NLP capturing a variety of linguistic and semantic properties.In this paper, we empirically leverage the application of Discrete Wavelet Transforms (DWT) to word and sentence embeddings. We aim to showcase the capabilities of DWT in analyzing embedding representations at different levels of resolution and compressing them while maintaining their overall quality.We assess the effectiveness of DWT embeddings on semantic similarity tasks to show how DWT can be used to consolidate important semantic information in an embedding vector. We show the efficacy of the proposed paradigm using different embedding models, including large language models, on downstream tasks. Our results show that DWT can reduce the dimensionality of embeddings by 50-93% with almost no change in performance for semantic similarity tasks, while achieving superior accuracy in most downstream tasks. Our findings pave the way for applying DWT to improve NLP applications.

Summarizing medical conversations is one of the tasks proposed by MEDIQA-Chat to promote research on automatic clinical note generation from doctor-patient conversations. In this paper, we present our submission to this task using fine-tuned language models, including T5, BART and BioGPT models. The fine-tuned models are evaluated using ensemble metrics including ROUGE, BERTScore andBLEURT. Among the fine-tuned models, Flan-T5 achieved the highest aggregated score for dialogue summarization.