Alif Munim

Stephen Obadinma, Faiza Khan Khattak, Shirley Wang et al. · utoronto

Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. In this paper, we outline the pipeline of the NAA's core system and also present three case studies in which three industry partners successfully adapt the framework to find solutions to their unique challenges. Our findings suggest that a collaborative process is instrumental in spurring the development of emerging NLP models for Conversational AI tasks in industry. The full reference implementation code and results are available at \url{https://github.com/VectorInstitute/NAA}

Alif Munim, Adibvafa Fallahpour, Teodora Szasz et al.

Foundation models for echocardiography promise to reduce annotation burden and improve diagnostic consistency by learning generalizable representations from large unlabeled video archives. However, current approaches fail to disentangle anatomical signal from the stochastic speckle and acquisition artifacts that dominate ultrasound imagery. We present EchoJEPA, a foundation model for echocardiography trained on 18 million echocardiograms across 300K patients, the largest pretraining corpus for this modality to date. We also introduce a novel multi-view probing framework with factorized stream embeddings that standardizes evaluation under frozen backbones. Compared to prior methods, EchoJEPA reduces left ventricular ejection fraction estimation error by 19% and achieves 87.4% view classification accuracy. EchoJEPA exhibits strong sample efficiency, reaching 78.6% accuracy with only 1% of labeled data versus 42.1% for the best baseline trained on 100%. Under acoustic perturbations, EchoJEPA degrades by only 2.3% compared to 16.8% for the next best model, and transfers zero-shot to pediatric patients with 15% lower error than the next best model, outperforming all fine-tuned baselines. These results establish latent prediction as a superior paradigm for ultrasound foundation models.