This paper presents the USAI team’s submission to the CLPsych 2026 Shared Task, targeting Tasks~1.1, 1.2, 2, and~3.1. We propose an ensemble-based approach combining multiple open-source large language models, where the contribution of each model is weighted according to its alignment with clinically grounded human annotations on the training set. Our system achieves competitive results across the evaluated subtasks, with particularly strong performance on Tasks~1.2 and~2.

The growing demand for Mental Health (MH) services highlights the need for scalable computational tools, yet progress in computational psychology is hindered by scarce sensitive data, complex assessment procedures, and high technical barriers. While language is a well-established marker of different MH conditions, existing NLP solutions are often fragmented, closed-source, or difficult to use, limiting their adoption in interdisciplinary research.We present TONY, an open-source, python TOolkit for NLP in clinical psYchology. TONY bridges traditional psycholinguistic analysis and modern NLP by combining interpretable lexical features with state-of-the-art lightweight transformer models within a unified and easy-to-use framework. This hybrid approach enables robust and transparent text analysis without relying on large-scale models or closed-source software.TONY is designed for researchers and practitioners working at the intersection of NLP and MH, facilitating collaboration across disciplines. Compared to the few existing systems, TONY offers a more comprehensive and exhaustive solution, reducing the barrier to entry through a unified, modular, and reproducible pipeline that integrates classical and neural approaches in a single open framework. The toolkit is released under an open-source license and is evaluated through multiple MH–related datasets, demonstrating its flexibility and effectiveness in low-resource settings