From Multimodal Depression Detection to Long-Context Language Models: 3 Recent arXiv Papers in Brief
From Multimodal Depression Detection to Long-Context Language Models: 3 Recent arXiv Papers in Brief This brief looks at three recent arXiv papers from different parts of AI: a multimodal system for binary depression detection, a long-context language modeling approach called ResonatorLM, and a graph learning paper that reinterprets attention through the lens of denoising and diffusion. All three were posted on arXiv in July 2026, and each focuses on a different core problem: separating difficult decision boundaries in mental health screening, handling long-range context efficiently in language models, and understanding whether attention is the right mechanism for graph denoising. [S1][S6][S8] [S1] [S6] [S8] Introduction: paper names and release context The first paper, Uncovering Latent Depression Severity for Binary Depression Detection via Advantage-weighting Ranking , addresses automatic depression detection from audio-visual data and was announced on arXiv as a new paper. The s...