Topic: vision-language models

Supports learned and generative processing of limited sensor observations in compact, low-power devices. Research topics include generating visual experiences beyond capture constraints, efficient visual encoding for on-device vision-language models, and learned multimodal sensor fusion for object tracking. Proposals should address realistic power, bandwidth, noise and capture constraints and explain how research advances perception. Use the ARA proposal template; four pages excluding appendices are encouraged. Decisions are expected in February 2027. Applications close on 4 November 2026 at

Munawar Hayat discusses visual grounding and object hallucination in vision-language models. Topics include attention-guided alignment, contrastive learning for combined image-and-text retrieval, and evaluating images containing multiple people. The conversation connects identity leakage and attribute blending with the constraints of efficient on-device deployment.