ArogyaSutra: Multi-agent framework for multimodal medical reasoning in Indic languages
Researchers introduce ArogyaSutra, an actor-critic-based multi-agent framework for multilingual multimodal medical reasoning targeting Indic languages, alongside ArogyaBodha, a large-scale dataset spanning 31 body systems, six imaging modalities, and 21 clinical domains across English and seven Indian languages. The framework integrates tool grounding with dual-memory mechanisms and uses actor-critic simulation trajectories for distillation. The work addresses a critical gap in AI healthcare access for low-resource, multilingual settings like rural India where English-centric MLLMs fall short.
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Researchers introduce OpenMedReason, a 450K-instance open multimodal medical reasoning corpus with reasoning traces derived from human-authored biomedical literature rather than synthetic chains of thought. The dataset covers diverse medical imaging modalities and is paired with OpenMedReason-Bench, a held-out benchmark evaluating LVLMs on perception, medical knowledge, and rationale axes. Training with OpenMedReason yields a 20% average VQA accuracy improvement over base models and achieves performance within 4.2% of leading comparable-scale medical VLMs. Both the dataset and code are publicly released.
agent-teams-ai: multi-agent orchestration framework with kanban-style oversight
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A Deepdive into Aya Vision: Advancing the Frontier of Multilingual Multimodality
Cohere's Aya Vision is a multilingual multimodal model designed to extend vision-language capabilities beyond English-centric systems. The blog post provides a technical deep-dive into the model's architecture, training approach, and multilingual evaluation results. It represents a notable push toward broader language coverage in multimodal AI, targeting underrepresented languages in the vision-language space.

