Open Science Community
Research notes, technical essays, and personal stories from the Cohere Labs Community
Research notes, stories, and ideas from people shaping AI together—transparent, collaborative, and community-led.
We trained and released Sparse Autoencoders on all four Tiny Aya regional variants and asked what an SAE sees inside a model built for 70+ languages: a representational-density gap, a mostly-shared feature basis, and an identity-versus-quality null.
We adapt Kyutai's Moshi architecture for Turkish↔Hindi speech-to-speech translation by replacing the audio-pretrained backbone with Cohere's multilingual TinyAya, and share what worked, what broke, and what we learned.
Sinhala NLP has often been framed as a data-scarcity problem, but the next major step is better evaluation: benchmarks that test real Sinhala, local context, code-mixing, and everyday use.
RLVR fine-tuning raises accuracy on medical VQA benchmarks while quietly degrading visual grounding: a new counterfactual evaluation framework identify the gap.
The people, projects, and conversations that turned a moment of change into a community I could give back to.
