Cohere
Aya Expanse is a highly performant 32B multilingual model, designed to rival monolingual performance through innovations in instruction tuning with data arbitrage, preference training, and model merging. Serves 23 languages.
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Quality Score
1224
Arena ELO
32B
Parameters
131K
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Cohere Transcribe is now available on @superwhisper 💬 Push to talk and get your transcription back almost instantly. With Superwhisper, you can use Transcribe offline, integrate it into your favorite apps, and recall specialist vocabulary to maximize accuracy.
View sourceIntroducing North Automations Use plain language to design automated workflows with accurate results. Regardless of technical ability, Cohere enables all employees to harness the power of AI. https://t.co/599Bhfu9wg
View sourceC4ai AYA Expanse 32B is now available through local Ollama runtime. 8K context window listed. Cohere For AI's language models trained to perform well across 23 different languages.
View sourceCohere has proudly signed on to this letter. The importance of open-source models to the AI ecosystem cannot be understated. We believe everyone, in every country, should have control over the technology that will transform their lives. Sovereign AI for all.
View source
Cohere Transcribe is now available on @superwhisper 💬 Push to talk and get your transcription back almost instantly. With Superwhisper, you can use Transcribe offline, integrate it into your favorite apps, and recall specialist vocabulary to maximize accuracy.
Introducing North Automations Use plain language to design automated workflows with accurate results. Regardless of technical ability, Cohere enables all employees to harness the power of AI. https://t.co/599Bhfu9wg
Cohere has proudly signed on to this letter. The importance of open-source models to the AI ecosystem cannot be understated. We believe everyone, in every country, should have control over the technology that will transform their lives. Sovereign AI for all.
We present Wonder, a general-purpose video world model for real-time, camera-controllable world exploration. Given an image or a conditional video, Wonder constructs a playable world where users can navigate interactively by moving the camera, discovering unseen regions, and revisiting previously observed areas in real time and over a long-term horizon. Achieving this capability requires a system-level co-design of control method, memory mechanism, and training strategy. We introduce a novel camera conditioning with a dense coordinate field whose renderings provide spatially aligned motion and orientation cues, allowing the model to interpret camera motion directly as visual evidence. To support fast and precise memory retrieval over a growing generation context, we propose an efficient sparse attention-based memory mechanism, enabling the model to selectively attend to a small set of relevant context tokens at inference time, regardless of actual context length. We further develop several techniques to rectify the self-forcing-style distillation pipeline, improving the student model's ability to respect control signals, as well as maintaining diverse generation modes and long-term memory from the teacher. Together, these components enable Wonder to synthesize diverse, minute-scale videos at 16 FPS while preserving coherent geometry, appearance, and dynamics across long rollouts. Beyond image-to-video generation, Wonder naturally supports video-conditioned generation, allowing existing dynamic scenes to be re-shot in real time.
Hyperspectral imaging (HSI) is useful for material discrimination, but operational mine screening also depends on how many false alarms must be inspected before targets are found. This paper studies PFM-1 landmine detection in unmanned aerial vehicle (UAV) visible and near-infrared (VNIR) HSI using spectral angle mapper (SAM), matched filter (MF), adaptive coherence estimator (ACE), and constrained energy minimization (CEM). We compare a ground-measured SVC signature, a fully informed in-scene core-pixel signature, and a simulated human-in-the-loop signature bootstrap. Besides receiver operating characteristic area under the curve and average precision, we report target-discovery curves and spatial candidate-review counts. Full-review bootstrapping reaches the fully informed in-scene signature case after all seven target regions are verified, but the required inspection effort varies strongly: ACE confirms all regions in two rounds and nine candidate inspections, whereas the SAM variants need thousands of candidate reviews for their final target locations. Code is available at https://github.com/SagarLekhak/IEEE_WHISPERS_2026_UAV_HSI_PFM1.
C4ai AYA Expanse 32B is now available through local Ollama runtime. 8K context window listed. Cohere For AI's language models trained to perform well across 23 different languages.