Aya Vision: Advancing the Frontier of Multilingual Multimodality research paper by Cohere, 2025
Cohere · Multimodal and robotics · May 13, 2025 · 37 citations · 14 upvotes
What it shows
Multi-modal language models are enhanced through synthetic data creation and cross-modal merging techniques, achieving superior performance in multilingual settings.
By Saurabh Dash, Yiyang Nan, John Dang and 22 more · arXiv 2505.08751 · PDF
Hugging Face's summary; not yet checked by hand.
More from Cohere
All 30Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language ReasoningOptimized supervised fine-tuning data composition enables reasoning models to consistently process and respond in diverse non-English languages without requiring reasoning supervision in each target language.
Cohere33 upvotesRead →The IOL-AI Challenge: An Open Challenge towards Advancing Linguistic ReasoningAn open competition on unseen Linguistics Olympiad puzzles, graded by the official jury; small 14B systems beat models twice their size.
Cohere1 citationsRead →CALIBER: Calibrating Confidence Before and After Reasoning in Language ModelsTrains reasoning models to state their confidence twice, before thinking and after answering, halving calibration error against the best single-estimate method.
Cohere1 citationsRead →AI Exposure Scores: what they measure, what they miss, and what comes nextReviews how widely cited scores of which jobs AI can assist are used in policy, what they miss, and newer measures that address it.
CohereRead →The Culture Funnel: You Can't Align What isn't in the DataModern LLM pipelines experience a cultural data funnel where explicit cultural signals diminish during post-training, necessitating shifts in training data approaches for better cultural alignment.
Cohere1 citationsRead →Soft-SVeRL: Self-Verified Reinforcement Learning with Soft RewardsTurns each prompt into a checklist scored item by item, giving reinforcement learning partial-credit rewards for tasks that cannot be checked automatically.
Cohere1 citationsRead →Agents Explore but Agents Ignore: LLMs Lack Environmental CuriosityLLM-based agents fail to exploit discovered unexpected information despite recognizing it, indicating a lack of environmental curiosity that depends on tools, compute, and training data distribution.
Cohere2 citations8 upvotesRead →Tiny Aya: Bridging Scale and Multilingual DepthTiny Aya demonstrates high-quality multilingual capabilities with 3.35 billion parameters through region-aware posttraining and balanced language performance.
Cohere18 citations10 upvotesRead →
Other multimodal and robotics papers
TopicSegment AnythingA promptable model and a dataset of over a billion masks that cut out any object in any image.
Meta16.8k citations6 upvotesRead →BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language ModelsBLIP-2 efficiently pre-trains vision-language models using off-the-shelf frozen encoders and decoders, achieving state-of-the-art performance with fewer parameters.
Salesforce9,805 citations3 upvotesRead →BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and GenerationBLIP, a Vision-Language Pre-training framework, improves performance across both understanding and generation tasks by bootstrapping captions from noisy web data, achieving state-of-the-art results on image-text retrieval, image captioning, and VQA, and showing strong generalization to video-language tasks.
Salesforce7,655 citations5 upvotesRead →GPT-4o System CardGPT-4o is an omnimodal autoregressive model trained to handle text, audio, image, and video inputs, offering high-performance outputs across these modalities, with particular strengths in vision and audio.
OpenAI5,388 citations88 upvotesRead →SAM 3: Segment Anything with ConceptsSegment Anything Model 3 achieves state-of-the-art performance in promptable concept segmentation and tracking by leveraging a unified model architecture with decoupled recognition and localization.
Meta1,466 citations138 upvotesRead →DeepSeek-VL: Towards Real-World Vision-Language UnderstandingDeepSeek-VL is an open-source vision-language model that achieves state-of-the-art performance in real-world applications by combining a hybrid vision encoder with effective pretraining strategies to preserve language model capabilities.
DeepSeek939 citations49 upvotesRead →
About this paper
- Venue
- arXiv.org
- Citations
- 37, 6 influential · Semantic Scholar
- Upvotes
- 14 · Hugging Face
- Lab
- Cohere · on Companies · on Acquisitions · on Releases
Changes
Influential citations fell from 7 to 6.
Citations rose from 36 to 37.
Citations rose from 35 to 36.
A first count of 7 influential citations.
A first count of 35 citations.
Added to the list.