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627 papers · 45 labs

Aya Vision: Advancing the Frontier of Multilingual Multimodality research paper by Cohere, 2025

Cohere · Multimodal and robotics · May 13, 2025 · 37 citations · 14 upvotes

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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 30
ReasoningSep 9, 2026Building 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 →ReasoningAug 18, 2026The 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 →ReasoningJun 23, 2026CALIBER: 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 →Foundation modelsJun 22, 2026AI 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 →Alignment and safetyJun 11, 2026The 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 →Foundation modelsMay 27, 2026Soft-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 and evaluationApr 19, 2026Agents 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 →Training and scalingMar 12, 2026Tiny 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 →
Topic
Multimodal and roboticsApr 5, 2023Segment AnythingA promptable model and a dataset of over a billion masks that cut out any object in any image.Meta16.8k citations6 upvotesRead →Multimodal and roboticsJan 30, 2023BLIP-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 →Multimodal and roboticsJan 28, 2022BLIP: 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 →Multimodal and roboticsUnverifiedOct 25, 2024GPT-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 →Multimodal and roboticsUnverifiedNov 20, 2025SAM 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 →Multimodal and roboticsUnverifiedMar 8, 2024DeepSeek-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

CitationsNov 9, 2026Influential citations fell from 7 to 6.
CitationsNov 2, 2026Citations rose from 36 to 37.
CitationsOct 12, 2026Citations rose from 35 to 36.
CitationsSep 28, 2026A first count of 7 influential citations.
CitationsSep 28, 2026A first count of 35 citations.
New paperSep 26, 2026Added to the list.