{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1","slug":"language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1","name":"Language Is Not All You Need: Aligning Perception with Language Models (Kosmos-1)","type":"product","url":"https://arxiv.org/abs/2302.14045v2","page_url":"https://unfragile.ai/language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1__cap_0","uri":"capability://image.visual.arbitrarily.interleaved.multimodal.input.processing","name":"arbitrarily-interleaved multimodal input processing","description":"Processes text and images in arbitrary sequential order within a single input stream, using a unified tokenization scheme that treats visual and textual tokens as equivalent sequence elements. This enables the model to maintain spatial and semantic relationships between modalities without requiring separate encoding pipelines or modal-specific preprocessing, allowing natural mixed-media prompts like 'Here is an image [IMG] of a cat. What color is it?' to be processed end-to-end.","intents":["Process documents with mixed text and images without separate OCR or vision preprocessing steps","Build multimodal dialogue systems that reference images inline with text queries","Create applications that accept naturally-formatted mixed-media inputs without modal segregation"],"best_for":["Multimodal AI researchers building unified perception-language systems","Teams developing document understanding systems that must handle scanned PDFs with embedded text and images","Builders creating conversational AI that references images contextually within dialogue"],"limitations":["No specified maximum image resolution or count per input sequence — likely constrained by context window size","Architectural details on modal token alignment not disclosed in abstract; implementation approach unknown","No information on handling variable image aspect ratios or extreme resolution disparities"],"requires":["Input images in standard formats (JPEG, PNG, etc. — specific formats not specified)","Text encoded in UTF-8 or compatible encoding","Sufficient context window capacity for interleaved sequences (window size not disclosed)"],"input_types":["text","image"],"output_types":["text"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1__cap_1","uri":"capability://image.visual.ocr.free.document.image.understanding","name":"ocr-free document image understanding","description":"Directly processes document images (scanned PDFs, photographs of text, handwritten notes) without requiring separate Optical Character Recognition preprocessing, extracting text and semantic meaning from visual document representations through end-to-end multimodal learning. 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The model is trained from scratch (not fine-tuned from existing models) on diverse multimodal data, learning to represent images and text in a shared embedding space.","intents":["Leverage web-scale multimodal data to build general-purpose vision-language models","Create foundation models that can be adapted to diverse downstream tasks","Study how multimodal pretraining affects model capabilities and generalization"],"best_for":["Researchers building foundation models and studying pretraining approaches","Organizations with access to large multimodal datasets seeking to build custom models","Teams studying transfer learning and representation learning in multimodal settings"],"limitations":["Training data composition and filtering criteria not disclosed — may contain biases from web data","No information on data deduplication, quality filtering, or removal of harmful content","Pretraining approach (contrastive learning, masked language modeling, etc.) not specified in abstract","Computational cost and training time not disclosed","No ablation studies on data composition or training objectives"],"requires":["Web-scale multimodal corpora (billions of image-text pairs)","Significant computational resources for training (GPU/TPU clusters, training time not specified)","Distributed training infrastructure for large-scale pretraining"],"input_types":["text","image"],"output_types":["embeddings","text"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1__cap_2","uri":"capability://planning.reasoning.zero.shot.and.few.shot.multimodal.instruction.following","name":"zero-shot and few-shot multimodal instruction following","description":"Executes visual and language tasks specified via natural language instructions without task-specific fine-tuning, using in-context learning to adapt to new tasks from 0 to K examples provided in the prompt. The model generalizes from training on diverse multimodal tasks to follow arbitrary new instructions at inference time, leveraging learned patterns of instruction-following from pretraining on web-scale data.","intents":["Apply the model to new visual tasks (image classification, VQA, captioning variants) without retraining","Build few-shot learning systems that adapt to domain-specific tasks with minimal labeled examples","Create flexible AI systems that follow natural language task specifications without engineering task-specific prompts"],"best_for":["Researchers exploring generalization and transfer learning in multimodal models","Teams building flexible AI systems that must handle diverse tasks without retraining","Developers prototyping new multimodal applications where labeled training data is scarce"],"limitations":["No disclosed performance degradation curves for few-shot scenarios (how many examples needed for X% accuracy)","Likely exhibits task-specific performance variance — some instructions may be followed more reliably than others","No information on instruction format robustness (sensitivity to phrasing, length, complexity)","Few-shot performance likely degrades with very complex or out-of-distribution tasks"],"requires":["Natural language task specification in English (language support not disclosed)","For few-shot: 1-K labeled examples in the prompt (optimal K not specified)","Task must be expressible in natural language instructions"],"input_types":["text","image"],"output_types":["text"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1__cap_3","uri":"capability://image.visual.multimodal.visual.question.answering.vqa","name":"multimodal visual question answering (vqa)","description":"Answers natural language questions about images by jointly processing visual content and textual queries, generating free-form text responses that demonstrate understanding of image semantics, spatial relationships, object properties, and scene context. The model learns to ground language in visual features through training on image-question-answer triplets, enabling reasoning over visual content.","intents":["Build conversational interfaces that answer questions about user-provided images","Create accessibility tools that describe image content in response to natural language queries","Develop image understanding systems that go beyond classification to answer complex questions about visual content"],"best_for":["Teams building image-based search or discovery systems with natural language queries","Accessibility-focused projects creating image understanding tools for visually impaired users","Researchers evaluating multimodal reasoning and visual grounding in language models"],"limitations":["No disclosed VQA benchmark results or accuracy metrics (e.g., VQA v2, GQA scores)","Likely struggles with questions requiring precise counting, spatial reasoning, or fine-grained visual details","No information on handling questions about multiple images or temporal sequences","Performance on out-of-distribution visual domains (medical imaging, satellite imagery) unknown"],"requires":["Image in standard format (JPEG, PNG, etc.)","Natural language question in English (language support not disclosed)","Question must be answerable from visual content alone (no external knowledge required for best performance)"],"input_types":["image","text"],"output_types":["text"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1__cap_4","uri":"capability://image.visual.image.captioning.and.visual.description.generation","name":"image captioning and visual description generation","description":"Generates natural language descriptions of image content, learning to identify objects, actions, spatial relationships, and scene context from visual input and produce coherent multi-sentence captions. The model is trained on image-caption pairs from web-scale corpora, learning to map visual features to descriptive language without explicit object detection or scene graph annotations.","intents":["Generate alt-text and accessibility descriptions for images automatically","Create image metadata and search indices through natural language descriptions","Build content creation tools that automatically caption user-uploaded images"],"best_for":["Content platforms and publishing systems requiring automatic image descriptions","Accessibility teams generating alt-text at scale for image archives","Researchers evaluating visual-to-linguistic transfer in multimodal models"],"limitations":["No disclosed caption quality metrics (BLEU, CIDEr, METEOR scores) or comparison to existing captioning models","Likely generates generic descriptions rather than detailed, domain-specific captions","No information on caption length control or style variation","May struggle with images containing text, charts, or specialized visual content"],"requires":["Image in standard format (JPEG, PNG, etc.)","Sufficient model capacity to learn visual-to-linguistic mapping (parameter count not disclosed)"],"input_types":["image"],"output_types":["text"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1__cap_5","uri":"capability://planning.reasoning.multimodal.chain.of.thought.reasoning","name":"multimodal chain-of-thought reasoning","description":"Performs step-by-step reasoning over images and text by generating intermediate reasoning steps that reference visual content, enabling complex multimodal reasoning tasks that require decomposing problems into sequential logical steps. The model learns to interleave visual references with textual reasoning during training, allowing it to explain visual reasoning processes.","intents":["Build systems that explain visual reasoning in natural language (e.g., 'Here's what I see in the image, and here's why I conclude X')","Create educational tools that teach visual reasoning through step-by-step explanations","Develop debugging and interpretability tools that expose multimodal reasoning processes"],"best_for":["Researchers studying interpretability and explainability in multimodal models","Educational technology teams building visual reasoning tutors","Teams developing high-stakes applications (medical imaging, autonomous systems) requiring explainable decisions"],"limitations":["No disclosed evaluation metrics for reasoning quality or step-by-step accuracy","Likely generates plausible-sounding but potentially incorrect intermediate steps (hallucination risk)","No information on reasoning depth limits or performance degradation with complex multi-step problems","Training data composition for chain-of-thought examples unknown"],"requires":["Image and text input in standard formats","Task must be expressible as a step-by-step reasoning problem","Prompt format that encourages step-by-step reasoning (e.g., 'Let's think step by step')"],"input_types":["image","text"],"output_types":["text"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1__cap_6","uri":"capability://planning.reasoning.nonverbal.reasoning.and.abstract.visual.pattern.recognition","name":"nonverbal reasoning and abstract visual pattern recognition","description":"Solves abstract visual reasoning tasks (e.g., Raven's Progressive Matrices IQ tests) that require identifying patterns, relationships, and transformations in visual sequences without relying on language or domain knowledge. The model learns to recognize visual patterns, analogies, and logical progressions through multimodal pretraining, enabling reasoning about abstract visual structure.","intents":["Evaluate general intelligence and reasoning capabilities of multimodal models beyond language-dependent tasks","Build systems that solve visual puzzle and pattern recognition tasks","Assess transfer learning from language to abstract visual reasoning"],"best_for":["AI researchers evaluating model reasoning capabilities and general intelligence","Teams building puzzle-solving or game-playing systems","Researchers studying cross-modal transfer learning from language to visual reasoning"],"limitations":["No disclosed accuracy on Raven's matrices or other nonverbal reasoning benchmarks","Likely performs worse than specialized visual reasoning models trained on these tasks","No information on reasoning about 3D spatial relationships or dynamic visual sequences","Performance on novel pattern types not seen in training data unknown"],"requires":["Visual reasoning task in image format (Raven's matrices, pattern completion, etc.)","Task must be solvable through visual pattern recognition alone"],"input_types":["image"],"output_types":["text"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1__cap_7","uri":"capability://memory.knowledge.cross.modal.knowledge.transfer.language.to.vision.and.vision.to.language","name":"cross-modal knowledge transfer (language-to-vision and vision-to-language)","description":"Transfers learned knowledge between language and vision modalities during pretraining, enabling the model to leverage linguistic patterns to improve visual understanding and vice versa. The unified multimodal architecture allows gradients to flow between modalities during training, creating bidirectional knowledge transfer that improves performance on both language and vision tasks.","intents":["Improve visual understanding by leveraging language knowledge learned from text-only pretraining","Enhance language generation by grounding it in visual understanding from image data","Build more robust multimodal models that benefit from diverse data sources"],"best_for":["Researchers studying multimodal learning and knowledge transfer mechanisms","Teams building multimodal models where language and vision data are available in different proportions","Organizations seeking to improve model robustness through cross-modal regularization"],"limitations":["No disclosed ablation studies quantifying the contribution of cross-modal transfer to performance","Transfer effectiveness likely varies by task — some tasks may benefit more than others","No information on transfer direction asymmetry (language-to-vision vs vision-to-language)","Optimal data mixing ratios for language and vision data unknown"],"requires":["Multimodal training data containing both text-only and image-text pairs","Unified model architecture enabling gradient flow between modalities","Sufficient training compute to learn cross-modal representations"],"input_types":["text","image"],"output_types":["text"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1__cap_8","uri":"capability://image.visual.image.classification.via.natural.language.instructions","name":"image classification via natural language instructions","description":"Classifies images into categories specified through natural language descriptions rather than fixed class indices, enabling flexible classification without retraining. The model maps image content to textual class descriptions learned during pretraining, allowing arbitrary classification schemes to be specified at inference time through language.","intents":["Build flexible image classification systems that adapt to new categories without retraining","Create zero-shot classification systems that classify into arbitrary user-specified categories","Develop image understanding systems that explain classifications in natural language"],"best_for":["Teams building flexible image tagging and categorization systems","Researchers exploring zero-shot and few-shot image classification","Organizations with evolving classification schemes that change frequently"],"limitations":["No disclosed zero-shot classification accuracy on standard benchmarks (ImageNet, CIFAR, etc.)","Likely performs worse than task-specific fine-tuned classifiers on fixed category sets","No information on handling ambiguous or overlapping class definitions","Performance degrades with very fine-grained or domain-specific classification tasks"],"requires":["Image in standard format (JPEG, PNG, etc.)","Natural language description of classification categories","Categories must be expressible in English (language support not disclosed)"],"input_types":["image","text"],"output_types":["text"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1__cap_9","uri":"capability://text.generation.language.multimodal.dialogue.and.conversational.understanding","name":"multimodal dialogue and conversational understanding","description":"Engages in multi-turn conversations that reference images, maintaining context across dialogue turns and answering follow-up questions about visual content. The model processes dialogue history along with images to generate contextually appropriate responses, enabling natural conversational interaction with visual content.","intents":["Build conversational AI assistants that can discuss images with users","Create interactive image exploration tools where users ask follow-up questions","Develop customer service systems that handle image-based inquiries (product photos, damage claims, etc.)"],"best_for":["Teams building conversational AI with visual understanding capabilities","Customer service platforms handling image-based inquiries","Researchers studying dialogue systems and conversational grounding"],"limitations":["No disclosed dialogue quality metrics or user satisfaction studies","Likely struggles with maintaining coherent context over very long dialogue histories","No information on handling multiple images across dialogue turns","May generate responses that contradict earlier statements in the conversation"],"requires":["Image(s) in standard format (JPEG, PNG, etc.)","Dialogue history in text format","Context window sufficient for dialogue history + image tokens (window size not disclosed)"],"input_types":["image","text"],"output_types":["text"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["Input images in standard formats (JPEG, PNG, etc. — specific formats not specified)","Text encoded in UTF-8 or compatible encoding","Sufficient context window capacity for interleaved sequences (window size not disclosed)","Document images in standard formats (JPEG, PNG, etc.)","Sufficient model capacity to learn text recognition patterns (parameter count not disclosed)","Training on multimodal corpora containing document-image pairs (not available for fine-tuning)","Web-scale multimodal corpora (billions of image-text pairs)","Significant computational resources for training (GPU/TPU clusters, training time not specified)","Distributed training infrastructure for large-scale pretraining","Natural language task specification in English (language support not disclosed)"],"failure_modes":["No specified maximum image resolution or count per input sequence — likely constrained by context window size","Architectural details on modal token alignment not disclosed in abstract; implementation approach unknown","No information on handling variable image aspect ratios or extreme resolution disparities","No disclosed accuracy metrics or comparison against dedicated OCR systems (Tesseract, commercial OCR)","Likely struggles with extremely low-resolution, heavily degraded, or non-Latin script documents","No information on handling multi-page documents or very large document images","Training data composition unknown — may have limited coverage of specialized document types","Training data composition and filtering criteria not disclosed — may contain biases from web data","No information on data deduplication, quality filtering, or removal of harmful content","Pretraining approach (contrastive learning, masked language modeling, etc.) not specified in abstract","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.25,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-06-17T09:51:03.577Z","last_scraped_at":"2026-05-03T14:00:27.894Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1","compare_url":"https://unfragile.ai/compare?artifact=language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1"}},"signature":"d2CKYuohopyO8kbGwTnjaTA8rYIeTHWC0ouHWFS5tzER7AIH6wnjr+KzDxK5CwtLzBNpjX0xNCoojBCCD8GkAw==","signedAt":"2026-06-20T16:16:47.643Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1","artifact":"https://unfragile.ai/language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1","verify":"https://unfragile.ai/api/v1/verify?slug=language-is-not-all-you-need-aligning-perception-with-language-models-kosmos-1","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}