Update README.md

README.md

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 - multilingual
 pipeline_tag: any-to-any
 ---
-
 # Universal-Multimodal-Agent (UMA)
 
-## 🌟 Vision
-
-**Universal-Multimodal-Agent (UMA)** is a next-generation agentic, multimodal large language model designed to seamlessly integrate and process multiple modalities including text, images, tables, and audio. UMA represents a paradigm shift in AI capabilities, combining deep understanding across modalities with autonomous reasoning, retrieval-augmented generation, and explainable decision-making.
-
-This model is envisioned as a comprehensive AI assistant capable of handling complex, multi-step tasks that require cross-modal understanding, contextual reasoning, and transparent explanations—all while maintaining accessibility and adaptability across diverse domains.
-
-## 🎯 Core Capabilities
-
-### Multimodal Integration
-- **Text Processing**: Advanced natural language understanding, generation, and reasoning
-- **Vision Understanding**: Image analysis, OCR, visual reasoning, and scene comprehension
-- **Table Comprehension**: Structured data parsing, analysis, and reasoning over tabular information
-- **Audio Processing**: Speech recognition, audio classification, and sound event detection
-- **Cross-Modal Reasoning**: Seamless integration and reasoning across all modalities
-
-### Agentic Intelligence
-- **Autonomous Task Planning**: Break down complex objectives into actionable sub-tasks
-- **Tool Use & API Integration**: Interact with external systems and databases
-- **Memory & Context Management**: Maintain long-term context and learning from interactions
-- **Self-Reflection & Error Correction**: Evaluate outputs and adjust strategies dynamically
-
-### Retrieval-Augmented Generation (RAG)
-- **Dynamic Knowledge Retrieval**: Access and integrate external knowledge bases in real-time
-- **Context-Aware Search**: Retrieve relevant information across multiple data sources
-- **Fact Verification**: Cross-reference claims with retrieved evidence
-- **Source Attribution**: Transparent citation of information sources
-
-### Explainable AI & Reasoning
-- **Chain-of-Thought Reasoning**: Show step-by-step logical progression
-- **Evidence-Based Answers**: Provide supporting evidence for conclusions
-- **Confidence Estimation**: Communicate uncertainty and confidence levels
-- **Interpretable Decisions**: Explain reasoning processes in human-understandable terms
-
-## 💼 Use Cases
-
-### Enterprise Applications
-- **Business Intelligence**: Analyze reports, dashboards, and multi-source data
-- **Document Processing**: Extract insights from mixed-media documents (PDFs with text, images, and tables)
-- **Customer Support**: Multi-channel support handling text, voice, and visual queries
-- **Market Research**: Synthesize information from diverse data sources
-- **Compliance & Auditing**: Review complex documents with explainable findings
-
-### Education & Research
-- **Intelligent Tutoring**: Adaptive learning across subjects with visual aids and explanations
-- **Research Assistance**: Literature review, data analysis, and hypothesis generation
-- **Accessibility Tools**: Converting content between modalities for different learning styles
-- **Assessment & Feedback**: Evaluate student work with detailed, constructive feedback
-- **Interactive Learning**: Multi-sensory educational experiences
-
-### Code & Development
-- **Multi-Modal Code Understanding**: Analyze code with diagrams, documentation, and logs
-- **Debugging Assistant**: Visual debugging with stack traces, graphs, and explanations
-- **Documentation Generation**: Create comprehensive docs from code, comments, and examples
-- **System Design**: Reason about architecture diagrams and technical specifications
-- **API Integration**: Understand and implement based on documentation in various formats
-
-### Accessibility
-- **Vision Impairment Support**: Describe images, charts, and visual content in detail
-- **Hearing Impairment Support**: Transcribe and contextualize audio content
-- **Cognitive Accessibility**: Simplify complex information with clear explanations
-- **Alternative Format Conversion**: Transform content between modalities (text-to-speech, image-to-text, etc.)
-- **Personalized Assistance**: Adapt communication style to user needs
-
-## 🚀 Sample Prompts
-
-### Cross-Modal Analysis
-```
-"Analyze this financial report (PDF with tables and charts), extract key metrics, 
-compare them to last quarter, and explain the trends you observe."
-```
-
-### Retrieval-Augmented Question Answering
-```
-"Using current information, explain the latest developments in quantum computing, 
-compare the approaches of major research labs, and cite your sources."
-```
-
-### Accessibility Support
-```
-"Describe this architectural diagram in detail for someone who cannot see it, 
-including spatial relationships and design patterns."
-```
-
-### Agentic Task Execution
-```
-"Plan a research project on renewable energy trends: identify key questions, 
-suggest data sources, outline methodology, and create a timeline."
-```
-
-### Educational Assistance
-```
-"Explain quantum entanglement using analogies, visual descriptions, and 
-step-by-step reasoning suitable for a high school student."
-```
-
-### Code Understanding
-```
-"Review this codebase along with its architecture diagram and test results. 
-Identify potential bottlenecks and suggest optimizations with explanations."
-```
-
-## 🛠️ Technical Architecture
-
-UMA is designed with a modular architecture:
-
-1. **Multimodal Encoders**: Specialized encoders for each input modality
-2. **Unified Representation Space**: Cross-modal alignment and fusion layer
-3. **Reasoning Engine**: Transformer-based architecture with enhanced reasoning capabilities
-4. **Retrieval Module**: Integration with vector databases and knowledge graphs
-5. **Agentic Control Layer**: Task planning, tool use, and execution monitoring
-6. **Explanation Generator**: Dedicated module for producing interpretable outputs
-7. **Output Decoders**: Multi-format output generation (text, structured data, etc.)
-
-## 📊 Model Status
-
-**Current Status**: 🏗️ **Placeholder Repository - Research & Development Phase**
-
-This repository currently serves as a conceptual framework and placeholder for the Universal-Multimodal-Agent project. No trained model weights or training files are available yet. We are in active research and development to bring this vision to reality.
-
-### Development Roadmap
-- [ ] Phase 1: Architecture design and prototyping
-- [ ] Phase 2: Data collection and curation across modalities
-- [ ] Phase 3: Initial model training and evaluation
-- [ ] Phase 4: Agentic capabilities integration
-- [ ] Phase 5: RAG system implementation
-- [ ] Phase 6: Explainability module development
-- [ ] Phase 7: Community beta testing
-- [ ] Phase 8: Public release and continuous improvement
-
-## 🤝 Call for Collaboration
-
-We believe in the power of open research and community-driven innovation! We're seeking collaborators, researchers, and contributors who share our vision for accessible, explainable, and capable AI.
-
-### How to Contribute
-- **Researchers**: Share insights on multimodal learning, agentic AI, or explainability
-- **Engineers**: Help design and implement architectural components
-- **Data Scientists**: Contribute to dataset curation and evaluation frameworks
-- **Domain Experts**: Provide use case guidance and validation
-- **Community Members**: Test, provide feedback, and suggest improvements
-
-### Research Partnerships
-We welcome partnerships with:
-- Academic institutions working on multimodal AI
-- Organizations focused on accessibility technology
-- Industry partners with real-world use cases
-- Open-source communities developing complementary tools
-
-### Get Involved
-- 💬 **Discussions**: Join our [Community](https://huggingface.co/amalsp/Universal-Multimodal-Agent/discussions) tab to share ideas
-- 📧 **Contact**: Reach out for research collaborations
-- ⭐ **Star & Follow**: Stay updated on development progress
-- 🐛 **Issues**: Report bugs or suggest features (when available)
-
-## 📚 Inspiration & Related Work
-
-UMA draws inspiration from cutting-edge research in:
-- Multimodal large language models (GPT-4V, Gemini, BLIP-2)
-- Agentic AI systems (AutoGPT, BabyAGI, ReAct)
-- Retrieval-augmented generation (RAG, REALM, Atlas)
-- Explainable AI (Chain-of-Thought, Constitutional AI)
-- Accessibility technology (screen readers, alternative text generation)
-
-## 🔍 Future Directions
-
-### Near-Term Goals
-- Develop robust cross-modal alignment techniques
-- Implement efficient attention mechanisms for long-context processing
-- Create comprehensive evaluation benchmarks
-- Establish baseline performance metrics
-
-### Long-Term Vision
-- Real-time learning and adaptation
-- Personalization while maintaining privacy
-- Expanded modality support (video, 3D, sensor data)
-- Improved reasoning capabilities with formal verification
-- Seamless human-AI collaboration interfaces
-
-## ⚖️ Ethical Considerations
-
-We are committed to developing UMA responsibly:
-- **Transparency**: Clear documentation of capabilities and limitations
-- **Fairness**: Mitigating biases across modalities and demographics
-- **Privacy**: Protecting user data and implementing secure retrieval
-- **Safety**: Red-teaming and adversarial testing
-- **Accessibility**: Ensuring the model serves diverse user needs
-
-## 📜 License
+## New: Multimodal Datasets Catalog (Phase 1 Data Collection)
+We’re kicking off data collection for UMA. Below is a curated, growing catalog of top public multimodal datasets by category with brief notes and links for immediate use.
+
+### A. Text–Image
+- LAION-5B — Massive web-scale image–text pairs; LAION-400M/1B subsets available. https://laion.ai/blog/laion-5b/
+- COCO (2017 Captions) — Image captioning and detection; strong baselines. https://cocodataset.org/#home
+- Visual Genome — Dense region descriptions, objects, attributes, relationships. https://visualgenome.org/
+- Conceptual Captions (3M/12M) — Web image–alt-text pairs. https://ai.google.com/research/ConceptualCaptions/
+- Flickr30k / Flickr8k — Classic captioning sets. https://hockenmaier.cs.illinois.edu/CS546-2014/data/flickr30k.html
+- CC3M/CC12M — Common Crawl–derived image–text pairs. https://github.com/google-research-datasets/conceptual-captions
+- SBU Captions — Image–text pairs from Flickr. http://www.cs.virginia.edu/~vicente/sbucaptions/
+- TextCaps — OCR-centric captioning with text in images. https://textvqa.org/textcaps/
+- VizWiz — Images taken by blind users; accessibility focus. https://vizwiz.org/
+- WebLI (if accessible) — Large-scale multilingual image–text pairs. https://ai.google/discover/papers/webli/
+
+### B. Text–Image Reasoning / VQA / Document QA
+- VQAv2 — Visual Question Answering benchmark. https://visualqa.org/
+- GQA — Compositional reasoning over scenes. https://cs.stanford.edu/people/dorarad/gqa/
+- OK-VQA / A-OKVQA — Requires external knowledge. https://okvqa.allenai.org/
+- ScienceQA — Multimodal science questions with diagrams. https://scienceqa.github.io/
+- DocVQA / TextVQA — Reading text in images. https://textvqa.org/
+- InfographicVQA — VQA on charts/infographics. https://www.microsoft.com/en-us/research/project/infographicvqa/
+- ChartQA / PlotQA / Chart-to-Text — Chart understanding, reasoning. https://github.com/vis-nlp/ChartQA
+
+### C. Text–Table (Structured Data)
+- TabFact — Table fact verification from Wikipedia. https://tabfact.github.io/
+- WikiTableQuestions — Semantic parsing over tables. https://ppasupat.github.io/WikiTableQuestions/
+- ToTTo — Controlled table-to-text generation. https://github.com/google-research-datasets/ToTTo
+- SQA (Sequential QA over tables) — Multi-turn QA on tables. https://allenai.org/data/sqa
+- Spider — Text-to-SQL over multiple DBs (semi-structured). https://yale-lily.github.io/spider
+- TURL — Table understanding pretraining. https://github.com/sunlab-osu/TURL
+- OpenTabQA — Open-domain QA over tables. https://github.com/IBM/OpenTabQA
+- MultiTab / TABBIE resources — Tabular reasoning. https://multitab-project.github.io/
+
+### D. Text–Audio / Speech
+- LibriSpeech — ASR with read English speech. https://www.openslr.org/12
+- Common Voice — Multilingual crowdsourced speech. https://commonvoice.mozilla.org/
+- Librilight — Large-scale unlabeled speech for self-supervised learning. https://github.com/facebookresearch/libri-light
+- TED-LIUM / How2 — Talks with transcripts and multimodal context. https://lium.univ-lemans.fr/ted-lium/
+- AudioSet — Weakly labeled ontology of sounds (with YouTube links). https://research.google.com/audioset/
+- ESC-50 / UrbanSound8K — Environmental sound classification. https://github.com/karoldvl/ESC-50
+- VoxCeleb — Speaker identification/verification. http://www.robots.ox.ac.uk/~vgg/data/voxceleb/
+- SPGISpeech (if license allows) — Financial domain ASR. https://datasets.kensho.com/s/sgpispeech
+
+### E. Full Multimodal / Multi-domain (Text–Image–Table–Audio—and more)
+- MMMU — Massive Multidiscipline Multimodal Understanding benchmark. https://mmmu-benchmark.github.io/
+- MMBench / MME / LVLM-eHub — LVLM comprehensive eval suites. https://mmbench.opencompass.org.cn/
+- Egoschema / Ego4D (video+audio+text) — Egocentric multi-sensor datasets. https://ego4d-data.org/
+- MultiModal C4 (MMC4) — Web-scale multi-doc image–text corpus. https://github.com/allenai/mmc4
+- WebQA / MultimodalQA — QA over web images and text. https://github.com/omni-us/research-multimodalqa
+- Chart/Document suites: DocLayNet, PubLayNet, DocVQA series. https://github.com/ibm-aur-nlp/PubLayNet
+- ArXivDoc / ChartX / SynthChart — Synthetic + real doc/chart sets. https://github.com/vis-nlp/ChartX
+
+### F. Safety, Bias, and Accessibility-focused Sets
+- Hateful Memes — Multimodal bias/toxicity benchmark. https://github.com/facebookresearch/mmf/tree/main/projects/hateful_memes
+- ImageNet-A/O/R — Robustness variants. https://github.com/hendrycks/imagenet-r
+- VizWiz (again) — Accessibility-oriented images/questions. https://vizwiz.org/
+- MS MARCO (multimodal passages via docs) + OCR corpora — Retrieval grounding. https://microsoft.github.io/msmarco/
+
+### G. Licensing and Usage Notes
+- Always check each dataset’s license and terms of use; some require access requests or restrict commercial use.
+- Maintain separate manifests with source, license, checksum, and intended use. Prefer mirrored, deduplicated shards with exact provenance.
+
+---
 
-This project is released under the Apache 2.0 License to encourage open research and collaboration.
-
-## 🙏 Acknowledgments
+## Call for Collaboration: Build UMA with Us
+We’re assembling an open team. If you’re passionate about agentic multimodal AI, join us.
 
-We stand on the shoulders of giants in the AI research community. This project is inspired by countless researchers, engineers, and advocates working to make AI more capable, accessible, and beneficial for all.
+Roles we’re seeking (volunteer or sponsored collaborations):
+- Research Scientists: Multimodal learning, alignment, grounding, evaluation.
+- Research Engineers: Training pipelines, distributed systems, retrieval, tool-use interfaces.
+- Data Scientists / Data Engineers: Dataset curation, cleaning, deduplication, data governance.
+- Domain Experts: Finance, healthcare, education, accessibility, scientific communication.
+- Accessibility Specialists: Inclusive design, alt-text/sonification, screen-reader workflows, disability advocacy.
+- MLOps/Infra: Dataset storage, versioning, scalable training eval infra (HF Datasets, WebDataset, parquet, Arrow).
+- Community & Documentation: Tutorials, examples, benchmark harnesses, governance.
+
+How to get involved now:
+- Open a Discussion with your background and interests: https://huggingface.co/amalsp/Universal-Multimodal-Agent/discussions
+- Propose datasets or contribute manifests via PRs (add to datasets/manifest/*.jsonl)
+- Share domain-specific tasks and evaluation rubrics
+- Star and watch the repo for updates
+
+Initial roadmap for data:
+- Phase 1: Curate public datasets and licenses; build manifests and downloaders
+- Phase 2: Unified preprocessing (image, OCR, tables, audio), deduping, quality filters
+- Phase 3: Balanced training mixtures + eval suites (MMMU/MMBench/DocVQA/ASR)
+
+Ethics & Safety:
+- Respect dataset licenses, privacy, and consent. Implement filter lists and red-teaming sets.
+- Document known biases and limitations; enable opt-out mechanisms where applicable.
+
+Contributors will be acknowledged in the README and future preprint.
 
----
 
-**Note**: This is a placeholder repository representing our vision for Universal-Multimodal-Agent. We're excited about the journey ahead and invite the community to join us in making this vision a reality!
+## Original Project Overview
+[Existing content retained below]
 
-*Last Updated: October 2025*