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AI Native Foundation is proud to co-sign the “Open Weights and American AI Leadership” open letter, joining a broad coalition across the AI ecosystem alongside global technology leaders such as @Microsoft, @nvidia, @Google, @OpenAI, @Meta, and @SpaceX, as well as @linuxfoundation, @mozilla, and many other companies and organizations.
As of July 30, 2026, more than 230 companies and organizations have signed the open letter. This milestone reflects broad momentum for expanding access to AI innovation and ensuring its benefits reach every sector of the economy.
We fully support the letter’s case: open-weight models broaden access to the AI economy, strengthen competition, give organizations greater control over their AI stack, and enable the independent scrutiny that underpins AI safety and security.
These principles are central to our mission. We stand against the concentration of AI capabilities and advocate for equal access and opportunity across the AI landscape. A healthy open-weight ecosystem helps make that future possible.
Learn more and view the full list of signatories:
microsoft.com/en-us/corporat…
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13. OmniXRef: A Unified Framework for Multimodal Multi-Reference Video Editing
🔑 Keywords: Multimodal, Video Editing, OmniXRef, AI Native, Curriculum Learning
💡 Category: Multi-Modal Learning
🌟 Research Objective:
- The paper aims to introduce OmniXRef, a unified framework for multimodal multi-reference video editing, allowing users to control diverse video attributes using various combinations of text, image, and video references.
🛠️ Research Methods:
- The researchers developed a unified input design with a reference-bank interface and introduced R^2-RoPE for generalization to more references. They utilized a single-stream diffusion transformer to process source video and multimodal context in an interleaved sequence and employed curriculum learning to increase task complexity and resolution progressively.
💬 Research Conclusions:
- The OmniXRef-Model demonstrates superior performance in video-reference attribute editing when compared to task-specific baselines and commercial models, as evaluated by the OmniXRef-Bench across multiple parameters such as Camera, Motion, Content, Style, and Visual Effects (VFX).
👉 Paper link: huggingface.co/papers/2610.2…
📚 AI Native Daily Paper Digest - 2026-10-19🌟
Follow @AINativeF for the latest insights on AI Native.
Covering the AI research papers from Hugging Face listed below.
💡 Stay updated with the latest research trends and dive deep into the future of AI! 🚀
#AI #HuggingFace #AIPaper #AINative #AINF
— Appendix: Today's AI research papers —
1. HexaShell: A Thin Harness for Exploring the Limits of VLM Agents as Robot Coding Agents
2. BizBench: Unveiling Hidden Challenges in Deploying Long-Horizon Agents for Real-World Business
3. Into the Unknown: Benchmarking AI agents on open scientific research problems
4. Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents
5. LEV: Thinking Fast and Deep with Looped Decision Models
6. OpenMobile-2: Building Versatile Mobile Agents with Scalable Environments and App-Native Tools
7. BridgeWAM: Bridging Video and Action Experts via Latent Bridge Queries
8. Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning
9. RAP: A Dataset for Paper-to-Poster Generation via Declarative Markup from Human-Crafted Exemplars
10. DLite: History-free Speculative Decoding for Agentic AI
11. OfficeTown: Generative Organizations for Evolving Agent Intelligence
12. MOSS-Image Technical Report
13. OmniXRef: A Unified Framework for Multimodal Multi-Reference Video Editing
11. OfficeTown: Generative Organizations for Evolving Agent Intelligence
🔑 Keywords: Generative Simulation Framework, AI Agents, Assistant Agents, Work-Grounded Data, Supervised Fine-tuning
💡 Category: AI Systems and Tools
🌟 Research Objective:
- The research introduces OfficeTown, a generative simulation framework that creates professional organizations aimed at generating work-grounded training data and evaluation benchmarks for AI agents.
🛠️ Research Methods:
- OfficeTown initializes organizations from natural-language descriptions, coordinating tasks and reviews within simulated professional environments. Implementations include OfficeTown Bench with tasks and evaluation criteria derived from expert feedback across various domains.
- Techniques such as supervised fine-tuning and on-policy distillation are applied to improve AI models using OfficeTown data.
💬 Research Conclusions:
- OfficeTown's framework demonstrates significant potential in enhancing generalist agents by providing structured environments for learning and evaluation. The use of organizational simulation shows valuable improvements in AI agent performance over traditional baselines.
👉 Paper link: huggingface.co/papers/2610.2…
12. MOSS-Image Technical Report
🔑 Keywords: MOSS-Image, Open Diffusion Transformer, Text-to-Image, Layout Coherence, Intention-aware
💡 Category: Generative Models
🌟 Research Objective:
- The main objective is to develop MOSS-Image, an open diffusion transformer that achieves high-fidelity image synthesis with intent-aware prompt enhancement, particularly excelling in text-rich and layout-coherent image generation.
🛠️ Research Methods:
- The approach involved pretraining a 5B parameter single-stream diffusion transformer from scratch, incorporating bilingual long-form text rendering and precise layout control.
- An intention-aware agentic prompt enhancement module was created to translate user requests into detailed content and layout, enhancing the model's output.
💬 Research Conclusions:
- MOSS-Image demonstrates state-of-the-art performance for open-weight models on benchmarks like LongLongTextBench, achieving impressive text coverage.
- The model ranked highly in the RealLayoutArena evaluation, proving its efficacy in generating complex layouts.
- The authors will release resources to support ongoing research in the text-to-image domain.
👉 Paper link: huggingface.co/papers/2610.2…
🌟 Today’s Global AI Native Industry Insights include:
1. Meta Releases Muse Spark Open, a Large-Scale Open-Weight AI Model for Coding and Agentic Tasks
2. OpenAI Upgrades GPT-6 Astra Pro in ChatGPT With Parallel Multi-Agent Task Handling
3. Perplexity Computer Adds Reverse-Engineering Skill That Rebuilds Apps Without Source Code
🔍 Dive into the in-depth insights in the thread below. Here’s what’s shaping the future of AI: 👇
Perplexity Computer Adds Reverse-Engineering Skill That Rebuilds Apps Without Source Code
Perplexity's Computer product has added REA (Reverse Engineer Anything), a skill created by developer morluto, as a built-in capability. Users can ask Computer to inspect how an existing app works without access to its source code and have it build a functioning version. Tasks invoking the skill via /reverse-engineer are free through October 31, with a cap of 10,000 credits per user. As a demonstration, Perplexity released five apps built with the skill, including a Figma clone featuring layers, auto-layout, sign-in, cross-device sync, and multiplayer editing.
Read more: xitter.cf/i/web/status/211112066…
@AskPerplexity
🎥 Credit: @AskPerplexity on X
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