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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…
📚 AI Native Daily Paper Digest - 2026-10-23🌟
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. WeVisOne-VL: Unified Visual Perception from Objects to Pixels
2. Deep Search in Real-World Web Environments
3. IQuest-Q1: Advancing Foundation Capabilities for Agentic CLI Systems
4. Deep Fusion Memory: A Chunk-Level Parametric Memory for Large Language Models
5. LaLa-World: Scaling Latent World Models with Natural Language Actions
6. Self-Distilling Future Video Representations for World Action Models
7. Heartbeat Thinking: Speculative Reasoning and Tool Execution While Listening in Realtime Voice Agents
8. Beyond Watching and Searching: Benchmarking Agents for Video-centered Deep Research Reports
9. RLSplat: Resource-Linear Budget Control for Feed-forward 3D Gaussian Splatting
10. Cola2: Co-Adaptive Hierarchical Language Modeling with Continuous Semantic Compression and Balanced Discrete Generation
11. Rethinking Sequence Compression with Test-time Regression
12. RTAformer: Relation-Transition-Aware Transformer for Video Scene Graph Generation
13. MolLean: A Process-Verifiable Language for Chemical Reasoning
2. Deep Search in Real-World Web Environments
🔑 Keywords: AI Native, OpenDS-World, autonomous deep search, tool coordination, human evaluation
💡 Category: Robotics and Autonomous Systems
🌟 Research Objective:
- Introduce OpenDS-World as a benchmark to evaluate autonomous multi-step deep search agents in real-world web environments.
🛠️ Research Methods:
- Evaluate agents by having them perform multi-step deep searches, requiring exploration of diverse information sources, and validate using 200 challenging tasks with human verification.
💬 Research Conclusions:
- Identified substantial performance gaps between frontier models and human performance, with the best model achieving 39.0% accuracy compared to human's 21.1%.
- Highlighted challenges in tool coordination and long-horizon search management affecting research strategies.
👉 Paper link: huggingface.co/papers/2610.3…
1. WeVisOne-VL: Unified Visual Perception from Objects to Pixels
🔑 Keywords: WeVisOne-VL, vision-language models, pixel decoder, curriculum SFT, mixed-task RL
💡 Category: Computer Vision
🌟 Research Objective:
- To present WeVisOne-VL, a comprehensive vision-language model that unifies object-level and pixel-level visual perception.
🛠️ Research Methods:
- Integration of a lightweight pixel decoder via a special <OBJ> token and three-stage curriculum SFT followed by mixed-task RL.
💬 Research Conclusions:
- WeVisOne-VL demonstrates leading performance across over 30 benchmarks, supporting various visual tasks while robustly rejecting object-absent queries.
👉 Paper link: huggingface.co/papers/2610.3…
🌟 Today’s Global AI Native Industry Insights include:
1. OpenAI AI Models Help Discover Critical PHP Security Flaws
2. Anthropic Launches Research Preview of Claude Duet, a Proactive AI Agent App
3. OpenAI's GPT-Rosalind AI agents identify potential drug target for neurodegenerative disease
🔍 Dive into the in-depth insights in the thread below. Here’s what’s shaping the future of AI: 👇
OpenAI's GPT-Rosalind AI agents identify potential drug target for neurodegenerative disease
OpenAI's GPT-Rosalind research system identified a new lead for drug discovery related to Perry syndrome, a rare neurodegenerative disorder linked to the DCTN1 gene. Working with Perturb Labs and Scripps Research, thousands of GPT-Rosalind agents analyzed gene-expression data from millions of mouse brain cells alongside scientific literature in just 32 minutes. The analysis suggested that disrupting DCTN1 may impair cholesterol transport to neurons, offering a testable mechanism for neurodegeneration. The collaboration is now expanding to identify additional drug targets.
Read more: openai.com/index/scaling-ai-…
@OpenAI
🎥 Credit: The original article
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📚 AI Native Daily Paper Digest - 2026-10-22🌟
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. StreamEval: Evaluating Interactive Streaming Video Generation Models
2. DONIC: Generalist Dexterous Bimanual Tracking Control from Human Demonstrations
3. Looking Ahead: Modeling Future Evolution in Autoregressive Video Generation
4. Agent World Model in the Wild: Computer-Use Agent Environment Synthesis
5. Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
6. Socratic Skill Learning: Interleaving Teaching and Practice for Transferable Agent Skills
7. AgenticRoboRL: Bringing Coding-Agent Post-Training to Agentic Robotics
8. TraceForge: Distilling with Your Own Thinking
9. Multimodal Embodied Agents: A Unified Capability-centric Perspective from Computer-Use to Robot-Use
10. DeLoop: Repeated Actions as Self-Supervision for Long-Horizon Agents
11. CulturalRiddles: A Multicultural Benchmark of Riddles
12. Real In, Real Out: What If We Only Use Real Data for Scene Text Editing?
13. Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?
13. Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?
🔑 Keywords: scientific ML benchmarks, frontier coding agents, ML-engineering tricks, domain insight, Agent4Sci
💡 Category: Machine Learning
🌟 Research Objective:
- To evaluate the performance of frontier coding agents on scientific ML benchmarks and determine their reliance on engineering tricks versus domain insight.
🛠️ Research Methods:
- Introduction of Agent4Sci, a long-horizon testbed covering 20 benchmarks across 11 domains, to assess the performance and limitations of current frontier agents.
💬 Research Conclusions:
- Frontier agents advance published models on all benchmarks through engineering tricks rather than substantial research insights; while plugin skills can enhance performance significantly.
- This study identifies key limitations, including novelty miscalibration, resource under-utilization, and a tendency towards local engineering sweeps.
👉 Paper link: huggingface.co/papers/2610.2…
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