AI Native Foundation

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…

1. Intrinsic Distribution Matching 🔑 Keywords: Intrinsic Distribution Matching, text-to-image generation, Distribution Matching Distillation, Gaussian moment, LongText-Bench 💡 Category: Generative Models 🌟 Research Objective: - To improve few-step text-to-image generation by incorporating explicit moment constraints into distribution matching distillation, leading to state-of-the-art long-text rendering. 🛠️ Research Methods: - Utilization of Intrinsic Distribution Matching (IDM) that incorporates explicit constraints on Gaussian moments, applied through the generator's exponential-moving-average copy, avoiding the need for an external feature encoder. 💬 Research Conclusions: - IDM significantly enhances long-text rendering accuracy, achieving the highest scores at minimal network evaluations on the Chinese and English LongText-Bench, outperforming existing methods while demonstrating competitive performance with an internal velocity branch against external encoders. 👉 Paper link: huggingface.co/papers/2610.3…

1. Efficient Streaming Audio-Visual Target Speaker Extraction for Real-World Acoustic Scenes 🔑 Keywords: Falcon, RealSSA, audio-visual target speaker extraction, causal AVTSE, multi-scale time-frequency separation 💡 Category: Multi-Modal Learning 🌟 Research Objective: - To develop a lightweight causal audio-visual target speaker extraction method, Falcon, targeting high-quality results with reduced parameters and latency. 🛠️ Research Methods: - Introduction of RealSSA, a realistic benchmark with both simulated and real-scene evaluations. - Falcon method employing multi-scale time-frequency separation in a single encoder-decoder pass and utilizing encoder-space speaker contrastive loss to enhance quality. 💬 Research Conclusions: - Falcon achieves state-of-the-art extraction quality, significantly reducing parameters by 91.9%, computation by 20.2 times, and GPU inference latency by 11.1 times compared to the baseline. 👉 Paper link: huggingface.co/papers/2610.3…

📚 AI Native Daily Paper Digest - 2026-10-29🌟 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. Efficient Streaming Audio-Visual Target Speaker Extraction for Real-World Acoustic Scenes 2. Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success 3. RealCLI: Toward a Unified Evaluation of Human-Agent Collaboration for CLI Agents 4. Amortizing Sequential Experimental Design in Reasoning LLMs 5. CausGround: Causal Interaction Grounding for Adaptive Embodied Planning 6. Instruction Capacity: Behavior and Degradation of Language Models under Controlled Load 7. OmniSpectra: A Unified Foundation Model for Universal Spectra Representation Learning 8. Know, then Draw: A New Learning Paradigm for Visual Generation 9. HuGe: Scaling Humanoid Generalist Vision-Language-Action Models with Egocentric Experience 10. Evaluating DeepSeek V4.1 Flash-1017 for loss of control and harmful manipulation risks 11. LongZero: Enhancing Long-Context Reasoning in LLMs from Zero Data 12. TauIndianBankBench: Procedure-Aware, Action-Oriented Evaluation of Tool-Using Conversational Agents 13. TAO: Tool-Aware Joint Scheduling for LLM-Based Agentic Serving

1. Multiscale Policy Optimization for Long-Horizon LLM Agents 🔑 Keywords: Multiscale Policy Optimization, Long-horizon LLM agents, credit assignment, advantage estimation 💡 Category: Reinforcement Learning 🌟 Research Objective: - The study aims to improve long-horizon agent training by aligning credit assignment and policy updates at the turn scale. 🛠️ Research Methods: - The proposed Multiscale Policy Optimization combines Multiscale Advantage Estimation and turn-level importance sampling to enhance the training process. 💬 Research Conclusions: - Extensive experiments demonstrate consistent gains in performance over strong RL baselines across various tasks including search, tool-use, and automation. 👉 Paper link: huggingface.co/papers/2610.3…

📚 AI Native Daily Paper Digest - 2026-10-28🌟 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. Multiscale Policy Optimization for Long-Horizon LLM Agents 2. The Trinity:Capability-Driven Recursive Self-Improvement for Multimodal Agents 3. PDRVLA: A Vision-Language-Action Model with Physical Dynamics Representations 4. Memory Tax: Dissecting the Net Value of Agent Memory 5. Can Discrete Tokens Serve as Semantically Lossless Representations of Visual Signals? 6. Rollback: Experience Backtracking in World Models for Policy Improvement 7. ThinkWorld: World Imagination Reinforces Chain-of-Dynamics Reasoning 8. Zooming without Zooming v2: From Seeing Details to Connecting Evidence for Generalizable Multimodal Perception 9. MatAnyone-E: Sparse Memory Propagation for Efficient High-Resolution Video Matting 10. h1: A Taste of the Bitter Lesson in Automated Theorem Proving 11. Omni-InteractScope: Benchmarking Proactive and Agentic Interaction in Continuous Omnimodal Streams 12. RynnWorld-Latent: A Cross-Embodiment World Model Conditioned on Universal Latent Actions 13. The Lexical Imprint of Pre-training Data