📚 AI Native Daily Paper Digest - 2026-07-14🌟

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Covering AI research papers from Hugging Face, featured in the image.

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— Appendix: Today's AI research papers —

1. Weak-to-Strong Generalization via Direct On-Policy Distillation

2. ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory

3. LightMem-Ego: Your AI Memory for Everyday Life

4. Metacognition in LLMs: Foundations, Progress, and Opportunities

5. Proxy Exploration and Reusable Guidance: A Modular LLM Post-Training Paradigm via Proxy-Guided Update Signals

6. NeuroCogMap Reveals Cognitive Organization of Large Language Models

7. CtrlVTON: Controllable Virtual Try-On via Visual-Instance-Prompt Segmentation

8. Motion4Motion: Motion Transfer Across Subjects at Inference

9. LATO.2: Factorized 3D Mesh Generation with Vertex and Topology Flow

10. A Theory of Contrastive Learning with Natural Images

11. Evidence-Backed Video Question Answering

12. Xiaomi-Robotics-U0: Unified Embodied Synthesis with World Foundation Model

13. Latent-Identity Tuning in Text-to-Image Personalization Models 1. Weak-to-Strong Generalization via Direct On-Policy Distillation

🔑 Keywords: Direct On-Policy Distillation, Reinforcement Learning, policy shift, implicit reward

💡 Category: Reinforcement Learning

🌟 Research Objective:
- The main goal is to efficiently transfer reinforcement learning improvements from smaller models to larger models without rerunning expensive RL processes.

🛠️ Research Methods:
- Introduction of Direct On-Policy Distillation, which uses the policy shift-induced reward signal from a smaller model to enhance a stronger target model's performance.

💬 Research Conclusions:
- Direct On-Policy Distillation consistently improves stronger models by leveraging signals from weaker teacher models, significantly enhancing performance and efficiency.
- Notably, it increases Qwen3-1.7B performance on AIME 2024 from 48.3% to 58.3% in just 4 hours using 8 A100 GPUs.

👉 Paper link: huggingface.co/papers/2607.05…