🔹 Title: Phased DMD: Few-step Distribution Matching Distillation via Score Matching within Subintervals

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27684
• PDF: https://arxiv.org/pdf/2510.27684

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🔹 Title: Visual Backdoor Attacks on MLLM Embodied Decision Making via Contrastive Trigger Learning

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27623
• PDF: https://arxiv.org/pdf/2510.27623
• Project Page: https://zqs1943.github.io/BEAT/

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🔹 Title: Dual-Stream Diffusion for World-Model Augmented Vision-Language-Action Model

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27607
• PDF: https://arxiv.org/pdf/2510.27607

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🔹 Title: HyperClick: Advancing Reliable GUI Grounding via Uncertainty Calibration

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27266
• PDF: https://arxiv.org/pdf/2510.27266

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🔹 Title: The Denario project: Deep knowledge AI agents for scientific discovery

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26887
• PDF: https://arxiv.org/pdf/2510.26887
• Github: https://github.com/AstroPilot-AI/Denario

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🔹 Title: INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

🔹 Publication Date: Published on Oct 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.25602
• PDF: https://arxiv.org/pdf/2510.25602
• Github: https://github.com/ChenMnZ/INT_vs_FP

🔹 Datasets citing this paper:
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🔹 Title: OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows

🔹 Publication Date: Published on Oct 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.24411
• PDF: https://arxiv.org/pdf/2510.24411
• Github: https://github.com/OS-Copilot/OS-Sentinel

🔹 Datasets citing this paper:
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🔹 Title: Defeating the Training-Inference Mismatch via FP16

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26788
• PDF: https://arxiv.org/pdf/2510.26788
• Github: https://github.com/sail-sg/Precision-RL

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🔹 Title: Higher-order Linear Attention

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27258
• PDF: https://arxiv.org/pdf/2510.27258
• Project Page: https://yifanzhang-pro.github.io/HLA
• Github: https://github.com/yifanzhang-pro/HLA

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🔹 Title: Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27606
• PDF: https://arxiv.org/pdf/2510.27606
• Github: https://github.com/InternLM/Spatial-SSRL

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🔹 Title: π_RL: Online RL Fine-tuning for Flow-based Vision-Language-Action Models

🔹 Publication Date: Published on Oct 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.25889
• PDF: https://arxiv.org/pdf/2510.25889
• Project Page: https://rlinf.readthedocs.io/en/latest/rst_source/examples/pi0.html
• Github: https://github.com/RLinf/RLinf

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🔹 Title: Mask-to-Height: A YOLOv11-Based Architecture for Joint Building Instance Segmentation and Height Classification from Satellite Imagery

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27224
• PDF: https://arxiv.org/pdf/2510.27224

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🔹 Title: Agent Lightning: Train ANY AI Agents with Reinforcement Learning

📝 Summary:
Agent Lightning is a flexible RL framework for training LLMs in any AI agent. It uniquely decouples agent execution from training, allowing seamless integration with diverse existing agents with minimal code changes. This enables robust training for complex interactions and shows stable performan...

🔹 Publication Date: Published on Aug 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.03680
• PDF: https://arxiv.org/pdf/2508.03680
• Project Page: https://www.microsoft.com/en-us/research/project/agent-lightning/
• Github: https://github.com/microsoft/agent-lightning

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🔹 Title: Kimi Linear: An Expressive, Efficient Attention Architecture

📝 Summary:
Kimi Linear is a new hybrid linear attention architecture that, for the first time, outperforms full attention across various contexts. It achieves superior performance and efficiency, reducing KV cache and increasing decoding throughput, making it a powerful drop-in replacement.

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26692
• PDF: https://arxiv.org/pdf/2510.26692
• Github: https://github.com/MoonshotAI/Kimi-Linear

🔹 Models citing this paper:
• https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct
• https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Base
• https://huggingface.co/aiqtech/Kimi-Linear-48B-A3B-Instruct

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🔹 Title: Emu3.5: Native Multimodal Models are World Learners

📝 Summary:
Emu3.5 is a multimodal world model natively predicting vision and language states. Trained on vast video data, it uses Discrete Diffusion Adaptation for 20x faster image inference. It excels at multimodal generation, world modeling, and performs competitively.

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26583
• PDF: https://arxiv.org/pdf/2510.26583
• Project Page: https://emu.world/
• Github: https://github.com/baaivision/Emu3.5

🔹 Models citing this paper:
• https://huggingface.co/BAAI/Emu3.5
• https://huggingface.co/BAAI/Emu3.5-Image
• https://huggingface.co/BAAI/Emu3.5-VisionTokenizer

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🔹 Title: olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models

📝 Summary:
olmOCR is an open-source toolkit using a fine-tuned vision language model to convert diverse PDFs into clean, structured plain text. It preserves formatting like tables and equations, and is optimized for cost-effective large-scale batch processing, unlocking tokens for language model training.

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.18443
• PDF: https://arxiv.org/pdf/2502.18443
• Github: https://github.com/allenai/olmocr

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/davanstrien/test-olmocr2
• https://huggingface.co/datasets/davanstrien/newspapers-olmocr2
• https://huggingface.co/datasets/stckmn/ocr-output-Directive017-1761355297

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🔹 Title: PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model

📝 Summary:
PaddleOCR-VL is a compact 0.9B vision-language model for multilingual document parsing. It achieves state-of-the-art performance on 109 languages with minimal resources and fast inference. It efficiently recognizes complex elements like text and tables.

🔹 Publication Date: Published on Oct 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.14528
• PDF: https://arxiv.org/pdf/2510.14528
• Github: https://github.com/PaddlePaddle/PaddleOCR

🔹 Models citing this paper:
• https://huggingface.co/PaddlePaddle/PaddleOCR-VL
• https://huggingface.co/PaddlePaddle/PP-DocLayoutV2
• https://huggingface.co/lvyufeng/PaddleOCR-VL-0.9B

🔹 Spaces citing this paper:
• https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL_Online_Demo
• https://huggingface.co/spaces/markobinario/PaddleOCR-VL_Online_Demo
• https://huggingface.co/spaces/waytoAGI/PaddleOCR-VL_Online_Demo

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🔹 Title: Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations

📝 Summary:
Concerto combines 3D self-distillation and 2D-3D joint embedding to learn superior spatial features. It significantly outperforms existing self-supervised models and achieves new state-of-the-art results in scene understanding and open-world perception.

🔹 Publication Date: Published on Oct 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.23607
• PDF: https://arxiv.org/pdf/2510.23607
• Project Page: https://pointcept.github.io/Concerto/
• Github: https://github.com/Pointcept/Pointcept

🔹 Models citing this paper:
• https://huggingface.co/Pointcept/Concerto

🔹 Spaces citing this paper:
• https://huggingface.co/spaces/Pointcept/Concerto

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