🔹 Title: Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation
🔹 Publication Date: Published on Sep 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02040
• PDF: https://arxiv.org/pdf/2509.02040
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🔹 Publication Date: Published on Sep 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02040
• PDF: https://arxiv.org/pdf/2509.02040
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🔹 Title: OpenVision 2: A Family of Generative Pretrained Visual Encoders for Multimodal Learning
🔹 Publication Date: Published on Sep 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01644
• PDF: https://arxiv.org/pdf/2509.01644
• Project Page: https://ucsc-vlaa.github.io/OpenVision2/
• Github: https://ucsc-vlaa.github.io/OpenVision2
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🔹 Publication Date: Published on Sep 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01644
• PDF: https://arxiv.org/pdf/2509.01644
• Project Page: https://ucsc-vlaa.github.io/OpenVision2/
• Github: https://ucsc-vlaa.github.io/OpenVision2
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🔹 Title: Kwai Keye-VL 1.5 Technical Report
🔹 Publication Date: Published on Sep 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01563
• PDF: https://arxiv.org/pdf/2509.01563
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🔹 Publication Date: Published on Sep 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01563
• PDF: https://arxiv.org/pdf/2509.01563
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🔹 Title: Discrete Noise Inversion for Next-scale Autoregressive Text-based Image Editing
🔹 Publication Date: Published on Sep 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01984
• PDF: https://arxiv.org/pdf/2509.01984
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🔹 Publication Date: Published on Sep 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01984
• PDF: https://arxiv.org/pdf/2509.01984
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🔹 Title: Improving Large Vision and Language Models by Learning from a Panel of Peers
🔹 Publication Date: Published on Sep 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01610
• PDF: https://arxiv.org/pdf/2509.01610
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🔹 Publication Date: Published on Sep 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01610
• PDF: https://arxiv.org/pdf/2509.01610
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❤1
🔹 Title: VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use
🔹 Publication Date: Published on Sep 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01055
• PDF: https://arxiv.org/pdf/2509.01055
• Github: https://github.com/TIGER-AI-Lab/verl-tool
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🔹 Publication Date: Published on Sep 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01055
• PDF: https://arxiv.org/pdf/2509.01055
• Github: https://github.com/TIGER-AI-Lab/verl-tool
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🔹 Title: C-DiffDet+: Fusing Global Scene Context with Generative Denoising for High-Fidelity Object Detection
🔹 Publication Date: Published on Aug 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00578
• PDF: https://arxiv.org/pdf/2509.00578
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🔹 Publication Date: Published on Aug 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00578
• PDF: https://arxiv.org/pdf/2509.00578
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🔹 Title: Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis
🔹 Publication Date: Published on Jul 31
🔹 Abstract: A novel framework uses a Direct 4DMesh-to-GS Variation Field VAE and Gaussian Variation Field diffusion model to generate high-quality dynamic 3D content from single video inputs, demonstrating superior quality and generalization. AI-generated summary In this paper, we present a novel framework for video-to-4D generation that creates high-quality dynamic 3D content from single video inputs. Direct 4D diffusion modeling is extremely challenging due to costly data construction and the high-dimensional nature of jointly representing 3D shape, appearance, and motion. We address these challenges by introducing a Direct 4DMesh-to-GS Variation Field VAE that directly encodes canonical Gaussian Splats (GS) and their temporal variations from 3D animation data without per-instance fitting, and compresses high-dimensional animations into a compact latent space. Building upon this efficient representation, we train a Gaussian Variation Field diffusion model with temporal-aware Diffusion Transformer conditioned on input videos and canonical GS. Trained on carefully-curated animatable 3D objects from the Objaverse dataset , our model demonstrates superior generation quality compared to existing methods. It also exhibits remarkable generalization to in-the-wild video inputs despite being trained exclusively on synthetic data, paving the way for generating high-quality animated 3D content. Project page: https://gvfdiffusion.github.io/.
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.23785
• PDF: https://arxiv.org/pdf/2507.23785
• Project Page: https://gvfdiffusion.github.io/
• Github: https://github.com/ForeverFancy/gvfdiffusion
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🔹 Publication Date: Published on Jul 31
🔹 Abstract: A novel framework uses a Direct 4DMesh-to-GS Variation Field VAE and Gaussian Variation Field diffusion model to generate high-quality dynamic 3D content from single video inputs, demonstrating superior quality and generalization. AI-generated summary In this paper, we present a novel framework for video-to-4D generation that creates high-quality dynamic 3D content from single video inputs. Direct 4D diffusion modeling is extremely challenging due to costly data construction and the high-dimensional nature of jointly representing 3D shape, appearance, and motion. We address these challenges by introducing a Direct 4DMesh-to-GS Variation Field VAE that directly encodes canonical Gaussian Splats (GS) and their temporal variations from 3D animation data without per-instance fitting, and compresses high-dimensional animations into a compact latent space. Building upon this efficient representation, we train a Gaussian Variation Field diffusion model with temporal-aware Diffusion Transformer conditioned on input videos and canonical GS. Trained on carefully-curated animatable 3D objects from the Objaverse dataset , our model demonstrates superior generation quality compared to existing methods. It also exhibits remarkable generalization to in-the-wild video inputs despite being trained exclusively on synthetic data, paving the way for generating high-quality animated 3D content. Project page: https://gvfdiffusion.github.io/.
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.23785
• PDF: https://arxiv.org/pdf/2507.23785
• Project Page: https://gvfdiffusion.github.io/
• Github: https://github.com/ForeverFancy/gvfdiffusion
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❤1
🔹 Title: Baichuan-M2: Scaling Medical Capability with Large Verifier System
🔹 Publication Date: Published on Sep 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02208
• PDF: https://arxiv.org/pdf/2509.02208
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🔹 Publication Date: Published on Sep 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02208
• PDF: https://arxiv.org/pdf/2509.02208
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🔹 Title: GenCompositor: Generative Video Compositing with Diffusion Transformer
🔹 Publication Date: Published on Sep 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02460
• PDF: https://arxiv.org/pdf/2509.02460
• Project Page: https://gencompositor.github.io/
• Github: https://github.com/TencentARC/GenCompositor
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🔹 Publication Date: Published on Sep 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02460
• PDF: https://arxiv.org/pdf/2509.02460
• Project Page: https://gencompositor.github.io/
• Github: https://github.com/TencentARC/GenCompositor
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🔹 Title: AMBEDKAR-A Multi-level Bias Elimination through a Decoding Approach with Knowledge Augmentation for Robust Constitutional Alignment of Language Models
🔹 Publication Date: Published on Sep 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02133
• PDF: https://arxiv.org/pdf/2509.02133
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🔹 Publication Date: Published on Sep 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02133
• PDF: https://arxiv.org/pdf/2509.02133
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🔹 Title: Reasoning Vectors: Transferring Chain-of-Thought Capabilities via Task Arithmetic
🔹 Publication Date: Published on Sep 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01363
• PDF: https://arxiv.org/pdf/2509.01363
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🔹 Publication Date: Published on Sep 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01363
• PDF: https://arxiv.org/pdf/2509.01363
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🔹 Title: SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction
🔹 Publication Date: Published on Aug 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00581
• PDF: https://arxiv.org/pdf/2509.00581
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🔹 Publication Date: Published on Aug 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00581
• PDF: https://arxiv.org/pdf/2509.00581
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🔹 Title: Metis: Training Large Language Models with Advanced Low-Bit Quantization
🔹 Publication Date: Published on Aug 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00404
• PDF: https://arxiv.org/pdf/2509.00404
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🔹 Publication Date: Published on Aug 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00404
• PDF: https://arxiv.org/pdf/2509.00404
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🔹 Title: Fantastic Pretraining Optimizers and Where to Find Them
🔹 Publication Date: Published on Sep 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02046
• PDF: https://arxiv.org/pdf/2509.02046
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🔹 Publication Date: Published on Sep 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02046
• PDF: https://arxiv.org/pdf/2509.02046
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❤1
🔹 Title: Benchmarking Optimizers for Large Language Model Pretraining
🔹 Publication Date: Published on Sep 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01440
• PDF: https://arxiv.org/pdf/2509.01440
• Github: https://github.com/epfml/llm-optimizer-benchmark
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🔹 Publication Date: Published on Sep 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01440
• PDF: https://arxiv.org/pdf/2509.01440
• Github: https://github.com/epfml/llm-optimizer-benchmark
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🔹 Title: The Gold Medals in an Empty Room: Diagnosing Metalinguistic Reasoning in LLMs with Camlang
🔹 Publication Date: Published on Aug 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00425
• PDF: https://arxiv.org/pdf/2509.00425
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🔹 Publication Date: Published on Aug 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00425
• PDF: https://arxiv.org/pdf/2509.00425
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🔹 Title: MobiAgent: A Systematic Framework for Customizable Mobile Agents
🔹 Publication Date: Published on Aug 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00531
• PDF: https://arxiv.org/pdf/2509.00531
• Github: https://github.com/IPADS-SAI/MobiAgent/releases/download/v1.0/Mobiagent.apk
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🔹 Publication Date: Published on Aug 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00531
• PDF: https://arxiv.org/pdf/2509.00531
• Github: https://github.com/IPADS-SAI/MobiAgent/releases/download/v1.0/Mobiagent.apk
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🔹 Title: ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association
🔹 Publication Date: Published on Sep 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01584
• PDF: https://arxiv.org/pdf/2509.01584
• Project Page: https://ganlinzhang.xyz/vista-slam/
• Github: https://github.com/zhangganlin/vista-slam
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🔹 Publication Date: Published on Sep 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01584
• PDF: https://arxiv.org/pdf/2509.01584
• Project Page: https://ganlinzhang.xyz/vista-slam/
• Github: https://github.com/zhangganlin/vista-slam
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🔹 Title: DCPO: Dynamic Clipping Policy Optimization
🔹 Publication Date: Published on Sep 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02333
• PDF: https://arxiv.org/pdf/2509.02333
• Github: https://github.com/lime-RL/DCPO
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🔹 Publication Date: Published on Sep 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02333
• PDF: https://arxiv.org/pdf/2509.02333
• Github: https://github.com/lime-RL/DCPO
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