🔹 Title: From reactive to cognitive: brain-inspired spatial intelligence for embodied agents

🔹 Publication Date: Published on Aug 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.17198
• PDF: https://arxiv.org/pdf/2508.17198
• Github: https://github.com/Heathcliff-saku/BSC-Nav

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🔹 Title: Democracy-in-Silico: Institutional Design as Alignment in AI-Governed Polities

🔹 Publication Date: Published on Aug 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19562
• PDF: https://arxiv.org/pdf/2508.19562

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🔹 Title: Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR

🔹 Publication Date: Published on Sep 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02522
• PDF: https://arxiv.org/pdf/2509.02522
• Github: https://github.com/ritzz-ai/PACS

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🔹 Title: Jointly Reinforcing Diversity and Quality in Language Model Generations

🔹 Publication Date: Published on Sep 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02534
• PDF: https://arxiv.org/pdf/2509.02534

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🔹 Title: POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion

🔹 Publication Date: Published on Sep 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01215
• PDF: https://arxiv.org/pdf/2509.01215
• Github: https://github.com/Tencent/POINTS-Reader

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🔹 Title: MedDINOv3: How to adapt vision foundation models for medical image segmentation?

🔹 Publication Date: Published on Sep 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02379
• PDF: https://arxiv.org/pdf/2509.02379
• Github: https://github.com/ricklisz/MedDINOv3

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🔹 Title: M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision

🔹 Publication Date: Published on Sep 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01360
• PDF: https://arxiv.org/pdf/2509.01360

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🔹 Title: Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views

🔹 Publication Date: Published on Sep 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.01250
• PDF: https://arxiv.org/pdf/2509.01250
• Github: https://github.com/aHapBean/Point-PQAE

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🔹 Title: Universal Deep Research: Bring Your Own Model and Strategy

🔹 Publication Date: Published on Aug 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00244
• PDF: https://arxiv.org/pdf/2509.00244

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🔹 Title: LLaVA-Critic-R1: Your Critic Model is Secretly a Strong Policy Model

🔹 Publication Date: Published on Aug 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.00676
• PDF: https://arxiv.org/pdf/2509.00676
• Github: https://github.com/LLaVA-VL/LLaVA-NeXT/tree/main/llava-critic-r1

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🔹 Title: The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

🔹 Publication Date: Published on Sep 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02547
• PDF: https://arxiv.org/pdf/2509.02547
• Github: https://github.com/xhyumiracle/Awesome-AgenticLLM-RL-Papers

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🔹 Title: UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning

🔹 Publication Date: Published on Sep 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02544
• PDF: https://arxiv.org/pdf/2509.02544

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🔹 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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🔹 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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🔹 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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🔹 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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🔹 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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🔹 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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🔹 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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🔹 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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