Today we will try to launch bots again

Although there are many selfish people who have reacted in a negative way, but it's okay, I will continue.

🔹 Title: A Coarse-to-Fine Approach to Multi-Modality 3D Occupancy Grounding

🔹 Publication Date: Published on Aug 2

🔹 Abstract: A benchmark and model for 3D occupancy grounding using natural language and voxel-level annotations improve object perception in autonomous driving. AI-generated summary Visual grounding aims to identify objects or regions in a scene based on natural language denoscriptions, essential for spatially aware perception in autonomous driving. However, existing visual grounding tasks typically depend on bounding boxes that often fail to capture fine-grained details. Not all voxels within a bounding box are occupied, resulting in inaccurate object representations. To address this, we introduce a benchmark for 3D occupancy grounding in challenging outdoor scenes. Built on the nuScenes dataset , it integrates natural language with voxel-level occupancy annotations, offering more precise object perception compared to the traditional grounding task. Moreover, we propose GroundingOcc, an end-to-end model designed for 3D occupancy grounding through multi-modal learning. It combines visual, textual, and point cloud features to predict object location and occupancy information from coarse to fine. Specifically, GroundingOcc comprises a multimodal encoder for feature extraction, an occupancy head for voxel-wise predictions, and a grounding head to refine localization. Additionally, a 2D grounding module and a depth estimation module enhance geometric understanding, thereby boosting model performance. Extensive experiments on the benchmark demonstrate that our method outperforms existing baselines on 3D occupancy grounding . The dataset is available at https://github.com/RONINGOD/GroundingOcc.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.01197

• PDF: https://arxiv.org/pdf/2508.01197

• Github: https://github.com/RONINGOD/GroundingOcc

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🔹 Title: Intern-S1: A Scientific Multimodal Foundation Model

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/intern-s1-a-scientific-multimodal-foundation-model
• PDF: https://arxiv.org/pdf/2508.15763
• Github: https://github.com/InternLM/Intern-S1

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🔹 Title: Mobile-Agent-v3: Foundamental Agents for GUI Automation

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15144
• PDF: https://arxiv.org/pdf/2508.15144
• Project Page: https://github.com/X-PLUG/MobileAgent
• Github: https://github.com/X-PLUG/MobileAgent

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🔹 Title: LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15760
• PDF: https://arxiv.org/pdf/2508.15760

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🔹 Title: Deep Think with Confidence

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2503.00031
• PDF: https://arxiv.org/pdf/2508.15260
• Project Page: https://jiaweizzhao.github.io/deepconf/

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🔹 Title: Waver: Wave Your Way to Lifelike Video Generation

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15761
• PDF: https://arxiv.org/pdf/2508.15761
• Github: https://github.com/FoundationVision/Waver

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🔹 Title: SceneGen: Single-Image 3D Scene Generation in One Feedforward Pass

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15769
• PDF: https://arxiv.org/pdf/2508.15769
• Project Page: https://mengmouxu.github.io/SceneGen/
• Github: https://github.com/Mengmouxu/SceneGen

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🔹 Title: A Survey on Large Language Model Benchmarks

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15361
• PDF: https://arxiv.org/pdf/2508.15361

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🔹 Title: ATLAS: Decoupling Skeletal and Shape Parameters for Expressive Parametric Human Modeling

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15767
• PDF: https://arxiv.org/pdf/2508.15767
• Project Page: https://jindapark.github.io/projects/atlas/

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🔹 Title: aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists

🔹 Publication Date: Published on Aug 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15126
• PDF: https://arxiv.org/pdf/2508.15126

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🔹 Title: Visual Autoregressive Modeling for Instruction-Guided Image Editing

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15772
• PDF: https://arxiv.org/pdf/2508.15772
• Project Page: https://huggingface.co/HiDream-ai/VAREdit
• Github: https://github.com/HiDream-ai/VAREdit

🔹 Datasets citing this paper:
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🔹 Spaces citing this paper:
• https://huggingface.co/spaces/HiDream-ai/VAREdit-8B-1024
• https://huggingface.co/spaces/HiDream-ai/VAREdit-8B-512
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🔹 Title: "Does the cafe entrance look accessible? Where is the door?" Towards Geospatial AI Agents for Visual Inquiries

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15752
• PDF: https://arxiv.org/pdf/2508.15752

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🔹 Title: Snap-Snap: Taking Two Images to Reconstruct 3D Human Gaussians in Milliseconds

🔹 Publication Date: Published on Aug 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.14892
• PDF: https://arxiv.org/pdf/2508.14892
• Github: https://hustvl.github.io/Snap-Snap/

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🔹 Title: When and What: Diffusion-Grounded VideoLLM with Entity Aware Segmentation for Long Video Understanding

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15641
• PDF: https://arxiv.org/pdf/2508.15641

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🔹 Title: LLaSO: A Foundational Framework for Reproducible Research in Large Language and Speech Model

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15418
• PDF: https://arxiv.org/pdf/2508.15418
• Github: https://github.com/EIT-NLP/LLaSO

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/YirongSun/LLaSO-Instruct

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🔹 Title: Fin-PRM: A Domain-Specialized Process Reward Model for Financial Reasoning in Large Language Models

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15202
• PDF: https://arxiv.org/pdf/2508.15202
• Project Page: https://github.com/aliyun/qwen-dianjin

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🔹 Title: INTIMA: A Benchmark for Human-AI Companionship Behavior

🔹 Publication Date: Published on Aug 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09998
• PDF: https://arxiv.org/pdf/2508.09998

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🔹 Title: Investigating Hallucination in Conversations for Low Resource Languages

🔹 Publication Date: Published on Jul 30

🔹 Abstract: LLMs generate fewer hallucinations in Mandarin compared to Hindi and Farsi across multiple models. AI-generated summary Large Language Models ( LLMs ) have demonstrated remarkable proficiency in generating text that closely resemble human writing. However, they often generate factually incorrect statements, a problem typically referred to as ' hallucination '. Addressing hallucination is crucial for enhancing the reliability and effectiveness of LLMs . While much research has focused on hallucination s in English, our study extends this investigation to conversational data in three languages: Hindi, Farsi, and Mandarin. We offer a comprehensive analysis of a dataset to examine both factual and linguistic errors in these languages for GPT-3.5 , GPT-4o , Llama-3.1 , Gemma-2.0 , DeepSeek-R1 and Qwen-3 . We found that LLMs produce very few hallucinated responses in Mandarin but generate a significantly higher number of hallucination s in Hindi and Farsi.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.22720

• PDF: https://arxiv.org/pdf/2507.22720

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🔹 Title: VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo

🔹 Publication Date: Published on Aug 4

🔹 Abstract: A modular training framework accelerates the development of omni-modal LLMs through efficient 3D parallelism and flexible configuration. AI-generated summary Recent advances in large language models (LLMs) have driven impressive progress in omni-modal understanding and generation. However, training omni-modal LLMs remains a significant challenge due to the heterogeneous model architectures required to process diverse modalities, necessitating sophisticated system design for efficient large-scale training. Existing frameworks typically entangle model definition with parallel logic , incurring limited scalability and substantial engineering overhead for end-to-end omni-modal training. % We present \veomni, a modular and efficient training framework to accelerate the development of omni-modal LLMs. \veomni introduces model-centric distributed recipes that decouples communication from computation , enabling efficient 3D parallelism on omni-modal LLMs. \veomni also features a flexible configuration interface supporting seamless integration of new modalities with minimal code change. % Using \veomni, a omni-modal mixture-of-experts (MoE) model with 30B parameters can be trained with over 2,800 tokens/sec/GPU throughput and scale to 160K context lengths via 3D parallelism on 128 GPUs, showcasing its superior efficiency and scalability for training large omni-modal LLMs.

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.02317

• PDF: https://arxiv.org/pdf/2508.02317

• Github: https://github.com/ByteDance-Seed/VeOmni

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🔹 Title: TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated Prefill \& Decode Inference

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15881
• PDF: https://arxiv.org/pdf/2508.15881

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