✨DeepSeek-OCR: Contexts Optical Compression
📝 Summary:
DeepSeek-OCR uses optical 2D mapping to compress long contexts, achieving high OCR precision with reduced vision tokens and demonstrating practical value in document processing. AI-generated summary W...
🔹 Publication Date: Published on Oct 21
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/deepseek-ocr-contexts-optical-compression
• PDF: https://arxiv.org/pdf/2510.18234
• Github: https://github.com/deepseek-ai/DeepSeek-OCR
🔹 Models citing this paper:
• https://huggingface.co/deepseek-ai/DeepSeek-OCR
• https://huggingface.co/unsloth/DeepSeek-OCR
• https://huggingface.co/Jalea96/DeepSeek-OCR-bnb-4bit-NF4
✨ Spaces citing this paper:
• https://huggingface.co/spaces/merterbak/DeepSeek-OCR-Demo
• https://huggingface.co/spaces/khang119966/DeepSeek-OCR-DEMO
• https://huggingface.co/spaces/prithivMLmods/Super-OCRs-Demo
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
DeepSeek-OCR uses optical 2D mapping to compress long contexts, achieving high OCR precision with reduced vision tokens and demonstrating practical value in document processing. AI-generated summary W...
🔹 Publication Date: Published on Oct 21
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/deepseek-ocr-contexts-optical-compression
• PDF: https://arxiv.org/pdf/2510.18234
• Github: https://github.com/deepseek-ai/DeepSeek-OCR
🔹 Models citing this paper:
• https://huggingface.co/deepseek-ai/DeepSeek-OCR
• https://huggingface.co/unsloth/DeepSeek-OCR
• https://huggingface.co/Jalea96/DeepSeek-OCR-bnb-4bit-NF4
✨ Spaces citing this paper:
• https://huggingface.co/spaces/merterbak/DeepSeek-OCR-Demo
• https://huggingface.co/spaces/khang119966/DeepSeek-OCR-DEMO
• https://huggingface.co/spaces/prithivMLmods/Super-OCRs-Demo
==================================
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Arxivexplained
DeepSeek-OCR: Contexts Optical Compression - Explained Simply
By Haoran Wei, Yaofeng Sun, Yukun Li. # DeepSeek-OCR: A Game-Changer for Processing Text-Heavy Documents
**The Problem:** Current AI syst...
**The Problem:** Current AI syst...
✨Multi-module GRPO: Composing Policy Gradients and Prompt Optimization for Language Model Programs
📝 Summary:
mmGRPO, a multi-module extension of GRPO, enhances accuracy in modular AI systems by optimizing LM calls and prompts across various tasks. AI-generated summary Group Relative Policy Optimization ( GRP...
🔹 Publication Date: Published on Aug 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.04660
• PDF: https://arxiv.org/pdf/2508.04660
• Project Page: https://dspy.ai
• Github: https://github.com/stanfordnlp/dspy
==================================
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📝 Summary:
mmGRPO, a multi-module extension of GRPO, enhances accuracy in modular AI systems by optimizing LM calls and prompts across various tasks. AI-generated summary Group Relative Policy Optimization ( GRP...
🔹 Publication Date: Published on Aug 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.04660
• PDF: https://arxiv.org/pdf/2508.04660
• Project Page: https://dspy.ai
• Github: https://github.com/stanfordnlp/dspy
==================================
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❤1
✨Directional Textual Inversion for Personalized Text-to-Image Generation
📝 Summary:
Directional Textual Inversion DTI enhances text-to-image personalization by fixing learned token magnitudes and optimizing only their direction. This prevents norm inflation issues of standard Textual Inversion, improving prompt conditioning and enabling smooth interpolation. DTI offers better te...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13672
• PDF: https://arxiv.org/pdf/2512.13672
• Project Page: https://kunheek.github.io/dti
• Github: https://github.com/kunheek/dti
==================================
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#TextualInversion #TextToImage #GenerativeAI #DeepLearning #AI
📝 Summary:
Directional Textual Inversion DTI enhances text-to-image personalization by fixing learned token magnitudes and optimizing only their direction. This prevents norm inflation issues of standard Textual Inversion, improving prompt conditioning and enabling smooth interpolation. DTI offers better te...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13672
• PDF: https://arxiv.org/pdf/2512.13672
• Project Page: https://kunheek.github.io/dti
• Github: https://github.com/kunheek/dti
==================================
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#TextualInversion #TextToImage #GenerativeAI #DeepLearning #AI
✨One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer
📝 Summary:
One-to-All Animation is a unified framework for high-fidelity character animation and image pose transfer. It tackles misaligned and partially visible references using self-supervised outpainting, a robust reference extractor, and identity-robust pose control to outperform existing methods.
🔹 Publication Date: Published on Nov 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22940
• PDF: https://arxiv.org/pdf/2511.22940
• Project Page: https://ssj9596.github.io/one-to-all-animation-project/
• Github: https://github.com/ssj9596/One-to-All-Animation
🔹 Models citing this paper:
• https://huggingface.co/MochunniaN1/One-to-All-14b
• https://huggingface.co/MochunniaN1/One-to-All-1.3b_2
• https://huggingface.co/MochunniaN1/One-to-All-1.3b_1
✨ Datasets citing this paper:
• https://huggingface.co/datasets/MochunniaN1/One-to-All-sub
==================================
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#CharacterAnimation #PoseTransfer #ComputerVision #AI #DeepLearning
📝 Summary:
One-to-All Animation is a unified framework for high-fidelity character animation and image pose transfer. It tackles misaligned and partially visible references using self-supervised outpainting, a robust reference extractor, and identity-robust pose control to outperform existing methods.
🔹 Publication Date: Published on Nov 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22940
• PDF: https://arxiv.org/pdf/2511.22940
• Project Page: https://ssj9596.github.io/one-to-all-animation-project/
• Github: https://github.com/ssj9596/One-to-All-Animation
🔹 Models citing this paper:
• https://huggingface.co/MochunniaN1/One-to-All-14b
• https://huggingface.co/MochunniaN1/One-to-All-1.3b_2
• https://huggingface.co/MochunniaN1/One-to-All-1.3b_1
✨ Datasets citing this paper:
• https://huggingface.co/datasets/MochunniaN1/One-to-All-sub
==================================
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#CharacterAnimation #PoseTransfer #ComputerVision #AI #DeepLearning
arXiv.org
One-to-All Animation: Alignment-Free Character Animation and Image...
Recent advances in diffusion models have greatly improved pose-driven character animation. However, existing methods are limited to spatially aligned reference-pose pairs with matched skeletal...
✨What matters for Representation Alignment: Global Information or Spatial Structure?
📝 Summary:
Representation alignment enhances generative training by transferring spatial structure from pretrained vision encoders to diffusion models, surpassing the importance of global semantic performance. A...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10794
• PDF: https://arxiv.org/pdf/2512.10794
• Project Page: https://end2end-diffusion.github.io/irepa
• Github: https://github.com/end2end-diffusion/irepa
==================================
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📝 Summary:
Representation alignment enhances generative training by transferring spatial structure from pretrained vision encoders to diffusion models, surpassing the importance of global semantic performance. A...
🔹 Publication Date: Published on Dec 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10794
• PDF: https://arxiv.org/pdf/2512.10794
• Project Page: https://end2end-diffusion.github.io/irepa
• Github: https://github.com/end2end-diffusion/irepa
==================================
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✨DrivePI: Spatial-aware 4D MLLM for Unified Autonomous Driving Understanding, Perception, Prediction and Planning
📝 Summary:
DrivePI is a new spatial-aware 4D MLLM for autonomous driving, unifying understanding, 3D perception, prediction, and planning. It integrates point clouds, images, and language instructions, achieving state-of-the-art performance by outperforming existing VLA and specialized VA models.
🔹 Publication Date: Published on Dec 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12799
• PDF: https://arxiv.org/pdf/2512.12799
• Github: https://github.com/happinesslz/DrivePI
==================================
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#AutonomousDriving #MLLM #ComputerVision #DeepLearning #AI
📝 Summary:
DrivePI is a new spatial-aware 4D MLLM for autonomous driving, unifying understanding, 3D perception, prediction, and planning. It integrates point clouds, images, and language instructions, achieving state-of-the-art performance by outperforming existing VLA and specialized VA models.
🔹 Publication Date: Published on Dec 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12799
• PDF: https://arxiv.org/pdf/2512.12799
• Github: https://github.com/happinesslz/DrivePI
==================================
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#AutonomousDriving #MLLM #ComputerVision #DeepLearning #AI
✨Towards Scalable Pre-training of Visual Tokenizers for Generation
📝 Summary:
Traditional visual tokenizer training fails to improve generation quality with more compute. VTP is a new framework that jointly optimizes image-text contrastive, self-supervised, and reconstruction losses. This enables better scaling, faster convergence, and significantly improved generative per...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13687
• PDF: https://arxiv.org/pdf/2512.13687
• Github: https://github.com/hustvl
🔹 Models citing this paper:
• https://huggingface.co/MiniMaxAI/VTP-Base-f16d64
• https://huggingface.co/MiniMaxAI/VTP-Small-f16d64
• https://huggingface.co/MiniMaxAI/VTP-Large-f16d64
==================================
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📝 Summary:
Traditional visual tokenizer training fails to improve generation quality with more compute. VTP is a new framework that jointly optimizes image-text contrastive, self-supervised, and reconstruction losses. This enables better scaling, faster convergence, and significantly improved generative per...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13687
• PDF: https://arxiv.org/pdf/2512.13687
• Github: https://github.com/hustvl
🔹 Models citing this paper:
• https://huggingface.co/MiniMaxAI/VTP-Base-f16d64
• https://huggingface.co/MiniMaxAI/VTP-Small-f16d64
• https://huggingface.co/MiniMaxAI/VTP-Large-f16d64
==================================
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✨Learning Robot Manipulation from Audio World Models
📝 Summary:
A generative latent flow matching model is proposed to predict future audio for robotic manipulation tasks, improving performance over methods without future lookahead by accurately capturing intrinsi...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08405
• PDF: https://arxiv.org/pdf/2512.08405
==================================
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📝 Summary:
A generative latent flow matching model is proposed to predict future audio for robotic manipulation tasks, improving performance over methods without future lookahead by accurately capturing intrinsi...
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08405
• PDF: https://arxiv.org/pdf/2512.08405
==================================
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❤1
✨WebOperator: Action-Aware Tree Search for Autonomous Agents in Web Environment
📝 Summary:
WebOperator is a tree-search framework that enhances web agents with reliable backtracking and strategic exploration. It addresses challenges like irreversible actions and partial observability by using a safety-aware search and verifying paths. WebOperator achieves state-of-the-art results on We...
🔹 Publication Date: Published on Dec 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12692
• PDF: https://arxiv.org/pdf/2512.12692
• Project Page: https://kagnlp.github.io/WebOperator
• Github: https://kagnlp.github.io/WebOperator
==================================
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#WebAgents #TreeSearch #AI #AutonomousAgents #MachineLearning
📝 Summary:
WebOperator is a tree-search framework that enhances web agents with reliable backtracking and strategic exploration. It addresses challenges like irreversible actions and partial observability by using a safety-aware search and verifying paths. WebOperator achieves state-of-the-art results on We...
🔹 Publication Date: Published on Dec 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12692
• PDF: https://arxiv.org/pdf/2512.12692
• Project Page: https://kagnlp.github.io/WebOperator
• Github: https://kagnlp.github.io/WebOperator
==================================
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#WebAgents #TreeSearch #AI #AutonomousAgents #MachineLearning
✨Towards Visual Re-Identification of Fish using Fine-Grained Classification for Electronic Monitoring in Fisheries
📝 Summary:
A deep learning pipeline was optimized for automated fish re-identification in electronic monitoring systems. Using the Swin-T architecture and AutoFish dataset, it achieved 90.43% Rank-1 accuracy, with intra-species viewpoint differences being the main challenge.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08400
• PDF: https://arxiv.org/pdf/2512.08400
• Github: https://github.com/msamdk/Fish_Re_Identification.git
==================================
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#FishReID #DeepLearning #ComputerVision #FisheriesTech #FineGrainedClassification
📝 Summary:
A deep learning pipeline was optimized for automated fish re-identification in electronic monitoring systems. Using the Swin-T architecture and AutoFish dataset, it achieved 90.43% Rank-1 accuracy, with intra-species viewpoint differences being the main challenge.
🔹 Publication Date: Published on Dec 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08400
• PDF: https://arxiv.org/pdf/2512.08400
• Github: https://github.com/msamdk/Fish_Re_Identification.git
==================================
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#FishReID #DeepLearning #ComputerVision #FisheriesTech #FineGrainedClassification
✨Efficient Memory Management for Large Language Model Serving with PagedAttention
📝 Summary:
PagedAttention algorithm and vLLM system enhance the throughput of large language models by efficiently managing memory and reducing waste in the key-value cache. AI-generated summary High throughput ...
🔹 Publication Date: Published on Sep 12, 2023
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2309.06180
• PDF: https://arxiv.org/pdf/2309.06180
• Github: https://github.com/vllm-project/vllm
🔹 Models citing this paper:
• https://huggingface.co/theonlyengine/Flash-attention1
✨ Datasets citing this paper:
• https://huggingface.co/datasets/TheBlueScrubs/TheBlueScrubs-v1
==================================
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📝 Summary:
PagedAttention algorithm and vLLM system enhance the throughput of large language models by efficiently managing memory and reducing waste in the key-value cache. AI-generated summary High throughput ...
🔹 Publication Date: Published on Sep 12, 2023
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2309.06180
• PDF: https://arxiv.org/pdf/2309.06180
• Github: https://github.com/vllm-project/vllm
🔹 Models citing this paper:
• https://huggingface.co/theonlyengine/Flash-attention1
✨ Datasets citing this paper:
• https://huggingface.co/datasets/TheBlueScrubs/TheBlueScrubs-v1
==================================
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✨Very Large-Scale Multi-Agent Simulation in AgentScope
📝 Summary:
Enhancements to the AgentScope platform improve scalability, efficiency, and ease of use for large-scale multi-agent simulations through distributed mechanisms, flexible environments, and user-friendl...
🔹 Publication Date: Published on Jul 25, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2407.17789
• PDF: https://arxiv.org/pdf/2407.17789
• Github: https://github.com/modelscope/agentscope
==================================
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📝 Summary:
Enhancements to the AgentScope platform improve scalability, efficiency, and ease of use for large-scale multi-agent simulations through distributed mechanisms, flexible environments, and user-friendl...
🔹 Publication Date: Published on Jul 25, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2407.17789
• PDF: https://arxiv.org/pdf/2407.17789
• Github: https://github.com/modelscope/agentscope
==================================
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✨Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
📝 Summary:
The Qwen2-VL Series uses Naive Dynamic Resolution and Multimodal Rotary Position Embedding to enhance visual processing and achieves competitive performance on multimodal benchmarks. AI-generated summ...
🔹 Publication Date: Published on Sep 18, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2409.12191
• PDF: https://arxiv.org/pdf/2409.12191
• Github: https://github.com/QwenLM/Qwen2-VL
🔹 Models citing this paper:
• https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct
• https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct
• https://huggingface.co/Qwen/QVQ-72B-Preview
✨ Spaces citing this paper:
• https://huggingface.co/spaces/prithivMLmods/Qwen-Image-Edit-2509-LoRAs-Fast
• https://huggingface.co/spaces/linoyts/Qwen-Image-Edit-Angles
• https://huggingface.co/spaces/tori29umai/Qwen-Image-2509-MultipleAngles
==================================
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📝 Summary:
The Qwen2-VL Series uses Naive Dynamic Resolution and Multimodal Rotary Position Embedding to enhance visual processing and achieves competitive performance on multimodal benchmarks. AI-generated summ...
🔹 Publication Date: Published on Sep 18, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2409.12191
• PDF: https://arxiv.org/pdf/2409.12191
• Github: https://github.com/QwenLM/Qwen2-VL
🔹 Models citing this paper:
• https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct
• https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct
• https://huggingface.co/Qwen/QVQ-72B-Preview
✨ Spaces citing this paper:
• https://huggingface.co/spaces/prithivMLmods/Qwen-Image-Edit-2509-LoRAs-Fast
• https://huggingface.co/spaces/linoyts/Qwen-Image-Edit-Angles
• https://huggingface.co/spaces/tori29umai/Qwen-Image-2509-MultipleAngles
==================================
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arXiv.org
Qwen2-VL: Enhancing Vision-Language Model's Perception of the...
We present the Qwen2-VL Series, an advanced upgrade of the previous Qwen-VL models that redefines the conventional predetermined-resolution approach in visual processing. Qwen2-VL introduces the...
✨Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration
📝 Summary:
Mobile-Agent-v2, a multi-agent system with planning, decision, and reflection components, improves task completion in mobile device operations by addressing navigation challenges and handling errors. ...
🔹 Publication Date: Published on Jun 3, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2406.01014
• PDF: https://arxiv.org/pdf/2406.01014
• Github: https://github.com/x-plug/mobileagent
✨ Spaces citing this paper:
• https://huggingface.co/spaces/junyangwang0410/Mobile-Agent
==================================
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📝 Summary:
Mobile-Agent-v2, a multi-agent system with planning, decision, and reflection components, improves task completion in mobile device operations by addressing navigation challenges and handling errors. ...
🔹 Publication Date: Published on Jun 3, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2406.01014
• PDF: https://arxiv.org/pdf/2406.01014
• Github: https://github.com/x-plug/mobileagent
✨ Spaces citing this paper:
• https://huggingface.co/spaces/junyangwang0410/Mobile-Agent
==================================
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✨Agent S: An Open Agentic Framework that Uses Computers Like a Human
📝 Summary:
Agent S is an open agentic framework enabling autonomous GUI interaction to automate complex tasks. It employs experience-augmented hierarchical planning and an Agent-Computer Interface with MLLMs for enhanced reasoning. Agent S achieves state-of-the-art performance on OSWorld and demonstrates br...
🔹 Publication Date: Published on Oct 10, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2410.08164
• PDF: https://arxiv.org/pdf/2410.08164
• Github: https://huggingface.co/collections/ranpox/awesome-computer-use-agents
==================================
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#AgenticAI #MultimodalAI #HumanComputerInteraction #Automation #AIResearch
📝 Summary:
Agent S is an open agentic framework enabling autonomous GUI interaction to automate complex tasks. It employs experience-augmented hierarchical planning and an Agent-Computer Interface with MLLMs for enhanced reasoning. Agent S achieves state-of-the-art performance on OSWorld and demonstrates br...
🔹 Publication Date: Published on Oct 10, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2410.08164
• PDF: https://arxiv.org/pdf/2410.08164
• Github: https://huggingface.co/collections/ranpox/awesome-computer-use-agents
==================================
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#AgenticAI #MultimodalAI #HumanComputerInteraction #Automation #AIResearch
✨Mamba: Linear-Time Sequence Modeling with Selective State Spaces
📝 Summary:
Mamba, a novel SSM-based model, outperforms Transformers in inference speed and scalability across various modalities by selectively propagating information and using efficient hardware-aware algorith...
🔹 Publication Date: Published on Dec 1, 2023
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/mamba-linear-time-sequence-modeling-with-selective-state-spaces
• PDF: https://arxiv.org/pdf/2312.00752
• Github: https://github.com/state-spaces/mamba
🔹 Models citing this paper:
• https://huggingface.co/tiiuae/falcon-mamba-7b
• https://huggingface.co/state-spaces/mamba-2.8b-slimpj
• https://huggingface.co/tiiuae/falcon-mamba-7b-instruct
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Sherirto/BD4UI
✨ Spaces citing this paper:
• https://huggingface.co/spaces/openfree/LLM_Quantization
• https://huggingface.co/spaces/FallnAI/Quantize-HF-Models
• https://huggingface.co/spaces/seawolf2357/LLM_Quantization
==================================
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📝 Summary:
Mamba, a novel SSM-based model, outperforms Transformers in inference speed and scalability across various modalities by selectively propagating information and using efficient hardware-aware algorith...
🔹 Publication Date: Published on Dec 1, 2023
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/mamba-linear-time-sequence-modeling-with-selective-state-spaces
• PDF: https://arxiv.org/pdf/2312.00752
• Github: https://github.com/state-spaces/mamba
🔹 Models citing this paper:
• https://huggingface.co/tiiuae/falcon-mamba-7b
• https://huggingface.co/state-spaces/mamba-2.8b-slimpj
• https://huggingface.co/tiiuae/falcon-mamba-7b-instruct
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Sherirto/BD4UI
✨ Spaces citing this paper:
• https://huggingface.co/spaces/openfree/LLM_Quantization
• https://huggingface.co/spaces/FallnAI/Quantize-HF-Models
• https://huggingface.co/spaces/seawolf2357/LLM_Quantization
==================================
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Arxivexplained
Mamba: Linear-Time Sequence Modeling with Selective State Spaces - Explained Simply
By Albert Gu, Tri Dao. # Mamba: The AI Architecture That Could Replace Transformers
**The Problem:** Today's most powerful...
**The Problem:** Today's most powerful...
✨AI-Trader: Benchmarking Autonomous Agents in Real-Time Financial Markets
📝 Summary:
AI-Trader evaluates the performance of large language models in real-world financial markets, highlighting their limitations in trading and risk management. AI-generated summary Large Language Models ...
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10971
• PDF: https://arxiv.org/pdf/2512.10971
• Project Page: https://ai4trade.ai/
• Github: https://github.com/HKUDS/AI-Trader
==================================
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📝 Summary:
AI-Trader evaluates the performance of large language models in real-world financial markets, highlighting their limitations in trading and risk management. AI-generated summary Large Language Models ...
🔹 Publication Date: Published on Dec 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10971
• PDF: https://arxiv.org/pdf/2512.10971
• Project Page: https://ai4trade.ai/
• Github: https://github.com/HKUDS/AI-Trader
==================================
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✨Inferring Compositional 4D Scenes without Ever Seeing One
📝 Summary:
COM4D infers 4D/3D object structure and spatio-temporal configuration from 2D video. It avoids 4D compositional training data by disentangling spatial and temporal attention learning. This purely data-driven method achieves state-of-the-art results in 4D object and composed 3D reconstruction.
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05272
• PDF: https://arxiv.org/pdf/2512.05272
• Project Page: https://berkegokmen1.github.io/com4d/
• Github: https://github.com/insait-institute/COM4D
==================================
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✓ https://news.1rj.ru/str/DataScienceT
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📝 Summary:
COM4D infers 4D/3D object structure and spatio-temporal configuration from 2D video. It avoids 4D compositional training data by disentangling spatial and temporal attention learning. This purely data-driven method achieves state-of-the-art results in 4D object and composed 3D reconstruction.
🔹 Publication Date: Published on Dec 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05272
• PDF: https://arxiv.org/pdf/2512.05272
• Project Page: https://berkegokmen1.github.io/com4d/
• Github: https://github.com/insait-institute/COM4D
==================================
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