✨RoboVIP: Multi-View Video Generation with Visual Identity Prompting Augments Robot Manipulation
📝 Summary:
Collecting diverse robot manipulation data is challenging. This paper introduces visual identity prompting, using exemplar images to guide diffusion models for generating multi-view, temporally coherent data. This augmented data improves robot policy performance in both simulation and real-world ...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05241
• PDF: https://arxiv.org/pdf/2601.05241
• Project Page: https://robovip.github.io/RoboVIP/
• Github: https://robovip.github.io/RoboVIP/
==================================
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#Robotics #AI #GenerativeAI #ComputerVision #MachineLearning
📝 Summary:
Collecting diverse robot manipulation data is challenging. This paper introduces visual identity prompting, using exemplar images to guide diffusion models for generating multi-view, temporally coherent data. This augmented data improves robot policy performance in both simulation and real-world ...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05241
• PDF: https://arxiv.org/pdf/2601.05241
• Project Page: https://robovip.github.io/RoboVIP/
• Github: https://robovip.github.io/RoboVIP/
==================================
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#Robotics #AI #GenerativeAI #ComputerVision #MachineLearning
✨AT^2PO: Agentic Turn-based Policy Optimization via Tree Search
📝 Summary:
AT^2PO is a framework for multi-turn agentic reinforcement learning. It uses a turn-level tree search with entropy-guided expansion and turn-wise credit assignment. This improves exploration, reward propagation, and policy optimization, achieving state-of-the-art results.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04767
• PDF: https://arxiv.org/pdf/2601.04767
• Github: https://github.com/zzfoutofspace/ATPO
==================================
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#ReinforcementLearning #AgenticAI #TreeSearch #PolicyOptimization #ArtificialIntelligence
📝 Summary:
AT^2PO is a framework for multi-turn agentic reinforcement learning. It uses a turn-level tree search with entropy-guided expansion and turn-wise credit assignment. This improves exploration, reward propagation, and policy optimization, achieving state-of-the-art results.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04767
• PDF: https://arxiv.org/pdf/2601.04767
• Github: https://github.com/zzfoutofspace/ATPO
==================================
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#ReinforcementLearning #AgenticAI #TreeSearch #PolicyOptimization #ArtificialIntelligence
✨Few Tokens Matter: Entropy Guided Attacks on Vision-Language Models
📝 Summary:
Attacking a few high-entropy tokens in VLMs significantly degrades outputs with reduced budgets. These selective attacks efficiently create harmful outputs and transfer across architectures, exposing new VLM safety weaknesses.
🔹 Publication Date: Published on Dec 26, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21815
• PDF: https://arxiv.org/pdf/2512.21815
==================================
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#VLMs #AISafety #AdversarialAI #MachineLearning #AIResearch
📝 Summary:
Attacking a few high-entropy tokens in VLMs significantly degrades outputs with reduced budgets. These selective attacks efficiently create harmful outputs and transfer across architectures, exposing new VLM safety weaknesses.
🔹 Publication Date: Published on Dec 26, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21815
• PDF: https://arxiv.org/pdf/2512.21815
==================================
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#VLMs #AISafety #AdversarialAI #MachineLearning #AIResearch
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✨VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control
📝 Summary:
VerseCrafter is a 4D video world model enabling unified control over camera and object dynamics. It uses a novel 4D Geometric Control representation with 3D Gaussian trajectories for high-fidelity video generation. An automatic data engine addresses training data scarcity.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05138
• PDF: https://arxiv.org/pdf/2601.05138
• Github: https://sixiaozheng.github.io/VerseCrafter_page/
🔹 Models citing this paper:
• https://huggingface.co/TencentARC/VerseCrafter
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
VerseCrafter is a 4D video world model enabling unified control over camera and object dynamics. It uses a novel 4D Geometric Control representation with 3D Gaussian trajectories for high-fidelity video generation. An automatic data engine addresses training data scarcity.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05138
• PDF: https://arxiv.org/pdf/2601.05138
• Github: https://sixiaozheng.github.io/VerseCrafter_page/
🔹 Models citing this paper:
• https://huggingface.co/TencentARC/VerseCrafter
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Agent-as-a-Judge
📝 Summary:
Large language models face limitations in evaluating complex, multi-step tasks, prompting the development of agent-based evaluation systems that utilize planning, tool-augmented verification, and mult...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05111
• PDF: https://arxiv.org/pdf/2601.05111
• Github: https://github.com/ModalityDance/Awesome-Agent-as-a-Judge
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Large language models face limitations in evaluating complex, multi-step tasks, prompting the development of agent-based evaluation systems that utilize planning, tool-augmented verification, and mult...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05111
• PDF: https://arxiv.org/pdf/2601.05111
• Github: https://github.com/ModalityDance/Awesome-Agent-as-a-Judge
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨The Illusion of Specialization: Unveiling the Domain-Invariant "Standing Committee" in Mixture-of-Experts Models
📝 Summary:
Mixture of Experts models exhibit a Standing Committee of experts that consistently dominates routing across domains, challenging the assumption of widespread specialization. This reveals a strong structural bias toward centralized computation, limiting effective specialization.
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03425
• PDF: https://arxiv.org/pdf/2601.03425
==================================
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#MixtureOfExperts #DeepLearning #MachineLearning #AISpecialization #NeuralNetworks
📝 Summary:
Mixture of Experts models exhibit a Standing Committee of experts that consistently dominates routing across domains, challenging the assumption of widespread specialization. This reveals a strong structural bias toward centralized computation, limiting effective specialization.
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03425
• PDF: https://arxiv.org/pdf/2601.03425
==================================
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#MixtureOfExperts #DeepLearning #MachineLearning #AISpecialization #NeuralNetworks
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✨Plenoptic Video Generation
📝 Summary:
PlenopticDreamer addresses multi-view video re-rendering inconsistency by synchronizing generative hallucinations. It uses an autoregressive model with camera-guided retrieval to ensure spatio-temporal coherence, achieving state-of-the-art results with high fidelity.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05239
• PDF: https://arxiv.org/pdf/2601.05239
• Project Page: https://research.nvidia.com/labs/dir/plenopticdreamer/
==================================
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#PlenopticVideo #GenerativeAI #VideoGeneration #ComputerVision #DeepLearning
📝 Summary:
PlenopticDreamer addresses multi-view video re-rendering inconsistency by synchronizing generative hallucinations. It uses an autoregressive model with camera-guided retrieval to ensure spatio-temporal coherence, achieving state-of-the-art results with high fidelity.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05239
• PDF: https://arxiv.org/pdf/2601.05239
• Project Page: https://research.nvidia.com/labs/dir/plenopticdreamer/
==================================
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#PlenopticVideo #GenerativeAI #VideoGeneration #ComputerVision #DeepLearning
✨CoV: Chain-of-View Prompting for Spatial Reasoning
📝 Summary:
Chain-of-View CoV prompting helps vision-language models improve spatial reasoning in 3D embodied question answering. It actively selects question-aligned views and iteratively adjusts camera positions to gather context, significantly boosting performance without additional training.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05172
• PDF: https://arxiv.org/pdf/2601.05172
• Github: https://github.com/ziplab/CoV
==================================
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#SpatialReasoning #VisionLanguageModels #PromptEngineering #EmbodiedAI #AIResearch
📝 Summary:
Chain-of-View CoV prompting helps vision-language models improve spatial reasoning in 3D embodied question answering. It actively selects question-aligned views and iteratively adjusts camera positions to gather context, significantly boosting performance without additional training.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05172
• PDF: https://arxiv.org/pdf/2601.05172
• Github: https://github.com/ziplab/CoV
==================================
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#SpatialReasoning #VisionLanguageModels #PromptEngineering #EmbodiedAI #AIResearch
✨DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs
📝 Summary:
DiffCoT reformulates chain-of-thought reasoning as an iterative denoising process using diffusion principles, enabling unified generation and correction of intermediate steps while maintaining causal ...
🔹 Publication Date: Published on Jan 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03559
• PDF: https://arxiv.org/pdf/2601.03559
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
DiffCoT reformulates chain-of-thought reasoning as an iterative denoising process using diffusion principles, enabling unified generation and correction of intermediate steps while maintaining causal ...
🔹 Publication Date: Published on Jan 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03559
• PDF: https://arxiv.org/pdf/2601.03559
==================================
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✨Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing
📝 Summary:
Re-Align addresses the gap between understanding and generation in in-context image generation and editing through structured reasoning-guided alignment and reinforcement learning training. AI-generat...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05124
• PDF: https://arxiv.org/pdf/2601.05124
• Project Page: https://hrz2000.github.io/realign/
• Github: https://github.com/hrz2000/realign
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Re-Align addresses the gap between understanding and generation in in-context image generation and editing through structured reasoning-guided alignment and reinforcement learning training. AI-generat...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05124
• PDF: https://arxiv.org/pdf/2601.05124
• Project Page: https://hrz2000.github.io/realign/
• Github: https://github.com/hrz2000/realign
==================================
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✨One Sample to Rule Them All: Extreme Data Efficiency in RL Scaling
📝 Summary:
This paper introduces polymath learning, demonstrating that a single, carefully designed training sample can significantly boost language model reasoning across multiple scientific disciplines. This sample engineering approach outperforms training with larger datasets, emphasizing quality over qu...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03111
• PDF: https://arxiv.org/pdf/2601.03111
==================================
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#AI #MachineLearning #LLM #DataEfficiency #SampleEngineering
📝 Summary:
This paper introduces polymath learning, demonstrating that a single, carefully designed training sample can significantly boost language model reasoning across multiple scientific disciplines. This sample engineering approach outperforms training with larger datasets, emphasizing quality over qu...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03111
• PDF: https://arxiv.org/pdf/2601.03111
==================================
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#AI #MachineLearning #LLM #DataEfficiency #SampleEngineering
✨DocDancer: Towards Agentic Document-Grounded Information Seeking
📝 Summary:
DocDancer is an end-to-end trained open-source document question answering agent that formulates the task as an information-seeking problem and uses a tool-driven framework with exploration and synthe...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05163
• PDF: https://arxiv.org/pdf/2601.05163
==================================
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📝 Summary:
DocDancer is an end-to-end trained open-source document question answering agent that formulates the task as an information-seeking problem and uses a tool-driven framework with exploration and synthe...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05163
• PDF: https://arxiv.org/pdf/2601.05163
==================================
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✨Multi-Scale Local Speculative Decoding for Image Generation
📝 Summary:
Multi-Scale Local Speculative Decoding accelerates autoregressive image generation through multi-resolution drafting and spatially informed verification while maintaining semantic quality and perceptu...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05149
• PDF: https://arxiv.org/pdf/2601.05149
• Project Page: https://qualcomm-ai-research.github.io/mulo-sd-webpage/
• Github: https://qualcomm-ai-research.github.io/mulo-sd-webpage
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Multi-Scale Local Speculative Decoding accelerates autoregressive image generation through multi-resolution drafting and spatially informed verification while maintaining semantic quality and perceptu...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05149
• PDF: https://arxiv.org/pdf/2601.05149
• Project Page: https://qualcomm-ai-research.github.io/mulo-sd-webpage/
• Github: https://qualcomm-ai-research.github.io/mulo-sd-webpage
==================================
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✨PyramidalWan: On Making Pretrained Video Model Pyramidal for Efficient Inference
📝 Summary:
Pyramidal diffusion models reduce computational cost through hierarchical resolution processing, with pretrained models converted via low-cost fine-tuning maintaining output quality while enabling eff...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04792
• PDF: https://arxiv.org/pdf/2601.04792
• Project Page: https://qualcomm-ai-research.github.io/PyramidalWan
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Pyramidal diffusion models reduce computational cost through hierarchical resolution processing, with pretrained models converted via low-cost fine-tuning maintaining output quality while enabling eff...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04792
• PDF: https://arxiv.org/pdf/2601.04792
• Project Page: https://qualcomm-ai-research.github.io/PyramidalWan
==================================
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✨ProFuse: Efficient Cross-View Context Fusion for Open-Vocabulary 3D Gaussian Splatting
📝 Summary:
ProFuse enhances 3D scene understanding by integrating semantic information into 3D Gaussian Splatting through efficient context-aware processing and pre-registration phases. AI-generated summary We p...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04754
• PDF: https://arxiv.org/pdf/2601.04754
• Project Page: https://chiou1203.github.io/ProFuse/
• Github: https://chiou1203.github.io/ProFuse/
==================================
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📝 Summary:
ProFuse enhances 3D scene understanding by integrating semantic information into 3D Gaussian Splatting through efficient context-aware processing and pre-registration phases. AI-generated summary We p...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04754
• PDF: https://arxiv.org/pdf/2601.04754
• Project Page: https://chiou1203.github.io/ProFuse/
• Github: https://chiou1203.github.io/ProFuse/
==================================
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✨Scaling Behavior Cloning Improves Causal Reasoning: An Open Model for Real-Time Video Game Playing
📝 Summary:
Behavior cloning demonstrates improved performance and causal reasoning through scaling model size and training data, achieving human-level gameplay in 3D video games. AI-generated summary Behavior cl...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04575
• PDF: https://arxiv.org/pdf/2601.04575
• Project Page: https://elefant-ai.github.io/open-p2p/
• Github: https://github.com/elefant-ai/open-p2p
🔹 Models citing this paper:
• https://huggingface.co/elefantai/open-p2p
✨ Datasets citing this paper:
• https://huggingface.co/datasets/elefantai/p2p-full-data
• https://huggingface.co/datasets/elefantai/p2p-toy-examples
==================================
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📝 Summary:
Behavior cloning demonstrates improved performance and causal reasoning through scaling model size and training data, achieving human-level gameplay in 3D video games. AI-generated summary Behavior cl...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04575
• PDF: https://arxiv.org/pdf/2601.04575
• Project Page: https://elefant-ai.github.io/open-p2p/
• Github: https://github.com/elefant-ai/open-p2p
🔹 Models citing this paper:
• https://huggingface.co/elefantai/open-p2p
✨ Datasets citing this paper:
• https://huggingface.co/datasets/elefantai/p2p-full-data
• https://huggingface.co/datasets/elefantai/p2p-toy-examples
==================================
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✨ReHyAt: Recurrent Hybrid Attention for Video Diffusion Transformers
📝 Summary:
ReHyAt presents a recurrent hybrid attention mechanism, merging softmax fidelity with linear efficiency. This enables scalable, high-quality video generation by reducing computational cost from quadratic to linear, with significantly lower training costs.
🔹 Publication Date: Published on Jan 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04342
• PDF: https://arxiv.org/pdf/2601.04342
• Project Page: https://qualcomm-ai-research.github.io/rehyat
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
ReHyAt presents a recurrent hybrid attention mechanism, merging softmax fidelity with linear efficiency. This enables scalable, high-quality video generation by reducing computational cost from quadratic to linear, with significantly lower training costs.
🔹 Publication Date: Published on Jan 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04342
• PDF: https://arxiv.org/pdf/2601.04342
• Project Page: https://qualcomm-ai-research.github.io/rehyat
==================================
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✨Guardians of the Hair: Rescuing Soft Boundaries in Depth, Stereo, and Novel Views
📝 Summary:
HairGuard is a framework for recovering fine-grained soft boundary details in 3D vision tasks through specialized depth refinement and view synthesis techniques. AI-generated summary Soft boundaries, ...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03362
• PDF: https://arxiv.org/pdf/2601.03362
==================================
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📝 Summary:
HairGuard is a framework for recovering fine-grained soft boundary details in 3D vision tasks through specialized depth refinement and view synthesis techniques. AI-generated summary Soft boundaries, ...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03362
• PDF: https://arxiv.org/pdf/2601.03362
==================================
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✨Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset
📝 Summary:
A large-scale industrial multimodal defect dataset with 1 million image-text pairs enables efficient foundation model adaptation for manufacturing quality inspection and generation tasks. AI-generated...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24160
• PDF: https://arxiv.org/pdf/2512.24160
• Project Page: https://ninaneon.github.io/projectpage/
• Github: https://github.com/NinaNeon/IMDD-1M-Towards-Open-Vocabulary-Industrial-Defect-
==================================
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📝 Summary:
A large-scale industrial multimodal defect dataset with 1 million image-text pairs enables efficient foundation model adaptation for manufacturing quality inspection and generation tasks. AI-generated...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24160
• PDF: https://arxiv.org/pdf/2512.24160
• Project Page: https://ninaneon.github.io/projectpage/
• Github: https://github.com/NinaNeon/IMDD-1M-Towards-Open-Vocabulary-Industrial-Defect-
==================================
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✨Memorization in 3D Shape Generation: An Empirical Study
📝 Summary:
Researchers develop a framework to measure memorization in 3D generative models and identify factors affecting it, finding that data modality and model design parameters influence how much training da...
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23628
• PDF: https://arxiv.org/pdf/2512.23628
• Github: https://github.com/zlab-princeton/3d-gen-mem
🔹 Models citing this paper:
• https://huggingface.co/pudashi/3DGenMem
==================================
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📝 Summary:
Researchers develop a framework to measure memorization in 3D generative models and identify factors affecting it, finding that data modality and model design parameters influence how much training da...
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23628
• PDF: https://arxiv.org/pdf/2512.23628
• Github: https://github.com/zlab-princeton/3d-gen-mem
🔹 Models citing this paper:
• https://huggingface.co/pudashi/3DGenMem
==================================
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✨AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering
📝 Summary:
AgentDevel presents a release engineering approach for large language model agents that treats them as shippable artifacts and emphasizes stable, auditable improvements through externalized testing an...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04620
• PDF: https://arxiv.org/pdf/2601.04620
• Project Page: https://trotsky1997.github.io/agentdevel-dashboard/
==================================
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📝 Summary:
AgentDevel presents a release engineering approach for large language model agents that treats them as shippable artifacts and emphasizes stable, auditable improvements through externalized testing an...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04620
• PDF: https://arxiv.org/pdf/2601.04620
• Project Page: https://trotsky1997.github.io/agentdevel-dashboard/
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