✨FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition
📝 Summary:
FiNERweb is a new pipeline that scales multilingual Named Entity Recognition dataset creation to 91 languages using LLMs. It produces 225k high-quality passages, enabling models to achieve comparable or improved zero-shot performance with 19x less data.
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13884
• PDF: https://arxiv.org/pdf/2512.13884
• Github: https://github.com/whoisjones/FiNERweb
==================================
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#NER #NLP #LLMs #MultilingualAI #Datasets
📝 Summary:
FiNERweb is a new pipeline that scales multilingual Named Entity Recognition dataset creation to 91 languages using LLMs. It produces 225k high-quality passages, enabling models to achieve comparable or improved zero-shot performance with 19x less data.
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13884
• PDF: https://arxiv.org/pdf/2512.13884
• Github: https://github.com/whoisjones/FiNERweb
==================================
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#NER #NLP #LLMs #MultilingualAI #Datasets
❤1
✨Understanding and Improving Hyperbolic Deep Reinforcement Learning
📝 Summary:
Hyper++ is a hyperbolic deep RL agent that improves stability and performance by addressing gradient issues and norm constraints in hyperbolic feature spaces. AI-generated summary The performance of r...
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14202
• PDF: https://arxiv.org/pdf/2512.14202
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Hyper++ is a hyperbolic deep RL agent that improves stability and performance by addressing gradient issues and norm constraints in hyperbolic feature spaces. AI-generated summary The performance of r...
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14202
• PDF: https://arxiv.org/pdf/2512.14202
==================================
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✨Puzzle Curriculum GRPO for Vision-Centric Reasoning
📝 Summary:
Puzzle Curriculum GRPO PC-GRPO improves VLM visual reasoning without annotations. It uses self-supervised puzzle environments for verifiable rewards and a difficulty-aware curriculum to enhance consistency and accuracy.
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14944
• PDF: https://arxiv.org/pdf/2512.14944
• Project Page: https://pcgrpo.github.io/
==================================
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#VLM #VisualReasoning #SelfSupervisedLearning #ComputerVision #AI
📝 Summary:
Puzzle Curriculum GRPO PC-GRPO improves VLM visual reasoning without annotations. It uses self-supervised puzzle environments for verifiable rewards and a difficulty-aware curriculum to enhance consistency and accuracy.
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14944
• PDF: https://arxiv.org/pdf/2512.14944
• Project Page: https://pcgrpo.github.io/
==================================
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#VLM #VisualReasoning #SelfSupervisedLearning #ComputerVision #AI
❤1
✨FrontierCS: Evolving Challenges for Evolving Intelligence
📝 Summary:
FrontierCS is a new benchmark for evaluating models on 156 open-ended computer science problems with unknown optimal solutions. Models must implement executable programs for tasks like NP-hard algorithmic and research problems. Empirical results show models lag human experts and over-optimize for...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15699
• PDF: https://arxiv.org/pdf/2512.15699
• Github: https://github.com/FrontierCS/Frontier-CS
==================================
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📝 Summary:
FrontierCS is a new benchmark for evaluating models on 156 open-ended computer science problems with unknown optimal solutions. Models must implement executable programs for tasks like NP-hard algorithmic and research problems. Empirical results show models lag human experts and over-optimize for...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15699
• PDF: https://arxiv.org/pdf/2512.15699
• Github: https://github.com/FrontierCS/Frontier-CS
==================================
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✨SonicMoE: Accelerating MoE with IO and Tile-aware Optimizations
📝 Summary:
SonicMoE optimizes Mixture of Experts models by reducing activation memory through minimal caching, overlapping IO with computation, and using token rounding to minimize padding waste. This achieves a 45 percent memory reduction and significantly improves compute throughput, enabling faster MoE t...
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14080
• PDF: https://arxiv.org/pdf/2512.14080
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
SonicMoE optimizes Mixture of Experts models by reducing activation memory through minimal caching, overlapping IO with computation, and using token rounding to minimize padding waste. This achieves a 45 percent memory reduction and significantly improves compute throughput, enabling faster MoE t...
🔹 Publication Date: Published on Dec 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14080
• PDF: https://arxiv.org/pdf/2512.14080
==================================
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✨Kling-Omni Technical Report
📝 Summary:
Kling-Omni is a versatile generative framework that synthesizes high-quality videos from multimodal inputs. It unifies video generation, editing, and reasoning tasks, supporting diverse inputs to create cinematic content. This system represents a pivotal step toward multimodal world simulators.
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16776
• PDF: https://arxiv.org/pdf/2512.16776
==================================
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📝 Summary:
Kling-Omni is a versatile generative framework that synthesizes high-quality videos from multimodal inputs. It unifies video generation, editing, and reasoning tasks, supporting diverse inputs to create cinematic content. This system represents a pivotal step toward multimodal world simulators.
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16776
• PDF: https://arxiv.org/pdf/2512.16776
==================================
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✨LLaDA2.0: Scaling Up Diffusion Language Models to 100B
📝 Summary:
LLaDA2.0 converts auto-regressive models into discrete diffusion large language models using a block-level training scheme, improving efficiency and performance at large scales. AI-generated summary T...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15745
• PDF: https://arxiv.org/pdf/2512.15745
==================================
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📝 Summary:
LLaDA2.0 converts auto-regressive models into discrete diffusion large language models using a block-level training scheme, improving efficiency and performance at large scales. AI-generated summary T...
🔹 Publication Date: Published on Dec 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15745
• PDF: https://arxiv.org/pdf/2512.15745
==================================
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✨DeContext as Defense: Safe Image Editing in Diffusion Transformers
📝 Summary:
DeContext defends against unauthorized in-context image editing by weakening cross-attention pathways in multimodal attention layers, preserving visual quality while blocking unwanted modifications. A...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16625
• PDF: https://arxiv.org/pdf/2512.16625
• Project Page: https://linghuiishen.github.io/decontext_project_page/
• Github: https://github.com/LinghuiiShen/DeContext
==================================
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📝 Summary:
DeContext defends against unauthorized in-context image editing by weakening cross-attention pathways in multimodal attention layers, preserving visual quality while blocking unwanted modifications. A...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16625
• PDF: https://arxiv.org/pdf/2512.16625
• Project Page: https://linghuiishen.github.io/decontext_project_page/
• Github: https://github.com/LinghuiiShen/DeContext
==================================
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✨Adaptation of Agentic AI
📝 Summary:
This paper presents a framework for agent and tool adaptation in agentic AI systems, clarifying design strategies and identifying open challenges for improving AI capabilities. AI-generated summary Cu...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16301
• PDF: https://arxiv.org/pdf/2512.16301
• Github: https://github.com/pat-jj/Awesome-Adaptation-of-Agentic-AI
==================================
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📝 Summary:
This paper presents a framework for agent and tool adaptation in agentic AI systems, clarifying design strategies and identifying open challenges for improving AI capabilities. AI-generated summary Cu...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16301
• PDF: https://arxiv.org/pdf/2512.16301
• Github: https://github.com/pat-jj/Awesome-Adaptation-of-Agentic-AI
==================================
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✨TabReX : Tabular Referenceless eXplainable Evaluation
📝 Summary:
TabReX is a reference-less framework using graph-based reasoning to evaluate the quality of tables generated by LLMs, offering structural and factual fidelity scores. AI-generated summary Evaluating t...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15907
• PDF: https://arxiv.org/pdf/2512.15907
• Project Page: https://coral-lab-asu.github.io/TabReX/
• Github: https://github.com/CoRAL-ASU/TabReX
==================================
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📝 Summary:
TabReX is a reference-less framework using graph-based reasoning to evaluate the quality of tables generated by LLMs, offering structural and factual fidelity scores. AI-generated summary Evaluating t...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15907
• PDF: https://arxiv.org/pdf/2512.15907
• Project Page: https://coral-lab-asu.github.io/TabReX/
• Github: https://github.com/CoRAL-ASU/TabReX
==================================
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✨Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model
📝 Summary:
Seedance 1.5 pro, a dual-branch Diffusion Transformer model, achieves high-quality audio-visual synchronization and generation through cross-modal integration, post-training optimizations, and an acce...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13507
• PDF: https://arxiv.org/pdf/2512.13507
• Project Page: https://seed.bytedance.com/seedance1_5_pro
==================================
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📝 Summary:
Seedance 1.5 pro, a dual-branch Diffusion Transformer model, achieves high-quality audio-visual synchronization and generation through cross-modal integration, post-training optimizations, and an acce...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13507
• PDF: https://arxiv.org/pdf/2512.13507
• Project Page: https://seed.bytedance.com/seedance1_5_pro
==================================
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✨Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation
📝 Summary:
A panoramic metric depth foundation model using DINOv3-Large and a three-stage pseudo-label pipeline achieves robust performance across diverse real-world scenes. AI-generated summary In this work, we...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16913
• PDF: https://arxiv.org/pdf/2512.16913
• Github: https://insta360-research-team.github.io/DAP
==================================
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📝 Summary:
A panoramic metric depth foundation model using DINOv3-Large and a three-stage pseudo-label pipeline achieves robust performance across diverse real-world scenes. AI-generated summary In this work, we...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16913
• PDF: https://arxiv.org/pdf/2512.16913
• Github: https://insta360-research-team.github.io/DAP
==================================
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✨Next-Embedding Prediction Makes Strong Vision Learners
📝 Summary:
Generative pretraining using next embedding prediction outperforms traditional self-supervised methods in visual learning tasks, achieving high accuracy on ImageNet and effective transfer to semantic ...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16922
• PDF: https://arxiv.org/pdf/2512.16922
🔹 Models citing this paper:
• https://huggingface.co/SixAILab/nepa-base-patch14-224-sft
• https://huggingface.co/SixAILab/nepa-large-patch14-224
• https://huggingface.co/SixAILab/nepa-base-patch14-224
==================================
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📝 Summary:
Generative pretraining using next embedding prediction outperforms traditional self-supervised methods in visual learning tasks, achieving high accuracy on ImageNet and effective transfer to semantic ...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16922
• PDF: https://arxiv.org/pdf/2512.16922
🔹 Models citing this paper:
• https://huggingface.co/SixAILab/nepa-base-patch14-224-sft
• https://huggingface.co/SixAILab/nepa-large-patch14-224
• https://huggingface.co/SixAILab/nepa-base-patch14-224
==================================
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✨Alchemist: Unlocking Efficiency in Text-to-Image Model Training via Meta-Gradient Data Selection
📝 Summary:
Alchemist, a meta-gradient-based framework, automatically selects high-quality subsets from large-scale text-image datasets to improve visual quality and training efficiency in Text-to-Image models. A...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16905
• PDF: https://arxiv.org/pdf/2512.16905
==================================
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📝 Summary:
Alchemist, a meta-gradient-based framework, automatically selects high-quality subsets from large-scale text-image datasets to improve visual quality and training efficiency in Text-to-Image models. A...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16905
• PDF: https://arxiv.org/pdf/2512.16905
==================================
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✨StereoPilot: Learning Unified and Efficient Stereo Conversion via Generative Priors
📝 Summary:
StereoPilot, a feed-forward model leveraging a learnable domain switcher and cycle consistency loss, synthesizes high-quality stereo video directly without depth maps, outperforming existing methods i...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16915
• PDF: https://arxiv.org/pdf/2512.16915
==================================
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📝 Summary:
StereoPilot, a feed-forward model leveraging a learnable domain switcher and cycle consistency loss, synthesizes high-quality stereo video directly without depth maps, outperforming existing methods i...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16915
• PDF: https://arxiv.org/pdf/2512.16915
==================================
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✨Exploration v.s. Exploitation: Rethinking RLVR through Clipping, Entropy, and Spurious Reward
📝 Summary:
Reinforcement learning with verifiable rewards improves LLM reasoning through spurious rewards and entropy minimization, despite seemingly paradoxical effects, by reducing clipping bias and policy ent...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16912
• PDF: https://arxiv.org/pdf/2512.16912
==================================
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📝 Summary:
Reinforcement learning with verifiable rewards improves LLM reasoning through spurious rewards and entropy minimization, despite seemingly paradoxical effects, by reducing clipping bias and policy ent...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16912
• PDF: https://arxiv.org/pdf/2512.16912
==================================
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✨Differences That Matter: Auditing Models for Capability Gap Discovery and Rectification
📝 Summary:
AuditDM, an automated framework using reinforcement learning, identifies and rectifies failure modes in multimodal LLMs by generating challenging examples, leading to improved performance across bench...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16921
• PDF: https://arxiv.org/pdf/2512.16921
• Project Page: https://auditdm.github.io/
• Github: https://auditdm.github.io/
==================================
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📝 Summary:
AuditDM, an automated framework using reinforcement learning, identifies and rectifies failure modes in multimodal LLMs by generating challenging examples, leading to improved performance across bench...
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16921
• PDF: https://arxiv.org/pdf/2512.16921
• Project Page: https://auditdm.github.io/
• Github: https://auditdm.github.io/
==================================
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✨EmoCaliber: Advancing Reliable Visual Emotion Comprehension via Confidence Verbalization and Calibration
📝 Summary:
EmoCaliber, a confidence-aware Multimodal Large Language Model, enhances Visual Emotion Comprehension by verbalizing confidence in emotion predictions, leading to improved reliability and accuracy. AI...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15528
• PDF: https://arxiv.org/pdf/2512.15528
• Github: https://github.com/wdqqdw/EmoCaliber
==================================
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📝 Summary:
EmoCaliber, a confidence-aware Multimodal Large Language Model, enhances Visual Emotion Comprehension by verbalizing confidence in emotion predictions, leading to improved reliability and accuracy. AI...
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15528
• PDF: https://arxiv.org/pdf/2512.15528
• Github: https://github.com/wdqqdw/EmoCaliber
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✨Generative Refocusing: Flexible Defocus Control from a Single Image
📝 Summary:
Generative Refocusing uses DeblurNet and BokehNet for high-quality single-image refocusing. Its semi-supervised training with real bokeh images and EXIF metadata enables controllable bokeh and text-guided adjustments, outperforming current methods.
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16923
• PDF: https://arxiv.org/pdf/2512.16923
• Project Page: https://generative-refocusing.github.io/
• Github: https://github.com/rayray9999/Genfocus
==================================
For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Generative Refocusing uses DeblurNet and BokehNet for high-quality single-image refocusing. Its semi-supervised training with real bokeh images and EXIF metadata enables controllable bokeh and text-guided adjustments, outperforming current methods.
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16923
• PDF: https://arxiv.org/pdf/2512.16923
• Project Page: https://generative-refocusing.github.io/
• Github: https://github.com/rayray9999/Genfocus
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
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