ML Engineers: NVIDIA has released a guide for beginners on fine-tuning LLMs using Unsloth.
The guide covers:
- training methods: LoRA, FFT, RL
- when and why to do fine-tuning, real use cases
- how much data and VRAM are required
- how to train locally on DGX Spark, RTX graphics cards, and more
Guide: https://blogs.nvidia.com/blog/rtx-ai-garage-fine-tuning-unsloth-dgx-spark/
π https://t.me/DataScienceT
The guide covers:
- training methods: LoRA, FFT, RL
- when and why to do fine-tuning, real use cases
- how much data and VRAM are required
- how to train locally on DGX Spark, RTX graphics cards, and more
Guide: https://blogs.nvidia.com/blog/rtx-ai-garage-fine-tuning-unsloth-dgx-spark/
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β€4
β¨Latent Implicit Visual Reasoning
π Summary:
Large Multimodal Models struggle with visual reasoning due to their text-centric nature and limitations of prior methods. This paper introduces a task-agnostic mechanism for LMMs to discover and use visual reasoning tokens without explicit supervision. The approach achieves state-of-the-art resul...
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21218
β’ PDF: https://arxiv.org/pdf/2512.21218
==================================
For more data science resources:
β https://t.me/DataScienceT
#LMMs #VisualReasoning #AI #ComputerVision #DeepLearning
π Summary:
Large Multimodal Models struggle with visual reasoning due to their text-centric nature and limitations of prior methods. This paper introduces a task-agnostic mechanism for LMMs to discover and use visual reasoning tokens without explicit supervision. The approach achieves state-of-the-art resul...
πΉ Publication Date: Published on Dec 24
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21218
β’ PDF: https://arxiv.org/pdf/2512.21218
==================================
For more data science resources:
β https://t.me/DataScienceT
#LMMs #VisualReasoning #AI #ComputerVision #DeepLearning
β€1
β¨Schoenfeld's Anatomy of Mathematical Reasoning by Language Models
π Summary:
This paper introduces ThinkARM, a framework based on Schoenfelds Episode Theory, to abstract LLM reasoning traces into functional steps. It reveals distinct thinking dynamics and structural differences in models solving math problems, with exploration being key for correctness. This makes LLM rea...
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19995
β’ PDF: https://arxiv.org/pdf/2512.19995
β’ Github: https://github.com/MingLiiii/ThinkARM
==================================
For more data science resources:
β https://t.me/DataScienceT
#LLM #AIReasoning #MathematicalReasoning #AI #MachineLearning
π Summary:
This paper introduces ThinkARM, a framework based on Schoenfelds Episode Theory, to abstract LLM reasoning traces into functional steps. It reveals distinct thinking dynamics and structural differences in models solving math problems, with exploration being key for correctness. This makes LLM rea...
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19995
β’ PDF: https://arxiv.org/pdf/2512.19995
β’ Github: https://github.com/MingLiiii/ThinkARM
==================================
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β https://t.me/DataScienceT
#LLM #AIReasoning #MathematicalReasoning #AI #MachineLearning
β€1
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β¨Spatia: Video Generation with Updatable Spatial Memory
π Summary:
Spatia is a video generation framework that improves long-term consistency by using an updatable 3D scene point cloud as persistent spatial memory. It iteratively generates video clips and updates this memory via visual SLAM, enabling realistic videos and 3D-aware interactive editing.
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15716
β’ PDF: https://arxiv.org/pdf/2512.15716
β’ Project Page: https://zhaojingjing713.github.io/Spatia/
β’ Github: https://github.com/ZhaoJingjing713/Spatia
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #GenerativeAI #ComputerVision #3DReconstruction #SLAM
π Summary:
Spatia is a video generation framework that improves long-term consistency by using an updatable 3D scene point cloud as persistent spatial memory. It iteratively generates video clips and updates this memory via visual SLAM, enabling realistic videos and 3D-aware interactive editing.
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15716
β’ PDF: https://arxiv.org/pdf/2512.15716
β’ Project Page: https://zhaojingjing713.github.io/Spatia/
β’ Github: https://github.com/ZhaoJingjing713/Spatia
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #GenerativeAI #ComputerVision #3DReconstruction #SLAM
β€1
β¨VA-Ο: Variational Policy Alignment for Pixel-Aware Autoregressive Generation
π Summary:
VA-$\pi$ optimizes autoregressive visual generators using a pixel-space objective to improve image quality and performance without retraining tokenizers or using external rewards. AI-generated summary...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19680
β’ PDF: https://arxiv.org/pdf/2512.19680
β’ Project Page: https://lil-shake.github.io/va-pi.github.io/
β’ Github: https://github.com/Lil-Shake/VA-Pi
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
VA-$\pi$ optimizes autoregressive visual generators using a pixel-space objective to improve image quality and performance without retraining tokenizers or using external rewards. AI-generated summary...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19680
β’ PDF: https://arxiv.org/pdf/2512.19680
β’ Project Page: https://lil-shake.github.io/va-pi.github.io/
β’ Github: https://github.com/Lil-Shake/VA-Pi
==================================
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β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€1
β¨GTR-Turbo: Merged Checkpoint is Secretly a Free Teacher for Agentic VLM Training
π Summary:
Multi-turn reinforcement learning (RL) for multi-modal agents built upon vision-language models (VLMs) is hampered by sparse rewards and long-horizon credit assignment. Recent methods densify the rewa...
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.13043
β’ PDF: https://arxiv.org/pdf/2512.13043
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Multi-turn reinforcement learning (RL) for multi-modal agents built upon vision-language models (VLMs) is hampered by sparse rewards and long-horizon credit assignment. Recent methods densify the rewa...
πΉ Publication Date: Published on Dec 15
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.13043
β’ PDF: https://arxiv.org/pdf/2512.13043
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€1
β¨How Much 3D Do Video Foundation Models Encode?
π Summary:
A new framework quantifies 3D understanding in Video Foundation Models VidFMs. VidFMs, trained only on video, show strong 3D awareness, often surpassing expert 3D models, providing insights for 3D AI.
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19949
β’ PDF: https://arxiv.org/pdf/2512.19949
β’ Project Page: https://vidfm-3d-probe.github.io/
β’ Github: https://vidfm-3d-probe.github.io
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoFoundationModels #3DUnderstanding #ComputerVision #AIResearch #DeepLearning
π Summary:
A new framework quantifies 3D understanding in Video Foundation Models VidFMs. VidFMs, trained only on video, show strong 3D awareness, often surpassing expert 3D models, providing insights for 3D AI.
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19949
β’ PDF: https://arxiv.org/pdf/2512.19949
β’ Project Page: https://vidfm-3d-probe.github.io/
β’ Github: https://vidfm-3d-probe.github.io
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoFoundationModels #3DUnderstanding #ComputerVision #AIResearch #DeepLearning
β€2
β¨Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning
π Summary:
AR models face inefficient exploration and sparse rewards in RL. Internal RL uses a higher-order model to learn temporal abstraction controllers. This enables efficient learning from sparse rewards where standard RL fails.
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20605
β’ PDF: https://arxiv.org/pdf/2512.20605
==================================
For more data science resources:
β https://t.me/DataScienceT
#ReinforcementLearning #HierarchicalRL #AutoregressiveModels #MachineLearning #ArtificialIntelligence
π Summary:
AR models face inefficient exploration and sparse rewards in RL. Internal RL uses a higher-order model to learn temporal abstraction controllers. This enables efficient learning from sparse rewards where standard RL fails.
πΉ Publication Date: Published on Dec 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.20605
β’ PDF: https://arxiv.org/pdf/2512.20605
==================================
For more data science resources:
β https://t.me/DataScienceT
#ReinforcementLearning #HierarchicalRL #AutoregressiveModels #MachineLearning #ArtificialIntelligence
β€2
π Master Data Science & Programming!
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π° Machine Learning with Python
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π§ Code With Python
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Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
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β€1
β¨Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
π Summary:
Fast3R is a Transformer-based method for efficient and scalable multi-view 3D reconstruction. It processes many images in parallel in a single forward pass, improving speed and accuracy over pairwise approaches like DUSt3R.
πΉ Publication Date: Published on Jan 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2501.13928
β’ PDF: https://arxiv.org/pdf/2501.13928
β’ Github: https://github.com/naver/dust3r/pull/16
πΉ Models citing this paper:
β’ https://huggingface.co/jedyang97/Fast3R_ViT_Large_512
==================================
For more data science resources:
β https://t.me/DataScienceT
#3DReconstruction #ComputerVision #Transformers #Fast3R #DeepLearning
π Summary:
Fast3R is a Transformer-based method for efficient and scalable multi-view 3D reconstruction. It processes many images in parallel in a single forward pass, improving speed and accuracy over pairwise approaches like DUSt3R.
πΉ Publication Date: Published on Jan 23
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2501.13928
β’ PDF: https://arxiv.org/pdf/2501.13928
β’ Github: https://github.com/naver/dust3r/pull/16
πΉ Models citing this paper:
β’ https://huggingface.co/jedyang97/Fast3R_ViT_Large_512
==================================
For more data science resources:
β https://t.me/DataScienceT
#3DReconstruction #ComputerVision #Transformers #Fast3R #DeepLearning
β¨SkyReels-V2: Infinite-length Film Generative Model
π Summary:
SkyReels-V2 is an infinite-length film generative model that addresses video generation challenges by synergizing MLLMs, reinforcement learning, and a diffusion forcing framework. It enables high-quality, long-form video synthesis with realistic motion and cinematic grammar awareness through mult...
πΉ Publication Date: Published on Apr 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2504.13074
β’ PDF: https://arxiv.org/pdf/2504.13074
β’ Github: https://github.com/skyworkai/skyreels-v2
πΉ Models citing this paper:
β’ https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-540P
β’ https://huggingface.co/Skywork/SkyCaptioner-V1
β’ https://huggingface.co/Skywork/SkyReels-V2-I2V-1.3B-540P
β¨ Spaces citing this paper:
β’ https://huggingface.co/spaces/fffiloni/SkyReels-V2
β’ https://huggingface.co/spaces/Dudu0043/SkyReels-V2
β’ https://huggingface.co/spaces/14eee109giet/SkyReels-V2
==================================
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β https://t.me/DataScienceT
#VideoGeneration #GenerativeAI #MLLM #DiffusionModels #AIResearch
π Summary:
SkyReels-V2 is an infinite-length film generative model that addresses video generation challenges by synergizing MLLMs, reinforcement learning, and a diffusion forcing framework. It enables high-quality, long-form video synthesis with realistic motion and cinematic grammar awareness through mult...
πΉ Publication Date: Published on Apr 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2504.13074
β’ PDF: https://arxiv.org/pdf/2504.13074
β’ Github: https://github.com/skyworkai/skyreels-v2
πΉ Models citing this paper:
β’ https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-540P
β’ https://huggingface.co/Skywork/SkyCaptioner-V1
β’ https://huggingface.co/Skywork/SkyReels-V2-I2V-1.3B-540P
β¨ Spaces citing this paper:
β’ https://huggingface.co/spaces/fffiloni/SkyReels-V2
β’ https://huggingface.co/spaces/Dudu0043/SkyReels-V2
β’ https://huggingface.co/spaces/14eee109giet/SkyReels-V2
==================================
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β https://t.me/DataScienceT
#VideoGeneration #GenerativeAI #MLLM #DiffusionModels #AIResearch
arXiv.org
SkyReels-V2: Infinite-length Film Generative Model
Recent advances in video generation have been driven by diffusion models and autoregressive frameworks, yet critical challenges persist in harmonizing prompt adherence, visual quality, motion...
β€2
β¨VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos
π Summary:
VideoRAG introduces the first RAG framework for long videos, using a dual-channel architecture to integrate textual knowledge grounding and multi-modal context encoding. This enables unlimited-length video processing and significantly outperforms existing methods.
πΉ Publication Date: Published on Feb 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2502.01549
β’ PDF: https://arxiv.org/pdf/2502.01549
β’ Github: https://github.com/hkuds/videorag
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoRAG #RAG #LongVideo #AI #MultimodalAI
π Summary:
VideoRAG introduces the first RAG framework for long videos, using a dual-channel architecture to integrate textual knowledge grounding and multi-modal context encoding. This enables unlimited-length video processing and significantly outperforms existing methods.
πΉ Publication Date: Published on Feb 3
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2502.01549
β’ PDF: https://arxiv.org/pdf/2502.01549
β’ Github: https://github.com/hkuds/videorag
==================================
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β https://t.me/DataScienceT
#VideoRAG #RAG #LongVideo #AI #MultimodalAI
β€2
π Master Data Science & Programming!
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π Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
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π§ Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
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π ML Research Hub
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π Data Science Jupyter Notebooks
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π Data Analytics
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β€1
β¨A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
π Summary:
This survey reviews self-evolving AI agents that adapt to dynamic environments via automatic enhancement from interaction data. It proposes a unified framework and systematically reviews current techniques, addressing evaluation, safety, and ethics.
πΉ Publication Date: Published on Aug 10
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.07407
β’ PDF: https://arxiv.org/pdf/2508.07407
β’ Project Page: https://huggingface.co/spaces/X-iZhang/Awesome-Self-Evolving-Agents
β’ Github: https://github.com/EvoAgentX/Awesome-Self-Evolving-Agents
β¨ Spaces citing this paper:
β’ https://huggingface.co/spaces/X-iZhang/Awesome-Self-Evolving-Agents
==================================
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β https://t.me/DataScienceT
#SelfEvolvingAI #AIAgents #FoundationModels #LifelongLearning #ArtificialIntelligence
π Summary:
This survey reviews self-evolving AI agents that adapt to dynamic environments via automatic enhancement from interaction data. It proposes a unified framework and systematically reviews current techniques, addressing evaluation, safety, and ethics.
πΉ Publication Date: Published on Aug 10
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2508.07407
β’ PDF: https://arxiv.org/pdf/2508.07407
β’ Project Page: https://huggingface.co/spaces/X-iZhang/Awesome-Self-Evolving-Agents
β’ Github: https://github.com/EvoAgentX/Awesome-Self-Evolving-Agents
β¨ Spaces citing this paper:
β’ https://huggingface.co/spaces/X-iZhang/Awesome-Self-Evolving-Agents
==================================
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β€1
π Master Data Science & Programming!
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https://t.me/CodeProgrammer
π Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
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π§ Code With Python
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π ML Research Hub
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π¬ Data Science Chat
An active community group for discussing data challenges and networking with peers.
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β¨InsertAnywhere: Bridging 4D Scene Geometry and Diffusion Models for Realistic Video Object Insertion
π Summary:
InsertAnywhere is a framework for realistic video object insertion. It uses 4D aware mask generation for geometric consistency and an extended diffusion model for appearance-faithful synthesis, outperforming existing methods.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17504
β’ PDF: https://arxiv.org/pdf/2512.17504
β’ Project Page: https://myyzzzoooo.github.io/InsertAnywhere/
β’ Github: https://github.com/myyzzzoooo/InsertAnywhere
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoEditing #DiffusionModels #ComputerVision #DeepLearning #GenerativeAI
π Summary:
InsertAnywhere is a framework for realistic video object insertion. It uses 4D aware mask generation for geometric consistency and an extended diffusion model for appearance-faithful synthesis, outperforming existing methods.
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17504
β’ PDF: https://arxiv.org/pdf/2512.17504
β’ Project Page: https://myyzzzoooo.github.io/InsertAnywhere/
β’ Github: https://github.com/myyzzzoooo/InsertAnywhere
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoEditing #DiffusionModels #ComputerVision #DeepLearning #GenerativeAI
β€1
β¨MAI-UI Technical Report: Real-World Centric Foundation GUI Agents
π Summary:
MAI-UI introduces a family of foundation GUI agents tackling real-world deployment challenges. It uses a self-evolving data pipeline, device-cloud collaboration, and online RL to set new state-of-the-art in GUI grounding and mobile navigation, significantly boosting performance and privacy.
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22047
β’ PDF: https://arxiv.org/pdf/2512.22047
==================================
For more data science resources:
β https://t.me/DataScienceT
#GUIAgents #AI #ReinforcementLearning #MobileTech #HCI
π Summary:
MAI-UI introduces a family of foundation GUI agents tackling real-world deployment challenges. It uses a self-evolving data pipeline, device-cloud collaboration, and online RL to set new state-of-the-art in GUI grounding and mobile navigation, significantly boosting performance and privacy.
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22047
β’ PDF: https://arxiv.org/pdf/2512.22047
==================================
For more data science resources:
β https://t.me/DataScienceT
#GUIAgents #AI #ReinforcementLearning #MobileTech #HCI
β€2
β¨See Less, See Right: Bi-directional Perceptual Shaping For Multimodal Reasoning
π Summary:
Bi-directional Perceptual Shaping BiPS improves vision-language models by using question-conditioned masked views to shape perception during training. It employs two constraints to ensure complete coverage of relevant pixels and enforce fine-grained visual reliance, preventing text-only shortcuts...
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22120
β’ PDF: https://arxiv.org/pdf/2512.22120
β’ Github: https://github.com/zss02/BiPS
==================================
For more data science resources:
β https://t.me/DataScienceT
#MultimodalAI #VisionLanguageModels #MachineLearning #AIResearch #DeepLearning
π Summary:
Bi-directional Perceptual Shaping BiPS improves vision-language models by using question-conditioned masked views to shape perception during training. It employs two constraints to ensure complete coverage of relevant pixels and enforce fine-grained visual reliance, preventing text-only shortcuts...
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22120
β’ PDF: https://arxiv.org/pdf/2512.22120
β’ Github: https://github.com/zss02/BiPS
==================================
For more data science resources:
β https://t.me/DataScienceT
#MultimodalAI #VisionLanguageModels #MachineLearning #AIResearch #DeepLearning
β€1
β¨ProEdit: Inversion-based Editing From Prompts Done Right
π Summary:
Inversion-based visual editing provides an effective and training-free way to edit an image or a video based on user instructions. Existing methods typically inject source image information during the...
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22118
β’ PDF: https://arxiv.org/pdf/2512.22118
β’ Project Page: https://isee-laboratory.github.io/ProEdit/
β’ Github: https://isee-laboratory.github.io/ProEdit
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Inversion-based visual editing provides an effective and training-free way to edit an image or a video based on user instructions. Existing methods typically inject source image information during the...
πΉ Publication Date: Published on Dec 26
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22118
β’ PDF: https://arxiv.org/pdf/2512.22118
β’ Project Page: https://isee-laboratory.github.io/ProEdit/
β’ Github: https://isee-laboratory.github.io/ProEdit
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€1
β¨SVBench: Evaluation of Video Generation Models on Social Reasoning
π Summary:
Recent text-to-video generation models exhibit remarkable progress in visual realism, motion fidelity, and text-video alignment, yet they remain fundamentally limited in their ability to generate soci...
πΉ Publication Date: Published on Dec 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21507
β’ PDF: https://arxiv.org/pdf/2512.21507
β’ Github: https://github.com/Gloria2tt/SVBench-Evaluation
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Recent text-to-video generation models exhibit remarkable progress in visual realism, motion fidelity, and text-video alignment, yet they remain fundamentally limited in their ability to generate soci...
πΉ Publication Date: Published on Dec 25
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.21507
β’ PDF: https://arxiv.org/pdf/2512.21507
β’ Github: https://github.com/Gloria2tt/SVBench-Evaluation
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€1