π Master Data Science & Programming!
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π° Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
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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― PyData Careers | Quiz
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Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
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Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
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An active community group for discussing data challenges and networking with peers.
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π Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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πΊ Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
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π Data Analytics
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
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Master Python with step-by-step courses β from basics to advanced projects and practical applications.
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Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.iss.one/CodeProgrammer
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.
https://t.iss.one/DataScienceM
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.
https://t.iss.one/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.iss.one/DataScienceQ
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.iss.one/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.iss.one/DataScienceC
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.iss.one/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.iss.one/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.iss.one/PythonArab
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.iss.one/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
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Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
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β¨Bolmo: Byteifying the Next Generation of Language Models
π Summary:
Bolmo introduces competitive byte-level language models by efficiently converting existing subword models. This byteification overcomes subword limitations, matching performance with minimal training. Bolmo makes byte-level LMs practical.
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15586
β’ PDF: https://arxiv.org/pdf/2512.15586
πΉ Models citing this paper:
β’ https://huggingface.co/allenai/Bolmo-7B
β’ https://huggingface.co/allenai/Bolmo-1B
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/allenai/bolmo_mix
==================================
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β https://t.iss.one/DataScienceT
#LanguageModels #ByteLevelLMs #NLP #DeepLearning #AIResearch
π Summary:
Bolmo introduces competitive byte-level language models by efficiently converting existing subword models. This byteification overcomes subword limitations, matching performance with minimal training. Bolmo makes byte-level LMs practical.
πΉ Publication Date: Published on Dec 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.15586
β’ PDF: https://arxiv.org/pdf/2512.15586
πΉ Models citing this paper:
β’ https://huggingface.co/allenai/Bolmo-7B
β’ https://huggingface.co/allenai/Bolmo-1B
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/allenai/bolmo_mix
==================================
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β https://t.iss.one/DataScienceT
#LanguageModels #ByteLevelLMs #NLP #DeepLearning #AIResearch
β€1
β¨DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AI
π Summary:
DataFlow is an LLM-driven framework for unified, high-quality data preparation. It automates pipeline generation from natural language, significantly boosting LLM performance across diverse tasks like math, code, and text. DataFlow ensures reproducible data and provides a scalable foundation for AI.
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16676
β’ PDF: https://arxiv.org/pdf/2512.16676
β’ Project Page: https://github.com/OpenDCAI/DataFlow
β’ Github: https://github.com/OpenDCAI/DataFlow
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/OpenDCAI/dataflow-demo-code
β’ https://huggingface.co/datasets/OpenDCAI/dataflow-demo-Text2SQL
β’ https://huggingface.co/datasets/OpenDCAI/dataflow-instruct-10k
==================================
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β https://t.iss.one/DataScienceT
#LLM #DataPreparation #DataCentricAI #WorkflowAutomation #AIResearch
π Summary:
DataFlow is an LLM-driven framework for unified, high-quality data preparation. It automates pipeline generation from natural language, significantly boosting LLM performance across diverse tasks like math, code, and text. DataFlow ensures reproducible data and provides a scalable foundation for AI.
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.16676
β’ PDF: https://arxiv.org/pdf/2512.16676
β’ Project Page: https://github.com/OpenDCAI/DataFlow
β’ Github: https://github.com/OpenDCAI/DataFlow
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/OpenDCAI/dataflow-demo-code
β’ https://huggingface.co/datasets/OpenDCAI/dataflow-demo-Text2SQL
β’ https://huggingface.co/datasets/OpenDCAI/dataflow-instruct-10k
==================================
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β https://t.iss.one/DataScienceT
#LLM #DataPreparation #DataCentricAI #WorkflowAutomation #AIResearch
arXiv.org
DataFlow: An LLM-Driven Framework for Unified Data Preparation and...
The rapidly growing demand for high-quality data in Large Language Models (LLMs) has intensified the need for scalable, reliable, and semantically rich data preparation pipelines. However, current...
β¨Can LLMs Estimate Student Struggles? Human-AI Difficulty Alignment with Proficiency Simulation for Item Difficulty Prediction
π Summary:
LLMs poorly estimate human cognitive difficulty for educational tasks. Scaling models does not improve alignment with humans; they converge to a machine consensus and fail to simulate student struggles or show introspection.
πΉ Publication Date: Published on Dec 21
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.18880
β’ PDF: https://arxiv.org/pdf/2512.18880
β’ Github: https://github.com/MingLiiii/Difficulty_Alignment
==================================
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β https://t.iss.one/DataScienceT
#LLM #EducationalAI #ItemDifficulty #HumanAIAlignment #AIResearch
π Summary:
LLMs poorly estimate human cognitive difficulty for educational tasks. Scaling models does not improve alignment with humans; they converge to a machine consensus and fail to simulate student struggles or show introspection.
πΉ Publication Date: Published on Dec 21
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.18880
β’ PDF: https://arxiv.org/pdf/2512.18880
β’ Github: https://github.com/MingLiiii/Difficulty_Alignment
==================================
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β https://t.iss.one/DataScienceT
#LLM #EducationalAI #ItemDifficulty #HumanAIAlignment #AIResearch
β¨The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding
π Summary:
The Prism Hypothesis posits semantic encoders capture low-frequency meaning, while pixel encoders retain high-frequency details. Unified Autoencoding UAE leverages this with a frequency-band modulator to harmonize both into a single latent space. This achieves state-of-the-art performance on imag...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19693
β’ PDF: https://arxiv.org/pdf/2512.19693
β’ Github: https://github.com/WeichenFan/UAE
==================================
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β https://t.iss.one/DataScienceT
#DeepLearning #ComputerVision #Autoencoders #RepresentationLearning #AIResearch
π Summary:
The Prism Hypothesis posits semantic encoders capture low-frequency meaning, while pixel encoders retain high-frequency details. Unified Autoencoding UAE leverages this with a frequency-band modulator to harmonize both into a single latent space. This achieves state-of-the-art performance on imag...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19693
β’ PDF: https://arxiv.org/pdf/2512.19693
β’ Github: https://github.com/WeichenFan/UAE
==================================
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β https://t.iss.one/DataScienceT
#DeepLearning #ComputerVision #Autoencoders #RepresentationLearning #AIResearch
β¨GenEnv: Difficulty-Aligned Co-Evolution Between LLM Agents and Environment Simulators
π Summary:
GenEnv, a framework using a co-evolutionary game with a generative environment simulator, enhances LLM agent performance by 40.3% over 7B baselines and uses less data than offline augmentation. AI-gen...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19682
β’ PDF: https://arxiv.org/pdf/2512.19682
β’ Github: https://github.com/Gen-Verse/GenEnv
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
GenEnv, a framework using a co-evolutionary game with a generative environment simulator, enhances LLM agent performance by 40.3% over 7B baselines and uses less data than offline augmentation. AI-gen...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19682
β’ PDF: https://arxiv.org/pdf/2512.19682
β’ Github: https://github.com/Gen-Verse/GenEnv
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨StoryMem: Multi-shot Long Video Storytelling with Memory
π Summary:
StoryMem enhances multi-shot video generation with cinematic quality and long-range consistency using a memory bank and pre-trained single-shot video diffusion models. AI-generated summary Visual stor...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19539
β’ PDF: https://arxiv.org/pdf/2512.19539
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
StoryMem enhances multi-shot video generation with cinematic quality and long-range consistency using a memory bank and pre-trained single-shot video diffusion models. AI-generated summary Visual stor...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19539
β’ PDF: https://arxiv.org/pdf/2512.19539
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive, and MCP-Augmented Environments
π Summary:
MobileWorld, a more challenging benchmark than AndroidWorld, includes diverse real-world mobile tasks and interactions, revealing significant gaps in current model capabilities. AI-generated summary A...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19432
β’ PDF: https://arxiv.org/pdf/2512.19432
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
MobileWorld, a more challenging benchmark than AndroidWorld, includes diverse real-world mobile tasks and interactions, revealing significant gaps in current model capabilities. AI-generated summary A...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19432
β’ PDF: https://arxiv.org/pdf/2512.19432
==================================
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β https://t.iss.one/DataScienceT
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β¨Name That Part: 3D Part Segmentation and Naming
π Summary:
ALIGN-Parts addresses semantic 3D part segmentation by aligning implicit 3D part representations with part descriptions using geometric, appearance, and semantic cues, supporting open-vocabulary part ...
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.18003
β’ PDF: https://arxiv.org/pdf/2512.18003
β’ Project Page: https://name-that-part.github.io/
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
ALIGN-Parts addresses semantic 3D part segmentation by aligning implicit 3D part representations with part descriptions using geometric, appearance, and semantic cues, supporting open-vocabulary part ...
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.18003
β’ PDF: https://arxiv.org/pdf/2512.18003
β’ Project Page: https://name-that-part.github.io/
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation
π Summary:
QuCo-RAG uses objective corpus statistics to mitigate hallucinations in large language models during generation, improving accuracy across various benchmarks. AI-generated summary Dynamic Retrieval-Au...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19134
β’ PDF: https://arxiv.org/pdf/2512.19134
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
QuCo-RAG uses objective corpus statistics to mitigate hallucinations in large language models during generation, improving accuracy across various benchmarks. AI-generated summary Dynamic Retrieval-Au...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19134
β’ PDF: https://arxiv.org/pdf/2512.19134
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
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β¨Region-Constraint In-Context Generation for Instructional Video Editing
π Summary:
ReCo is a novel instructional video editing paradigm that enhances accuracy and reduces token interference by incorporating constraint modeling and regularization techniques during in-context generati...
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17650
β’ PDF: https://arxiv.org/pdf/2512.17650
β’ Project Page: https://zhw-zhang.github.io/ReCo-page/
β’ Github: https://github.com/HiDream-ai/ReCo
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/HiDream-ai/ReCo-Data
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
ReCo is a novel instructional video editing paradigm that enhances accuracy and reduces token interference by incorporating constraint modeling and regularization techniques during in-context generati...
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17650
β’ PDF: https://arxiv.org/pdf/2512.17650
β’ Project Page: https://zhw-zhang.github.io/ReCo-page/
β’ Github: https://github.com/HiDream-ai/ReCo
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/HiDream-ai/ReCo-Data
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
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β¨WorldWarp: Propagating 3D Geometry with Asynchronous Video Diffusion
π Summary:
WorldWarp addresses the challenge of generating consistent long-range videos by integrating a 3D geometric cache with a spatio-temporal diffusion model, ensuring structural consistency and textural re...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19678
β’ PDF: https://arxiv.org/pdf/2512.19678
β’ Project Page: https://hyokong.github.io/worldwarp-page/
β’ Github: https://hyokong.github.io/worldwarp-page/
πΉ Models citing this paper:
β’ https://huggingface.co/imsuperkong/worldwarp
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
WorldWarp addresses the challenge of generating consistent long-range videos by integrating a 3D geometric cache with a spatio-temporal diffusion model, ensuring structural consistency and textural re...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19678
β’ PDF: https://arxiv.org/pdf/2512.19678
β’ Project Page: https://hyokong.github.io/worldwarp-page/
β’ Github: https://hyokong.github.io/worldwarp-page/
πΉ Models citing this paper:
β’ https://huggingface.co/imsuperkong/worldwarp
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨Real2Edit2Real: Generating Robotic Demonstrations via a 3D Control Interface
π Summary:
A framework called Real2Edit2Real generates new manipulation demonstrations by using 3D reconstruction, editing, and video synthesis, improving data efficiency in robot learning. AI-generated summary ...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19402
β’ PDF: https://arxiv.org/pdf/2512.19402
β’ Github: https://real2edit2real.github.io/
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
A framework called Real2Edit2Real generates new manipulation demonstrations by using 3D reconstruction, editing, and video synthesis, improving data efficiency in robot learning. AI-generated summary ...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19402
β’ PDF: https://arxiv.org/pdf/2512.19402
β’ Github: https://real2edit2real.github.io/
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨Reasoning Palette: Modulating Reasoning via Latent Contextualization for Controllable Exploration for (V)LMs
π Summary:
Reasoning Palette enhances large language models by using a latent-modulation framework to guide internal planning and improve both inference and reinforcement learning performance. AI-generated summa...
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17206
β’ PDF: https://arxiv.org/pdf/2512.17206
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Reasoning Palette enhances large language models by using a latent-modulation framework to guide internal planning and improve both inference and reinforcement learning performance. AI-generated summa...
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17206
β’ PDF: https://arxiv.org/pdf/2512.17206
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
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β¨LoGoPlanner: Localization Grounded Navigation Policy with Metric-aware Visual Geometry
π Summary:
LoGoPlanner is an end-to-end navigation framework integrating localization, scene geometry, and policy conditioning. It provides implicit state estimation and dense environmental awareness, improving obstacle avoidance and outperforming oracle-localization baselines by over 27 percent.
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19629
β’ PDF: https://arxiv.org/pdf/2512.19629
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
LoGoPlanner is an end-to-end navigation framework integrating localization, scene geometry, and policy conditioning. It provides implicit state estimation and dense environmental awareness, improving obstacle avoidance and outperforming oracle-localization baselines by over 27 percent.
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19629
β’ PDF: https://arxiv.org/pdf/2512.19629
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€1
β¨Infinite-Homography as Robust Conditioning for Camera-Controlled Video Generation
π Summary:
InfCam generates high-fidelity videos with accurate camera poses by using infinite homography warping and augmenting synthetic datasets with diverse trajectories. AI-generated summary Recent progress ...
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/pdf/2512.17040
β’ PDF: https://arxiv.org/pdf/2512.17040
β’ Project Page: https://emjay73.github.io/InfCam/
β’ Github: https://github.com/emjay73/InfCam
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
InfCam generates high-fidelity videos with accurate camera poses by using infinite homography warping and augmenting synthetic datasets with diverse trajectories. AI-generated summary Recent progress ...
πΉ Publication Date: Published on Dec 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/pdf/2512.17040
β’ PDF: https://arxiv.org/pdf/2512.17040
β’ Project Page: https://emjay73.github.io/InfCam/
β’ Github: https://github.com/emjay73/InfCam
==================================
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β https://t.iss.one/DataScienceT
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β¨UCoder: Unsupervised Code Generation by Internal Probing of Large Language Models
π Summary:
IPC is an unsupervised framework that uses internal probing of large language models to generate code without labeled datasets, achieving competitive performance with reduced resource dependency. AI-g...
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17385
β’ PDF: https://arxiv.org/pdf/2512.17385
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
IPC is an unsupervised framework that uses internal probing of large language models to generate code without labeled datasets, achieving competitive performance with reduced resource dependency. AI-g...
πΉ Publication Date: Published on Dec 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.17385
β’ PDF: https://arxiv.org/pdf/2512.17385
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨Brain-Grounded Axes for Reading and Steering LLM States
π Summary:
Neurophysiological brain activity is used to create interpretable axes for large language models, enhancing their controllability and interpretability. AI-generated summary Interpretability methods fo...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19399
β’ PDF: https://arxiv.org/pdf/2512.19399
β’ Github: https://github.com/sandroandric/Brain-Grounded-Axes-for-Reading-and-Steering-LLM-States
β¨ Spaces citing this paper:
β’ https://huggingface.co/spaces/AI-nthusiast/cognitive-proxy
==================================
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β https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
Neurophysiological brain activity is used to create interpretable axes for large language models, enhancing their controllability and interpretability. AI-generated summary Interpretability methods fo...
πΉ Publication Date: Published on Dec 22
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.19399
β’ PDF: https://arxiv.org/pdf/2512.19399
β’ Github: https://github.com/sandroandric/Brain-Grounded-Axes-for-Reading-and-Steering-LLM-States
β¨ Spaces citing this paper:
β’ https://huggingface.co/spaces/AI-nthusiast/cognitive-proxy
==================================
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β https://t.iss.one/DataScienceT
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β¨Understanding Syllogistic Reasoning in LLMs from Formal and Natural Language Perspectives
π Summary:
This study explores syllogistic reasoning in LLMs, examining both symbolic inference and natural language understanding. Some models achieve perfect symbolic performance, leading to questions about whether LLMs are becoming more formal reasoning mechanisms.
πΉ Publication Date: Published on Dec 14
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.12620
β’ PDF: https://arxiv.org/pdf/2512.12620
β’ Github: https://github.com/XAheli/Logic-in-LLMs
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For more data science resources:
β https://t.iss.one/DataScienceT
#LLMs #SyllogisticReasoning #NaturalLanguageProcessing #AIResearch #FormalLogic
π Summary:
This study explores syllogistic reasoning in LLMs, examining both symbolic inference and natural language understanding. Some models achieve perfect symbolic performance, leading to questions about whether LLMs are becoming more formal reasoning mechanisms.
πΉ Publication Date: Published on Dec 14
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.12620
β’ PDF: https://arxiv.org/pdf/2512.12620
β’ Github: https://github.com/XAheli/Logic-in-LLMs
==================================
For more data science resources:
β https://t.iss.one/DataScienceT
#LLMs #SyllogisticReasoning #NaturalLanguageProcessing #AIResearch #FormalLogic
nature papers: 400$
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Doctoral thesis (complete) 500$
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paper simulation 150$
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