✨Evaluating Parameter Efficient Methods for RLVR

📝 Summary:
This work evaluates 12 PEFT methods for RLVR in mathematical reasoning, challenging LoRAs default use. It finds that structural variants like DoRA outperform LoRA, while SVD-informed methods fail and extreme parameter reduction bottlenecks reasoning.

🔹 Publication Date: Published on Dec 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23165
• PDF: https://arxiv.org/pdf/2512.23165

==================================

For more data science resources:
✓ https://t.me/DataScienceT

#PEFT #RLVR #MathematicalReasoning #LoRA #DeepLearning

✨UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement

📝 Summary:
UltraShape 1.0 is a 3D diffusion framework that generates high-fidelity shapes using a two-stage process: coarse then refined geometry. It includes a novel data pipeline improving dataset quality, enabling strong geometric results on public data.

🔹 Publication Date: Published on Dec 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21185
• PDF: https://arxiv.org/pdf/2512.21185
• Project Page: https://pku-yuangroup.github.io/UltraShape-1.0/
• Github: https://pku-yuangroup.github.io/UltraShape-1.0/

🔹 Models citing this paper:
• https://huggingface.co/infinith/UltraShape

==================================

For more data science resources:
✓ https://t.me/DataScienceT

#3DGeneration #DiffusionModels #GenerativeAI #ComputerGraphics #DeepLearning

✨CosineGate: Semantic Dynamic Routing via Cosine Incompatibility in Residual Networks

📝 Summary:
CosineGate enables dynamic routing in residual networks using cosine incompatibility to skip redundant blocks. This reduces computation by up to 28.5 percent while matching or exceeding ResNet-20 accuracy, without auxiliary supervision.

🔹 Publication Date: Published on Dec 21, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22206
• PDF: https://arxiv.org/pdf/2512.22206
• Github: https://github.com/thotayogeswarreddy/CosineGate

==================================

For more data science resources:
✓ https://t.me/DataScienceT

#DeepLearning #NeuralNetworks #DynamicRouting #ModelEfficiency #AIResearch

✨Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models

📝 Summary:
Youtu-LLM is a lightweight 1.96B LLM, pre-trained from scratch with a compact architecture and a multi-stage curriculum focused on commonsense, STEM, and agentic tasks. It achieves state-of-the-art performance for sub-2B models, demonstrating strong intrinsic agentic capabilities.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24618
• PDF: https://arxiv.org/pdf/2512.24618

==================================

For more data science resources:
✓ https://t.me/DataScienceT

#LLM #AI #AgenticAI #LightweightLLM #DeepLearning

✨SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and Time

📝 Summary:
SpaceTimePilot is a video diffusion model for dynamic scene rendering, offering independent control over spatial viewpoint and temporal motion. It achieves precise space-time disentanglement via a time-embedding, temporal-warping training, and a synthetic dataset.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.25075
• PDF: https://arxiv.org/pdf/2512.25075
• Project Page: https://zheninghuang.github.io/Space-Time-Pilot/

==================================

For more data science resources:
✓ https://t.me/DataScienceT

#VideoDiffusion #GenerativeAI #DynamicScenes #ComputerGraphics #DeepLearning

✨Geometry-Aware Optimization for Respiratory Sound Classification: Enhancing Sensitivity with SAM-Optimized Audio Spectrogram Transformers

📝 Summary:
This paper improves respiratory sound classification using AST enhanced with SAM. It optimizes loss surface geometry for flatter minima, yielding state-of-the-art 68.10% score and crucial 68.31% sensitivity on ICBHI 2017.

🔹 Publication Date: Published on Dec 27, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22564
• PDF: https://arxiv.org/pdf/2512.22564

==================================

For more data science resources:
✓ https://t.me/DataScienceT

#RespiratoryHealth #MedicalAI #DeepLearning #SoundClassification #AIHealthcare

✨mHC: Manifold-Constrained Hyper-Connections

📝 Summary:
Manifold-Constrained Hyper-Connections mHC resolve training instability and scalability issues of Hyper-Connections HC. mHC restores identity mapping via manifold projection and infrastructure optimization, enabling effective large-scale training with improved performance.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24880
• PDF: https://arxiv.org/pdf/2512.24880

==================================

For more data science resources:
✓ https://t.me/DataScienceT

#MachineLearning #DeepLearning #NeuralNetworks #ManifoldLearning #AI

✨Kronos: A Foundation Model for the Language of Financial Markets

📝 Summary:
Kronos is a novel foundation model for financial K-line data. It uses a specialized tokenizer and autoregressive pre-training on a vast dataset to significantly outperform existing models in price and volatility forecasting, and synthetic data generation, establishing it as a versatile tool for f...

🔹 Publication Date: Published on Aug 2, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.02739
• PDF: https://arxiv.org/pdf/2508.02739
• Github: https://github.com/shiyu-coder/Kronos

🔹 Models citing this paper:
• https://huggingface.co/NeoQuasar/Kronos-base
• https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base
• https://huggingface.co/NeoQuasar/Kronos-mini

✨ Spaces citing this paper:
• https://huggingface.co/spaces/ByronWang2005/Kronos-CS2-Skins-Forecast-Demo
• https://huggingface.co/spaces/yangyang158/kronos
• https://huggingface.co/spaces/heyunfei/crypt

==================================

For more data science resources:
✓ https://t.me/DataScienceT

#FoundationModel #FinancialAI #DeepLearning #QuantitativeFinance #Forecasting

✨Guiding a Diffusion Transformer with the Internal Dynamics of Itself

📝 Summary:
This paper introduces Internal Guidance IG for diffusion models, which adds auxiliary supervision to intermediate layers during training and extrapolates outputs during sampling. This simple strategy significantly improves training efficiency and generation quality. IG achieves state-of-the-art F...

🔹 Publication Date: Published on Dec 30, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24176
• PDF: https://arxiv.org/pdf/2512.24176
• Project Page: https://zhouxingyu13.github.io/Internal-Guidance/
• Github: https://github.com/CVL-UESTC/Internal-Guidance

==================================

For more data science resources:
✓ https://t.me/DataScienceT

#DiffusionModels #AI #DeepLearning #GenerativeAI #ComputerVision

✨FlowBlending: Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Video Generation

📝 Summary:
FlowBlending optimizes video generation by adapting model capacity to each stage. It uses large models for critical early and late timesteps, and small models for intermediate ones. This achieves faster inference and fewer FLOPs with no loss in large model fidelity.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24724
• PDF: https://arxiv.org/pdf/2512.24724

==================================

For more data science resources:
✓ https://t.me/DataScienceT

#VideoGeneration #GenerativeAI #DeepLearning #AIResearch #ModelOptimization