✨RynnBrain: Open Embodied Foundation Models

📝 Summary:
RynnBrain is an open-source spatiotemporal foundation model for embodied intelligence that unifies perception, reasoning, and planning capabilities across multiple scales and task-specific variants. A...

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14979
• PDF: https://arxiv.org/pdf/2602.14979
• Project Page: https://alibaba-damo-academy.github.io/RynnBrain.github.io/
• Github: https://github.com/alibaba-damo-academy/RynnBrain

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Empty Shelves or Lost Keys? Recall Is the Bottleneck for Parametric Factuality

📝 Summary:
LLMs demonstrate near-complete factual encoding but struggle with retrieval accessibility, where errors stem from access limitations rather than knowledge gaps, with reasoning improving recall of enco...

🔹 Publication Date: Published on Feb 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14080
• PDF: https://arxiv.org/pdf/2602.14080

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Optimizing Few-Step Generation with Adaptive Matching Distillation

📝 Summary:
Adaptive Matching Distillation AMD improves generative model training by detecting and escaping unstable optimization regions. It uses reward proxies to correct trajectories, boosting sample fidelity and training robustness across generation tasks.

🔹 Publication Date: Published on Feb 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07345
• PDF: https://arxiv.org/pdf/2602.07345

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨BiManiBench: A Hierarchical Benchmark for Evaluating Bimanual Coordination of Multimodal Large Language Models

📝 Summary:
BiManiBench evaluates multimodal large language models on bimanual robotic tasks, revealing limitations in spatial grounding and control despite strong high-level reasoning capabilities. AI-generated ...

🔹 Publication Date: Published on Feb 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08392
• PDF: https://arxiv.org/pdf/2602.08392
• Project Page: https://bimanibench.github.io/
• Github: https://github.com/bimanibench/BiManiBench

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Visual Memory Injection Attacks for Multi-Turn Conversations

📝 Summary:
Visual Memory Injection VMI covertly manipulates generative vision-language models using images. These images trigger specific manipulative responses only with certain prompts in multi-turn conversations, showing large-scale user manipulation is feasible.

🔹 Publication Date: Published on Feb 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.15927
• PDF: https://arxiv.org/pdf/2602.15927
• Github: https://github.com/chs20/visual-memory-injection

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

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

#VMI #VisionLanguageModels #AISecurity #AIManipulation #GenerativeAI

✨Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents

📝 Summary:
Agent S2 is a new compositional framework for computer use agents. It uses Mixture-of-Grounding and Proactive Hierarchical Planning to achieve state-of-the-art performance across various benchmarks and operating systems, significantly improving automation.

🔹 Publication Date: Published on Apr 1, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.00906
• PDF: https://arxiv.org/pdf/2504.00906
• Project Page: https://www.simular.ai/articles/agent-s2-technical-review
• Github: https://github.com/simular-ai/Agent-S

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

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

#AI #AIagents #Automation #MachineLearning #ComputerScience

✨MAEB: Massive Audio Embedding Benchmark

📝 Summary:
MAEB is a large-scale audio benchmark evaluating 50+ models across 30 diverse tasks. No single model dominates; strengths vary significantly between speech and environmental sound tasks. Performance on MAEB highly correlates with audio large language model performance.

🔹 Publication Date: Published on Feb 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16008
• PDF: https://arxiv.org/pdf/2602.16008
• Project Page: https://mteb-leaderboard.hf.space/?benchmark_name=MAEB%28beta%29

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

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

#AudioAI #Benchmarking #AudioEmbeddings #SpeechProcessing #AudioLLMs

✨CADEvolve: Creating Realistic CAD via Program Evolution

📝 Summary:
CADEvolve presents an evolution-based pipeline using VLM-guided edits to generate complex CAD programs from simple primitives. It creates a large dataset of 1.3 million scripts, enabling fine-tuned VLMs to achieve state-of-the-art Image2CAD performance.

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16317
• PDF: https://arxiv.org/pdf/2602.16317
• Github: https://github.com/zhemdi/CADEvolve

🔹 Models citing this paper:
• https://huggingface.co/kulibinai/cadevolve-rl1

✨ Datasets citing this paper:
• https://huggingface.co/datasets/kulibinai/cadevolve

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

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

#CAD #ProgramEvolution #VLMs #Image2CAD #AI

✨Reinforced Fast Weights with Next-Sequence Prediction

📝 Summary:
REFINE is an RL framework that improves fast weight models for long-context tasks. It uses next-sequence prediction NSP instead of next-token prediction, enhancing long-range dependency capture. Experiments show it consistently outperforms supervised fine-tuning.

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16704
• PDF: https://arxiv.org/pdf/2602.16704
• Github: https://github.com/princetonvisualai/ReFINE/

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

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

#ReinforcementLearning #MachineLearning #DeepLearning #NLP #AI

✨Learning Personalized Agents from Human Feedback

📝 Summary:
PAHF enables AI agents to continually personalize through explicit user memory and dual feedback. It rapidly adapts to changing user preferences by integrating pre-action clarification and post-action updates, significantly reducing personalization error and improving learning speed.

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16173
• PDF: https://arxiv.org/pdf/2602.16173
• Project Page: https://personalized-ai.github.io/
• Github: https://github.com/facebookresearch/PAHF

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

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

#AI #Personalization #HumanAIInteraction #MachineLearning #AIAgents

✨OPBench: A Graph Benchmark to Combat the Opioid Crisis

📝 Summary:
OPBench is the first comprehensive graph benchmark to systematically evaluate graph learning methods for the opioid crisis. It includes five diverse datasets across three domains and a unified framework, providing insights for future research.

🔹 Publication Date: Published on Feb 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14602
• PDF: https://arxiv.org/pdf/2602.14602
• Github: https://github.com/Tianyi-Billy-Ma/OPBench

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

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

#OpioidCrisis #GraphLearning #DataScience #MachineLearning #PublicHealth

✨Unified Latents (UL): How to train your latents

📝 Summary:
Unified Latents UL learns joint latent representations using diffusion prior regularization and decoding. It achieves competitive FID of 1.4 on ImageNet-512 with fewer training FLOPs and sets a new state of the art FVD of 1.3 on Kinetics-600.

🔹 Publication Date: Published on Feb 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.17270
• PDF: https://arxiv.org/pdf/2602.17270

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

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

#GenerativeAI #DiffusionModels #LatentSpace #ImageGeneration #VideoGeneration

✨Arcee Trinity Large Technical Report

📝 Summary:
Arcee Trinity introduces sparse Mixture-of-Experts models Nano, Mini, Large with up to 400B total parameters. They feature advanced attention, novel normalization, and sigmoid MoE routing, trained on massive token datasets.

🔹 Publication Date: Published on Feb 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.17004
• PDF: https://arxiv.org/pdf/2602.17004

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

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

#MixtureOfExperts #LargeLanguageModels #SparseModels #DeepLearning #AI

✨TactAlign: Human-to-Robot Policy Transfer via Tactile Alignment

📝 Summary:
TactAlign transfers human tactile demonstrations to robots with different embodiments. It aligns human and robot tactile signals into a shared latent space without paired data, improving policy transfer for contact-rich tasks and enabling zero-shot transfer.

🔹 Publication Date: Published on Feb 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.13579
• PDF: https://arxiv.org/pdf/2602.13579
• Project Page: https://yswi.github.io/tactalign/

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

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

#Robotics #TactileRobotics #PolicyTransfer #HRI #AI

✨Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5

📝 Summary:
This report assesses frontier AI risks, updating granular scenarios for cyber offense, manipulation, deception, uncontrolled AI R&D, and self-replication. It also proposes robust mitigation strategies for secure deployment of advanced AI systems.

🔹 Publication Date: Published on Feb 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14457
• PDF: https://arxiv.org/pdf/2602.14457
• Project Page: https://ai45lab.github.io/safeworkf1-page/

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨SpargeAttention2: Trainable Sparse Attention via Hybrid Top-k+Top-p Masking and Distillation Fine-Tuning

📝 Summary:
A trainable sparse attention method called SpargeAttention2 is proposed that achieves high sparsity in diffusion models while maintaining generation quality through hybrid masking rules and distillati...

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.13515
• PDF: https://arxiv.org/pdf/2602.13515

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents

📝 Summary:
GUI-Owl-1.5 is a multi-platform GUI agent model with varying sizes that achieves superior performance across GUI automation, grounding, tool-calling, and memory tasks through innovations in data pipel...

🔹 Publication Date: Published on Feb 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16855
• PDF: https://arxiv.org/pdf/2602.16855
• Project Page: https://github.com/X-PLUG/MobileAgent/tree/main/Mobile-Agent-v3.5
• Github: https://github.com/X-PLUG/MobileAgent

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨DDiT: Dynamic Patch Scheduling for Efficient Diffusion Transformers

📝 Summary:
Dynamic tokenization improves diffusion transformer efficiency by adjusting patch sizes based on content complexity and denoising timestep, achieving significant speedup without quality loss. AI-gener...

🔹 Publication Date: Published on Feb 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16968
• PDF: https://arxiv.org/pdf/2602.16968

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Computer-Using World Model

📝 Summary:
A world model for desktop software that predicts UI state changes through textual description followed by visual synthesis, improving decision quality and execution robustness in computer-using tasks....

🔹 Publication Date: Published on Feb 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.17365
• PDF: https://arxiv.org/pdf/2602.17365

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨2Mamba2Furious: Linear in Complexity, Competitive in Accuracy

📝 Summary:
Researchers enhance linear attention by simplifying Mamba-2 and improving its architectural components to achieve near-softmax accuracy while maintaining memory efficiency for long sequences. AI-gener...

🔹 Publication Date: Published on Feb 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.17363
• PDF: https://arxiv.org/pdf/2602.17363
• Github: https://github.com/gmongaras/2Mamba2Furious

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Discovering Multiagent Learning Algorithms with Large Language Models

📝 Summary:
AlphaEvolve, an evolutionary coding agent using large language models, automatically discovers new multiagent learning algorithms for imperfect-information games by evolving regret minimization and po...

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16928
• PDF: https://arxiv.org/pdf/2602.16928

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research