🔹 Title: Agentic Reinforcement Learning for Search is Unsafe

🔹 Publication Date: Published on Oct 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.17431
• PDF: https://arxiv.org/pdf/2510.17431

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🔹 Title: QueST: Incentivizing LLMs to Generate Difficult Problems

🔹 Publication Date: Published on Oct 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.17715
• PDF: https://arxiv.org/pdf/2510.17715

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🤖🧠 Wan 2.1: Alibaba’s Open-Source Revolution in Video Generation

🗓️ 21 Oct 2025
📚 AI News & Trends

The landscape of artificial intelligence has been evolving rapidly, especially in the domain of video generation. Since OpenAI unveiled Sora in 2024, the world has witnessed an explosive surge in research and innovation within generative AI. However, most of these cutting-edge tools remained closed-source limiting transparency and accessibility. Recognizing this gap, Alibaba Group introduced Wan, ...

#Alibaba #Wan2.1 #VideoGeneration #GenerativeAI #OpenSource #ArtificialIntelligence

🤖🧠 DeepSeek-OCR: Redefining Document Understanding Through Optical Context Compression

🗓️ 21 Oct 2025
📚 AI News & Trends

In the age of large language models (LLMs) and vision-language models (VLMs), handling long and complex textual data efficiently remains a massive challenge. Traditional models struggle with processing extended contexts because the computational cost increases quadratically with sequence length. To overcome this, researchers from DeepSeek-AI have introduced a groundbreaking approach – DeepSeek-OCR, a model that ...

🔹 Title: Test-Time Scaling of Reasoning Models for Machine Translation

🔹 Publication Date: Published on Oct 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.06471
• PDF: https://arxiv.org/pdf/2510.06471

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🔹 Title: Beacon: Single-Turn Diagnosis and Mitigation of Latent Sycophancy in Large Language Models

🔹 Publication Date: Published on Oct 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.16727
• PDF: https://arxiv.org/pdf/2510.16727

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/sanskxr02/Beacon

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🔹 Title: Automated Composition of Agents: A Knapsack Approach for Agentic Component Selection

🔹 Publication Date: Published on Oct 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.16499
• PDF: https://arxiv.org/pdf/2510.16499

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🔹 Title: What Limits Agentic Systems Efficiency?

🔹 Publication Date: Published on Oct 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.16276
• PDF: https://arxiv.org/pdf/2510.16276

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🤖🧠 The Art of Scaling Reinforcement Learning Compute for LLMs: Top Insights from Meta, UT Austin and Harvard University

🗓️ 21 Oct 2025
📚 AI News & Trends

As Large Language Models (LLMs) continue to redefine artificial intelligence, a new research breakthrough has emerged from Meta, The University of Texas at Austin, University College London, UC Berkeley, Harvard University and Periodic Labs. Their paper, noscriptd “The Art of Scaling Reinforcement Learning Compute for LLMs,” introduces a transformative framework for understanding how reinforcement learning ...

#ReinforcementLearning #LLMs #AIResearch #Meta #UTAustin #HarvardUniversity

🤖🧠 Master Machine Learning with Stanford’s CS229 Cheatsheets: The Ultimate Learning Resource

🗓️ 21 Oct 2025
📚 AI News & Trends

Machine learning is one of the most transformative fields in technology today. From powering recommendation systems to enabling self-driving cars, machine learning is at the core of modern artificial intelligence. However, mastering its vast concepts, equations and algorithms can be overwhelming especially for beginners and busy professionals. That’s where the Stanford CS229 Machine Learning Cheatsheets ...

🔹 Title: TrajSelector: Harnessing Latent Representations for Efficient and Effective Best-of-N in Large Reasoning Model

🔹 Publication Date: Published on Oct 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.16449
• PDF: https://arxiv.org/pdf/2510.16449

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🔹 Title: AION-1: Omnimodal Foundation Model for Astronomical Sciences

🔹 Publication Date: Published on Oct 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.17960
• PDF: https://arxiv.org/pdf/2510.17960
• Github: https://github.com/PolymathicAI/AION

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🔹 Title: MoReBench: Evaluating Procedural and Pluralistic Moral Reasoning in Language Models, More than Outcomes

🔹 Publication Date: Published on Oct 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.16380
• PDF: https://arxiv.org/pdf/2510.16380
• Project Page: https://morebench.github.io/
• Github: https://github.com/morebench/morebench

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/morebench/morebench

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🔹 Title: Grasp Any Region: Towards Precise, Contextual Pixel Understanding for Multimodal LLMs

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.18876
• PDF: https://arxiv.org/pdf/2510.18876
• Github: https://github.com/Haochen-Wang409/Grasp-Any-Region

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🔹 Title: Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale Thinking Model

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.18855
• PDF: https://arxiv.org/pdf/2510.18855
• Github: https://github.com/inclusionAI/Ring-V2

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🔹 Title: IF-VidCap: Can Video Caption Models Follow Instructions?

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.18726
• PDF: https://arxiv.org/pdf/2510.18726
• Project Page: https://if-vidcap.github.io/
• Github: https://github.com/NJU-LINK/IF-VidCap

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🔹 Title: MoGA: Mixture-of-Groups Attention for End-to-End Long Video Generation

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.18692
• PDF: https://arxiv.org/pdf/2510.18692

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🔹 Title: MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues

🔹 Publication Date: Published on Oct 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.17722
• PDF: https://arxiv.org/pdf/2510.17722
• Project Page: https://mt-video-bench.github.io/
• Github: https://github.com/NJU-LINK/MT-Video-Bench

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/NJU-LINK/MT-Video-Bench

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🔹 Title: Chem-R: Learning to Reason as a Chemist

🔹 Publication Date: Published on Oct 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.16880
• PDF: https://arxiv.org/pdf/2510.16880
• Github: https://github.com/davidweidawang/Chem-R

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🔹 Title: UltraGen: High-Resolution Video Generation with Hierarchical Attention

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.18775
• PDF: https://arxiv.org/pdf/2510.18775
• Project Page: https://sjtuplayer.github.io/projects/UltraGen/

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🔹 Title: UniGenBench++: A Unified Semantic Evaluation Benchmark for Text-to-Image Generation

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.18701
• PDF: https://arxiv.org/pdf/2510.18701
• Project Page: https://codegoat24.github.io/UniGenBench/
• Github: https://github.com/CodeGoat24/UniGenBench

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