🔹 Title: Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback

🔹 Publication Date: Published on Oct 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.16888
• PDF: https://arxiv.org/pdf/2510.16888
• Github: https://github.com/PKU-YuanGroup/UniWorld-V2

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🔹 Title: Annotation-Efficient Universal Honesty Alignment

🔹 Publication Date: Published on Oct 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.17509
• PDF: https://arxiv.org/pdf/2510.17509

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🔹 Title: RL makes MLLMs see better than SFT

🔹 Publication Date: Published on Oct 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.16333
• PDF: https://arxiv.org/pdf/2510.16333
• Project Page: https://june-page.github.io/pivot/

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🔹 Title: UltraCUA: A Foundation Model for Computer Use Agents with Hybrid Action

🔹 Publication Date: Published on Oct 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.17790
• PDF: https://arxiv.org/pdf/2510.17790

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🔹 Title: Embody 3D: A Large-scale Multimodal Motion and Behavior Dataset

🔹 Publication Date: Published on Oct 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.16258
• PDF: https://arxiv.org/pdf/2510.16258
• Project Page: https://www.meta.com/emerging-tech/codec-avatars/embody-3d/

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🔹 Title: When to Ensemble: Identifying Token-Level Points for Stable and Fast LLM Ensembling

🔹 Publication Date: Published on Oct 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.15346
• PDF: https://arxiv.org/pdf/2510.15346

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🔹 Title: Constantly Improving Image Models Need Constantly Improving Benchmarks

🔹 Publication Date: Published on Oct 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.15021
• PDF: https://arxiv.org/pdf/2510.15021
• Project Page: https://echo-bench.github.io/
• Github: https://github.com/para-lost/ECHO

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/echo-bench/echo2025

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🔹 Title: Foundational Automatic Evaluators: Scaling Multi-Task Generative Evaluator Training for Reasoning-Centric Domains

🔹 Publication Date: Published on Oct 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.17793
• PDF: https://arxiv.org/pdf/2510.17793

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🔹 Title: Chronos-2: From Univariate to Universal Forecasting

🔹 Publication Date: Published on Oct 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.15821
• PDF: https://arxiv.org/pdf/2510.15821
• Github: https://github.com/amazon-science/chronos-forecasting

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🔹 Title: GuideFlow3D: Optimization-Guided Rectified Flow For Appearance Transfer

🔹 Publication Date: Published on Oct 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.16136
• PDF: https://arxiv.org/pdf/2510.16136
• Github: https://github.com/GradientSpaces/GuideFlow3D

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🔹 Title: MultiVerse: A Multi-Turn Conversation Benchmark for Evaluating Large Vision and Language Models

🔹 Publication Date: Published on Oct 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.16641
• PDF: https://arxiv.org/pdf/2510.16641
• Github: https://github.com/passing2961/MultiVerse

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/passing2961/MultiVerse

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🔹 Title: On Non-interactive Evaluation of Animal Communication Translators

🔹 Publication Date: Published on Oct 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.15768
• PDF: https://arxiv.org/pdf/2510.15768

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🔹 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