πΉ Title: Knowledge-based Visual Question Answer with Multimodal Processing, Retrieval and Filtering
πΉ Publication Date: Published on Oct 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.14605
β’ PDF: https://arxiv.org/pdf/2510.14605
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πΉ Publication Date: Published on Oct 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.14605
β’ PDF: https://arxiv.org/pdf/2510.14605
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πΉ Title: AsyncVoice Agent: Real-Time Explanation for LLM Planning and Reasoning
πΉ Publication Date: Published on Oct 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.16156
β’ PDF: https://arxiv.org/pdf/2510.16156
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.16156
β’ PDF: https://arxiv.org/pdf/2510.16156
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πΉ Title: Glyph: Scaling Context Windows via Visual-Text Compression
πΉ Publication Date: Published on Oct 20
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.17800
β’ PDF: https://arxiv.org/pdf/2510.17800
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 20
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.17800
β’ PDF: https://arxiv.org/pdf/2510.17800
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πΉ Title: Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback
πΉ Publication Date: Published on Oct 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.16888
β’ PDF: https://arxiv.org/pdf/2510.16888
β’ Github: https://github.com/PKU-YuanGroup/UniWorld-V2
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.16888
β’ PDF: https://arxiv.org/pdf/2510.16888
β’ Github: https://github.com/PKU-YuanGroup/UniWorld-V2
πΉ Datasets citing this paper:
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πΉ Title: Annotation-Efficient Universal Honesty Alignment
πΉ Publication Date: Published on Oct 20
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.17509
β’ PDF: https://arxiv.org/pdf/2510.17509
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 20
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.17509
β’ PDF: https://arxiv.org/pdf/2510.17509
πΉ Datasets citing this paper:
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πΉ Title: RL makes MLLMs see better than SFT
πΉ Publication Date: Published on Oct 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.16333
β’ PDF: https://arxiv.org/pdf/2510.16333
β’ Project Page: https://june-page.github.io/pivot/
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.16333
β’ PDF: https://arxiv.org/pdf/2510.16333
β’ Project Page: https://june-page.github.io/pivot/
πΉ Datasets citing this paper:
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πΉ Title: UltraCUA: A Foundation Model for Computer Use Agents with Hybrid Action
πΉ Publication Date: Published on Oct 20
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.17790
β’ PDF: https://arxiv.org/pdf/2510.17790
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 20
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.17790
β’ PDF: https://arxiv.org/pdf/2510.17790
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πΉ Title: Embody 3D: A Large-scale Multimodal Motion and Behavior Dataset
πΉ Publication Date: Published on Oct 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.16258
β’ PDF: https://arxiv.org/pdf/2510.16258
β’ Project Page: https://www.meta.com/emerging-tech/codec-avatars/embody-3d/
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.16258
β’ PDF: https://arxiv.org/pdf/2510.16258
β’ Project Page: https://www.meta.com/emerging-tech/codec-avatars/embody-3d/
πΉ Datasets citing this paper:
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β€1
πΉ Title: When to Ensemble: Identifying Token-Level Points for Stable and Fast LLM Ensembling
πΉ Publication Date: Published on Oct 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.15346
β’ PDF: https://arxiv.org/pdf/2510.15346
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.15346
β’ PDF: https://arxiv.org/pdf/2510.15346
πΉ Datasets citing this paper:
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πΉ Title: Constantly Improving Image Models Need Constantly Improving Benchmarks
πΉ Publication Date: Published on Oct 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.15021
β’ PDF: https://arxiv.org/pdf/2510.15021
β’ Project Page: https://echo-bench.github.io/
β’ Github: https://github.com/para-lost/ECHO
πΉ Datasets citing this paper:
β’ https://huggingface.co/datasets/echo-bench/echo2025
πΉ Spaces citing this paper:
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πΉ Publication Date: Published on Oct 16
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.15021
β’ PDF: https://arxiv.org/pdf/2510.15021
β’ Project Page: https://echo-bench.github.io/
β’ Github: https://github.com/para-lost/ECHO
πΉ Datasets citing this paper:
β’ https://huggingface.co/datasets/echo-bench/echo2025
πΉ Spaces citing this paper:
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πΉ Title: Foundational Automatic Evaluators: Scaling Multi-Task Generative Evaluator Training for Reasoning-Centric Domains
πΉ Publication Date: Published on Oct 20
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.17793
β’ PDF: https://arxiv.org/pdf/2510.17793
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 20
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.17793
β’ PDF: https://arxiv.org/pdf/2510.17793
πΉ Datasets citing this paper:
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πΉ Title: Chronos-2: From Univariate to Universal Forecasting
πΉ Publication Date: Published on Oct 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.15821
β’ PDF: https://arxiv.org/pdf/2510.15821
β’ Github: https://github.com/amazon-science/chronos-forecasting
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.15821
β’ PDF: https://arxiv.org/pdf/2510.15821
β’ Github: https://github.com/amazon-science/chronos-forecasting
πΉ Datasets citing this paper:
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πΉ Title: GuideFlow3D: Optimization-Guided Rectified Flow For Appearance Transfer
πΉ Publication Date: Published on Oct 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.16136
β’ PDF: https://arxiv.org/pdf/2510.16136
β’ Github: https://github.com/GradientSpaces/GuideFlow3D
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.16136
β’ PDF: https://arxiv.org/pdf/2510.16136
β’ Github: https://github.com/GradientSpaces/GuideFlow3D
πΉ Datasets citing this paper:
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πΉ Title: MultiVerse: A Multi-Turn Conversation Benchmark for Evaluating Large Vision and Language Models
πΉ Publication Date: Published on Oct 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.16641
β’ PDF: https://arxiv.org/pdf/2510.16641
β’ Github: https://github.com/passing2961/MultiVerse
πΉ Datasets citing this paper:
β’ https://huggingface.co/datasets/passing2961/MultiVerse
πΉ Spaces citing this paper:
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πΉ Publication Date: Published on Oct 18
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.16641
β’ PDF: https://arxiv.org/pdf/2510.16641
β’ Github: https://github.com/passing2961/MultiVerse
πΉ Datasets citing this paper:
β’ https://huggingface.co/datasets/passing2961/MultiVerse
πΉ Spaces citing this paper:
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πΉ Title: On Non-interactive Evaluation of Animal Communication Translators
πΉ Publication Date: Published on Oct 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.15768
β’ PDF: https://arxiv.org/pdf/2510.15768
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 17
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.15768
β’ PDF: https://arxiv.org/pdf/2510.15768
πΉ Datasets citing this paper:
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πΉ Title: Agentic Reinforcement Learning for Search is Unsafe
πΉ Publication Date: Published on Oct 20
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.17431
β’ PDF: https://arxiv.org/pdf/2510.17431
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 20
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.17431
β’ PDF: https://arxiv.org/pdf/2510.17431
πΉ Datasets citing this paper:
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πΉ Title: QueST: Incentivizing LLMs to Generate Difficult Problems
πΉ Publication Date: Published on Oct 20
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.17715
β’ PDF: https://arxiv.org/pdf/2510.17715
πΉ Datasets citing this paper:
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πΉ Publication Date: Published on Oct 20
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.17715
β’ PDF: https://arxiv.org/pdf/2510.17715
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β€1
π€π§ Wan 2.1: Alibabaβs Open-Source Revolution in Video Generation
ποΈ 21 Oct 2025
π 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
ποΈ 21 Oct 2025
π 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
β€1
π€π§ DeepSeek-OCR: Redefining Document Understanding Through Optical Context Compression
ποΈ 21 Oct 2025
π 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 β DeepSeek-OCR, a model that ...
ποΈ 21 Oct 2025
π 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 β DeepSeek-OCR, a model that ...
πΉ Title: Test-Time Scaling of Reasoning Models for Machine Translation
πΉ Publication Date: Published on Oct 7
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.06471
β’ PDF: https://arxiv.org/pdf/2510.06471
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πΉ Publication Date: Published on Oct 7
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.06471
β’ PDF: https://arxiv.org/pdf/2510.06471
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πΉ Title: Beacon: Single-Turn Diagnosis and Mitigation of Latent Sycophancy in Large Language Models
πΉ Publication Date: Published on Oct 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.16727
β’ PDF: https://arxiv.org/pdf/2510.16727
πΉ Datasets citing this paper:
β’ https://huggingface.co/datasets/sanskxr02/Beacon
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πΉ Publication Date: Published on Oct 19
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.16727
β’ PDF: https://arxiv.org/pdf/2510.16727
πΉ Datasets citing this paper:
β’ https://huggingface.co/datasets/sanskxr02/Beacon
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β€1