🔹 Title: KORE: Enhancing Knowledge Injection for Large Multimodal Models via Knowledge-Oriented Augmentations and Constraints

🔹 Publication Date: Published on Oct 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.19316
• PDF: https://arxiv.org/pdf/2510.19316
• Project Page: https://kore-lmm.github.io/

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🔹 Title: FinSight: Towards Real-World Financial Deep Research

🔹 Publication Date: Published on Oct 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.16844
• PDF: https://arxiv.org/pdf/2510.16844

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🔹 Title: AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.18428
• PDF: https://arxiv.org/pdf/2510.18428

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🔹 Title: BAPO: Stabilizing Off-Policy Reinforcement Learning for LLMs via Balanced Policy Optimization with Adaptive Clipping

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.18927
• PDF: https://arxiv.org/pdf/2510.18927
• Project Page: https://github.com/WooooDyy/BAPO
• Github: https://github.com/WooooDyy/BAPO

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🔹 Title: ProfBench: Multi-Domain Rubrics requiring Professional Knowledge to Answer and Judge

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.18941
• PDF: https://arxiv.org/pdf/2510.18941

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

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🔹 Title: NeuroAda: Activating Each Neuron's Potential for Parameter-Efficient Fine-Tuning

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.18940
• PDF: https://arxiv.org/pdf/2510.18940
• Github: https://github.com/FightingFighting/NeuroAda.git

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🔹 Title: RIR-Mega: a large-scale simulated room impulse response dataset for machine learning and room acoustics modeling

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.18917
• PDF: https://arxiv.org/pdf/2510.18917
• Project Page: https://doi.org/10.5281/zenodo.17387402
• Github: https://github.com/mandip42/rirmega

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

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🔹 Title: Are they lovers or friends? Evaluating LLMs' Social Reasoning in English and Korean Dialogues

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.19028
• PDF: https://arxiv.org/pdf/2510.19028
• Github: https://github.com/rladmstn1714/SCRIPTS

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

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🔹 Title: Learning from the Best, Differently: A Diversity-Driven Rethinking on Data Selection

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.18909
• PDF: https://arxiv.org/pdf/2510.18909

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🔹 Title: When Do Transformers Learn Heuristics for Graph Connectivity?

🔹 Publication Date: Published on Oct 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.19753
• PDF: https://arxiv.org/pdf/2510.19753

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🔹 Title: Machine Text Detectors are Membership Inference Attacks

🔹 Publication Date: Published on Oct 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.19492
• PDF: https://arxiv.org/pdf/2510.19492
• Github: https://github.com/ryuryukke/mint

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🔹 Title: GigaBrain-0: A World Model-Powered Vision-Language-Action Model

🔹 Publication Date: Published on Oct 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.19430
• PDF: https://arxiv.org/pdf/2510.19430
• Project Page: https://gigabrain0.github.io/
• Github: https://github.com/open-gigaai/giga-brain-0

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🔹 Title: From Charts to Code: A Hierarchical Benchmark for Multimodal Models

🔹 Publication Date: Published on Oct 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.17932
• PDF: https://arxiv.org/pdf/2510.17932
• Project Page: https://csu-jpg.github.io/Chart2Code.github.io/
• Github: https://github.com/CSU-JPG/Chart2Code

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/CSU-JPG/Chart2Code

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🔹 Title: Attention Sinks in Diffusion Language Models

🔹 Publication Date: Published on Oct 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.15731
• PDF: https://arxiv.org/pdf/2510.15731

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🔹 Title: Directional Reasoning Injection for Fine-Tuning MLLMs

🔹 Publication Date: Published on Oct 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.15050
• PDF: https://arxiv.org/pdf/2510.15050
• Github: https://github.com/WikiChao/DRIFT

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🔹 Title: See the Text: From Tokenization to Visual Reading

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.18840
• PDF: https://arxiv.org/pdf/2510.18840

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🔹 Title: DeLeaker: Dynamic Inference-Time Reweighting For Semantic Leakage Mitigation in Text-to-Image Models

🔹 Publication Date: Published on Oct 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.15015
• PDF: https://arxiv.org/pdf/2510.15015

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🔹 Title: Language Models are Injective and Hence Invertible

🔹 Publication Date: Published on Oct 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.15511
• PDF: https://arxiv.org/pdf/2510.15511

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🔹 Title: Decomposed Attention Fusion in MLLMs for Training-Free Video Reasoning Segmentation

🔹 Publication Date: Published on Oct 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.19592
• PDF: https://arxiv.org/pdf/2510.19592
• Project Page: https://www.jshyun.me/projects/decaf
• Github: https://github.com/HYUNJS/DecAF

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🤖🧠 LangGraph by LangChain-AI: The Framework Powering Stateful, Long-Running AI Agents

🗓️ 23 Oct 2025
📚 AI News & Trends

As artificial intelligence continues to reshape industries, one major challenge remains building reliable, stateful and long-running AI agents that can handle complex workflows over time. Traditional AI frameworks often focus on short interactions, lacking the infrastructure to manage persistent states, durable memory or human feedback loops. That’s where LangGraph from LangChain-AI steps in. Trusted by ...

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🤖🧠 Master Machine Learning: Explore the Ultimate “Machine-Learning-Tutorials” Repository

🗓️ 23 Oct 2025
📚 AI News & Trends

In today’s data-driven world, Machine Learning (ML) has become the cornerstone of modern technology from intelligent chatbots to predictive analytics and recommendation systems. However, mastering ML isn’t just about coding, it requires a structured understanding of algorithms, statistics, optimization techniques and real-world problem-solving. That’s where Ujjwal Karn’s Machine-Learning-Tutorials GitHub repository stands out. This open-source, topic-wise ...

#MachineLearning #MLTutorials #ArtificialIntelligence #DataScience #OpenSource #AIEducation