ML Research Hub
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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.

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πŸ”Ή Title: Track, Inpaint, Resplat: Subject-driven 3D and 4D Generation with Progressive Texture Infilling

πŸ”Ή Publication Date: Published on Oct 27

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.23605
β€’ PDF: https://arxiv.org/pdf/2510.23605

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πŸ”Ή Title: VoMP: Predicting Volumetric Mechanical Property Fields

πŸ”Ή Publication Date: Published on Oct 27

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.22975
β€’ PDF: https://arxiv.org/pdf/2510.22975
β€’ Project Page: https://research.nvidia.com/labs/sil/projects/vomp

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πŸ”Ή Title: A Survey of Data Agents: Emerging Paradigm or Overstated Hype?

πŸ”Ή Publication Date: Published on Oct 27

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.23587
β€’ PDF: https://arxiv.org/pdf/2510.23587
β€’ Github: https://github.com/HKUSTDial/awesome-data-agents

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πŸ”Ή Title: Code Aesthetics with Agentic Reward Feedback

πŸ”Ή Publication Date: Published on Oct 27

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.23272
β€’ PDF: https://arxiv.org/pdf/2510.23272
β€’ Project Page: https://bangx7.github.io/code-aesthetics/

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πŸ”Ή Title: Memory-based Language Models: An Efficient, Explainable, and Eco-friendly Approach to Large Language Modeling

πŸ”Ή Publication Date: Published on Oct 25

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.22317
β€’ PDF: https://arxiv.org/pdf/2510.22317
β€’ Github: https://github.com/antalvdb/olifant

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πŸ”Ή Title: Mitigating Attention Sinks and Massive Activations in Audio-Visual Speech Recognition with LLMS

πŸ”Ή Publication Date: Published on Oct 26

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.22603
β€’ PDF: https://arxiv.org/pdf/2510.22603
β€’ Github: https://github.com/umbertocappellazzo/Llama-AVSR

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πŸ€–πŸ§  Free for 1 Year: ChatGPT Go’s Big Move in India

πŸ—“οΈ 28 Oct 2025
πŸ“š AI News & Trends

On 28 October 2025, OpenAI announced that its mid-tier subscription plan, ChatGPT Go, will be available free for one full year in India starting from 4 November. (www.ndtv.com) What is ChatGPT Go? What’s the deal? Why this matters ? Things to check / caveats What should users do? Broader implications This move by OpenAI indicates ...

#ChatGPTGo #OpenAI #India #FreeAccess #ArtificialIntelligence #TechNews
πŸ”Ή Title: The Best of N Worlds: Aligning Reinforcement Learning with Best-of-N Sampling via max@k Optimisation

πŸ”Ή Publication Date: Published on Oct 27

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.23393
β€’ PDF: https://arxiv.org/pdf/2510.23393

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πŸ”Ή Title: EchoDistill: Bidirectional Concept Distillation for One-Step Diffusion Personalization

πŸ”Ή Publication Date: Published on Oct 23

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.20512
β€’ PDF: https://arxiv.org/pdf/2510.20512

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πŸ”Ή Title: DiffusionLane: Diffusion Model for Lane Detection

πŸ”Ή Publication Date: Published on Oct 25

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.22236
β€’ PDF: https://arxiv.org/pdf/2510.22236

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πŸ”Ή Title: Scaling Laws for Deepfake Detection

πŸ”Ή Publication Date: Published on Oct 18

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.16320
β€’ PDF: https://arxiv.org/pdf/2510.16320

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πŸ”Ή Title: SyncHuman: Synchronizing 2D and 3D Generative Models for Single-view Human Reconstruction

πŸ”Ή Publication Date: Published on Oct 9

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.07723
β€’ PDF: https://arxiv.org/pdf/2510.07723

πŸ”Ή Datasets citing this paper:
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πŸ”Ή Title: Once Upon an Input: Reasoning via Per-Instance Program Synthesis

πŸ”Ή Publication Date: Published on Oct 26

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.22849
β€’ PDF: https://arxiv.org/pdf/2510.22849
β€’ Github: https://github.com/adaminsky/pips

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πŸ”Ή Title: Open Multimodal Retrieval-Augmented Factual Image Generation

πŸ”Ή Publication Date: Published on Oct 26

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.22521
β€’ PDF: https://arxiv.org/pdf/2510.22521
β€’ Project Page: https://tyangjn.github.io/orig.github.io/
β€’ Github: https://github.com/TyangJN/ORIG

πŸ”Ή Datasets citing this paper:
β€’ https://huggingface.co/datasets/TyangJN/FIG

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πŸ‘1
πŸ”Ή Title: FlowOpt: Fast Optimization Through Whole Flow Processes for Training-Free Editing

πŸ”Ή Publication Date: Published on Oct 24

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.22010
β€’ PDF: https://arxiv.org/pdf/2510.22010
β€’ Project Page: https://orronai.github.io/FlowOpt/
β€’ Github: https://github.com/orronai/FlowOpt

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πŸ€–πŸ§  Agent Lightning By Microsoft: Reinforcement Learning Framework to Train Any AI Agent

πŸ—“οΈ 28 Oct 2025
πŸ“š Agentic AI

Artificial Intelligence (AI) is rapidly moving from static models to intelligent agents capable of reasoning, adapting, and performing complex, real-world tasks. However, training these agents effectively remains a major challenge. Most frameworks today tightly couple the agent’s logic with training processes making it hard to scale or transfer across use cases. Enter Agent Lightning, a ...

#AgentLightning #Microsoft #ReinforcementLearning #AIAgents #ArtificialIntelligence #MachineLearning
πŸ€–πŸ§  PandasAI: Transforming Data Analysis with Conversational Artificial Intelligence

πŸ—“οΈ 28 Oct 2025
πŸ“š AI News & Trends

In a world dominated by data, the ability to analyze and interpret information efficiently has become a core competitive advantage. From business intelligence dashboards to large-scale machine learning models, data-driven decision-making fuels innovation across industries. Yet, for most people, data analysis remains a technical challenge requiring coding expertise, statistical knowledge and familiarity with libraries like ...

#PandasAI #ConversationalAI #DataAnalysis #ArtificialIntelligence #DataScience #MachineLearning
πŸ”Ή Title: MergeMix: A Unified Augmentation Paradigm for Visual and Multi-Modal Understanding

πŸ”Ή Publication Date: Published on Oct 27

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.23479
β€’ PDF: https://arxiv.org/pdf/2510.23479
β€’ Github: https://github.com/Westlake-AI/openmixup

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πŸ”Ή Title: Multi-Agent Evolve: LLM Self-Improve through Co-evolution

πŸ”Ή Publication Date: Published on Oct 27

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.23595
β€’ PDF: https://arxiv.org/pdf/2510.23595

πŸ”Ή Datasets citing this paper:
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πŸ”Ή Title: Sprint: Sparse-Dense Residual Fusion for Efficient Diffusion Transformers

πŸ”Ή Publication Date: Published on Oct 24

πŸ”Ή Paper Links:
β€’ arXiv Page: https://arxiv.org/abs/2510.21986
β€’ PDF: https://arxiv.org/pdf/2510.21986

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πŸ€–πŸ§  Microsoft Data Formulator: Revolutionizing AI-Powered Data Visualization

πŸ—“οΈ 28 Oct 2025
πŸ“š AI News & Trends

In today’s data-driven world, visualization is everything. Whether you’re a business analyst, data scientist or researcher, the ability to convert raw data into meaningful visuals can define the success of your decisions. That’s where Microsoft’s Data Formulator steps in a cutting-edge, open-source platform designed to empower analysts to create rich, AI-assisted visualizations effortlessly. Developed by ...

#Microsoft #DataVisualization #AI #DataScience #OpenSource #Analytics