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

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🔹 Title: From Denoising to Refining: A Corrective Framework for Vision-Language Diffusion Model

🔹 Publication Date: Published on Oct 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.19871
• PDF: https://arxiv.org/pdf/2510.19871

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🔹 Title: Model Merging with Functional Dual Anchors

🔹 Publication Date: Published on Oct 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21223
• PDF: https://arxiv.org/pdf/2510.21223
• Project Page: https://spherelab.ai/fda/
• Github: https://github.com/Sphere-AI-Lab/fda/tree/main

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🔹 Title: A Definition of AGI

🔹 Publication Date: Published on Oct 21

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/a-definition-of-agi
• PDF: https://arxiv.org/pdf/2510.18212

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🔹 Title: Visual Diffusion Models are Geometric Solvers

🔹 Publication Date: Published on Oct 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21697
• PDF: https://arxiv.org/pdf/2510.21697
• Project Page: https://kariander1.github.io/visual-geo-solver/
• Github: https://github.com/kariander1/visual-geo-solver

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🔹 Title: AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suite

🔹 Publication Date: Published on Oct 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21652
• PDF: https://arxiv.org/pdf/2510.21652

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🔹 Title: Document Understanding, Measurement, and Manipulation Using Category Theory

🔹 Publication Date: Published on Oct 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21553
• PDF: https://arxiv.org/pdf/2510.21553

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🔹 Title: RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging

🔹 Publication Date: Published on Oct 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.20479
• PDF: https://arxiv.org/pdf/2510.20479

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🔹 Title: Map the Flow: Revealing Hidden Pathways of Information in VideoLLMs

🔹 Publication Date: Published on Oct 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.13251
• PDF: https://arxiv.org/pdf/2510.13251
• Project Page: https://map-the-flow.github.io
• Github: https://github.com/byminji/map-the-flow

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🔹 Title: RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via Data Alignment and Test-Time Scaling

🔹 Publication Date: Published on Oct 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.20206
• PDF: https://arxiv.org/pdf/2510.20206
• Project Page: https://whynothaha.github.io/RAPO_plus_github/
• Github: https://github.com/Vchitect/RAPO

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🔹 Title: Sparser Block-Sparse Attention via Token Permutation

🔹 Publication Date: Published on Oct 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21270
• PDF: https://arxiv.org/pdf/2510.21270
• Github: https://github.com/xinghaow99/pbs-attn

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🔹 Title: Taming Modality Entanglement in Continual Audio-Visual Segmentation

🔹 Publication Date: Published on Oct 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.17234
• PDF: https://arxiv.org/pdf/2510.17234

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🔹 Title: Reasoning with Sampling: Your Base Model is Smarter Than You Think

🔹 Publication Date: Published on Oct 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.14901
• PDF: https://arxiv.org/pdf/2510.14901
• Project Page: https://aakaran.github.io/reasoning_with_sampling/
• Github: https://github.com/aakaran/reasoning-with-sampling

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🔹 Title: Are Large Reasoning Models Good Translation Evaluators? Analysis and Performance Boost

🔹 Publication Date: Published on Oct 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.20780
• PDF: https://arxiv.org/pdf/2510.20780
• Github: https://github.com/NLP2CT/ThinMQM

🔹 Datasets citing this paper:
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🔹 Title: Sample By Step, Optimize By Chunk: Chunk-Level GRPO For Text-to-Image Generation

🔹 Publication Date: Published on Oct 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21583
• PDF: https://arxiv.org/pdf/2510.21583

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🔹 Title: PhysVLM-AVR: Active Visual Reasoning for Multimodal Large Language Models in Physical Environments

🔹 Publication Date: Published on Oct 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21111
• PDF: https://arxiv.org/pdf/2510.21111

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🔹 Title: Soft Instruction De-escalation Defense

🔹 Publication Date: Published on Oct 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21057
• PDF: https://arxiv.org/pdf/2510.21057

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🔹 Title: Foley Control: Aligning a Frozen Latent Text-to-Audio Model to Video

🔹 Publication Date: Published on Oct 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.21581
• PDF: https://arxiv.org/pdf/2510.21581
• Github: https://stability-ai.github.io/foleycontrol.github.io/

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🔹 Title: Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routers

🔹 Publication Date: Published on Oct 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.11370
• PDF: https://arxiv.org/pdf/2510.11370

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🔹 Title: ALICE-LRI: A General Method for Lossless Range Image Generation for Spinning LiDAR Sensors without Calibration Metadata

🔹 Publication Date: Published on Oct 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.20708
• PDF: https://arxiv.org/pdf/2510.20708
• Project Page: https://alice-lri.github.io/alice-lri/
• Github: https://github.com/alice-lri/alice-lri

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Forwarded from Machine Learning
Sepp Hochreiter, who invented LSTM 30+ year ago, gave a keynote talk at Neurips 2024 and introduced xLSTM (Extended Long Short-Term Memory).

I designed this Excel exercise to help you understand how xLSTM works.

More: https://www.byhand.ai/p/xlstm
🤖🧠 Qwen3-VL-8B-Instruct — The Next Generation of Vision-Language Intelligence by Qwen

🗓️ 27 Oct 2025
📚 AI News & Trends

In the rapidly evolving landscape of multimodal AI, Qwen3-VL-8B-Instruct stands out as a groundbreaking leap forward. Developed by Qwen, this model represents the most advanced vision-language (VL) system in the Qwen series to date. As artificial intelligence continues to bridge the gap between text and vision, Qwen3-VL-8B-Instruct emerges as a powerful engine capable of comprehending ...

#Qwen3VL #VisionLanguageAI #MultimodalAI #AISystems #QwenSeries #NextGenAI