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: Unified Reinforcement and Imitation Learning for Vision-Language Models

🔹 Publication Date: Published on Oct 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.19307
• PDF: https://arxiv.org/pdf/2510.19307
• Project Page: https://byungkwanlee.github.io/RIL-page/
• Github: https://byungkwanlee.github.io/RIL-page/

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🔹 Title: MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models

🔹 Publication Date: Published on Oct 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.19457
• PDF: https://arxiv.org/pdf/2510.19457
• Project Page: https://mined-lmm.github.io/

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🔹 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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