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

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CoDance: An Unbind-Rebind Paradigm for Robust Multi-Subject Animation

📝 Summary:
CoDance introduces an Unbind-Rebind framework for animating multiple subjects with flexible spatial configurations, using pose shift encoding and semantic/textual guidance for motion reassignment. AI-...

🔹 Publication Date: Published on Jan 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11096
• PDF: https://arxiv.org/pdf/2601.11096
• Project Page: https://lucaria-academy.github.io/CoDance/

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#AI #DataScience #MachineLearning #HuggingFace #Research
EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning

📝 Summary:
EverMemOS is a self-organizing memory system for LLMs that processes dialogue into structured memory cells and scenes. This enhances long-term interaction and reasoning, achieving state-of-the-art performance.

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02163
• PDF: https://arxiv.org/pdf/2601.02163
• Github: https://github.com/EverMind-AI/EverMemOS

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#AI #DataScience #MachineLearning #HuggingFace #Research
InfiAgent: An Infinite-Horizon Framework for General-Purpose Autonomous Agents

📝 Summary:
InfiAgent is a framework that maintains bounded reasoning context for long-horizon tasks by externalizing persistent state into a file-centric abstraction, enabling stable performance without task-spe...

🔹 Publication Date: Published on Jan 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03204
• PDF: https://arxiv.org/pdf/2601.03204
• Github: https://github.com/ChenglinPoly/infiAgent

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#AI #DataScience #MachineLearning #HuggingFace #Research
MemOS: A Memory OS for AI System

📝 Summary:
MemOS is a memory operating system for LLMs that unifies plaintext, activation-based, and parameter-level memories. It treats memory as a system resource, using MemCubes for efficient storage, retrieval, and enabling continual learning and personalized modeling.

🔹 Publication Date: Published on Jul 4, 2025

🔹 Paper Links:
• arXiv Page: https://arxivlens.com/PaperView/Details/memos-a-memory-os-for-ai-system-4846-c5e0c676
• PDF: https://arxiv.org/pdf/2507.03724
• Project Page: https://memos.openmem.net/
• Github: https://github.com/MemTensor/MemOS

🔹 Models citing this paper:
https://huggingface.co/kagvi13/HMP

Datasets citing this paper:
https://huggingface.co/datasets/MemTensor/MemOS_eval_result

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#AI #LLMs #MemoryOS #ContinualLearning #SystemDesign
1
SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature

📝 Summary:
SIN-Bench and the Fish-in-the-Ocean paradigm evaluate multimodal models by requiring explicit cross-modal evidence chains in scientific documents. This new method shows grounding is a bottleneck, revealing a gap between answer correctness and traceable support.

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10108
• PDF: https://arxiv.org/pdf/2601.10108

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#MultimodalAI #Benchmarking #ScientificLiterature #AIResearch #Grounding
1
Spurious Rewards Paradox: Mechanistically Understanding How RLVR Activates Memorization Shortcuts in LLMs

📝 Summary:
Spurious rewards in RLVR trigger a memorization shortcut in LLMs, causing a paradox where models bypass reasoning. Researchers identified a hidden neural Anchor-Adapter circuit enabling this shortcut. This discovery offers a way to mitigate data contamination in RLVR models.

🔹 Publication Date: Published on Jan 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11061
• PDF: https://arxiv.org/pdf/2601.11061
• Github: https://github.com/idwts/How-RLVR-Activates-Memorization-Shortcuts

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#LLMs #ReinforcementLearning #SpuriousRewards #AIResearch #DataContamination
1
YaPO: Learnable Sparse Activation Steering Vectors for Domain Adaptation

📝 Summary:
YaPO learns sparse steering vectors for LLMs using Sparse Autoencoders, enabling more effective and stable control than dense methods. This leads to disentangled, interpretable directions for fine-grained alignment across various behaviors, without degrading general knowledge. YaPO offers a gener...

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08441
• PDF: https://arxiv.org/pdf/2601.08441
• Project Page: https://mbzuai-paris.github.io/YaPO/
• Github: https://github.com/MBZUAI-Paris/YaPO

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#LLMs #DomainAdaptation #SparseLearning #MachineLearning #AI
1
Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation

📝 Summary:
Medical SAM3 is a foundation model for universal prompt-driven medical image segmentation. It fine-tunes the general SAM3 on diverse medical datasets to overcome domain shifts. This provides robust, flexible segmentation across modalities and structures.

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10880
• PDF: https://arxiv.org/pdf/2601.10880
• Project Page: https://chongcongjiang.github.io/MedicalSAM3/
• Github: https://github.com/AIM-Research-Lab/Medical-SAM3.git

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#MedicalAI #ImageSegmentation #FoundationModels #DeepLearning #ComputerVision
2
CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion

📝 Summary:
CLARE enables robots to continually learn new tasks without forgetting, using lightweight adapters. It autonomously expands these adapters and dynamically routes them, ensuring high performance without needing task labels or storing past data.

🔹 Publication Date: Published on Jan 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09512
• PDF: https://arxiv.org/pdf/2601.09512
• Project Page: https://tum-lsy.github.io/clare/
• Github: https://github.com/utiasDSL/clare

Datasets citing this paper:
https://huggingface.co/datasets/continuallearning/libero_10_image_task_0

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#ContinualLearning #Robotics #AI #MachineLearning #VLAModels
2
PubMed-OCR: PMC Open Access OCR Annotations

📝 Summary:
PubMed-OCR is a corpus of 209.5K scientific articles from PubMed Central with Google Cloud Vision OCR annotations. It provides word, line, and paragraph bounding boxes to support layout-aware modeling and OCR evaluation. This data is publicly released.

🔹 Publication Date: Published on Jan 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11425
• PDF: https://arxiv.org/pdf/2601.11425

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#OCR #Dataset #ComputerVision #MachineLearning #DataScience
3
NAACL: Noise-AwAre Verbal Confidence Calibration for LLMs in RAG Systems

📝 Summary:
LLMs in RAG systems exhibit poor confidence calibration due to noisy contexts. This paper proposes NAACL, a noise-aware calibration framework. NAACL uses new rules and supervised fine-tuning to make LLMs intrinsically aware of noisy input, significantly improving confidence calibration.

🔹 Publication Date: Published on Jan 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11004
• PDF: https://arxiv.org/pdf/2601.11004
• Github: https://github.com/HKUST-KnowComp/NAACL

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#LLMs #RAG #ConfidenceCalibration #NLP #AI
3
PaddleOCR 3.0 Technical Report

📝 Summary:
PaddleOCR 3.0 is an open-source toolkit offering efficient OCR and document parsing solutions. Its models achieve competitive accuracy and efficiency with fewer than 100 million parameters, rivaling much larger vision-language models.

🔹 Publication Date: Published on Jul 8, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.05595
• PDF: https://huggingface.co/collections/PaddlePaddle/pp-structurev3

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#AI #DataScience #MachineLearning #HuggingFace #Research
1
Multi-Agent Collaboration via Evolving Orchestration

📝 Summary:
A centralized orchestrator, trained with reinforcement learning, dynamically directs LLM agents for multi-agent collaboration. This puppeteer-style method achieves superior performance and reduced computational costs.

🔹 Publication Date: Published on May 26, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.19591
• PDF: https://arxiv.org/pdf/2505.19591
• Github: https://github.com/OpenBMB/ChatDev/tree/puppeteer

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#AI #DataScience #MachineLearning #HuggingFace #Research
MemoryRewardBench: Benchmarking Reward Models for Long-Term Memory Management in Large Language Models

📝 Summary:
MemoryRewardBench is a new benchmark evaluating reward models ability to assess long-term memory management in LLMs across various context lengths and patterns. Evaluations reveal newer RMs outperform predecessors, open-source models are closing the gap, and current RMs have limitations.

🔹 Publication Date: Published on Jan 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11969
• PDF: https://arxiv.org/pdf/2601.11969
• Github: https://github.com/LCM-Lab/MemRewardBench

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#AI #DataScience #MachineLearning #HuggingFace #Research
UniX: Unifying Autoregression and Diffusion for Chest X-Ray Understanding and Generation

📝 Summary:
UniX presents a unified medical foundation model that decouples visual understanding and generation tasks using distinct autoregressive and diffusion branches with cross-modal attention for enhanced p...

🔹 Publication Date: Published on Jan 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11522
• PDF: https://arxiv.org/pdf/2601.11522
• Github: https://github.com/ZrH42/UniX

🔹 Models citing this paper:
https://huggingface.co/ZrH42/UniX

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#AI #DataScience #MachineLearning #HuggingFace #Research
Aligning Agentic World Models via Knowledgeable Experience Learning

📝 Summary:
WorldMind addresses LLM physical hallucinations by autonomously building a symbolic world knowledge repository. It unifies process and goal experiences to enforce physical feasibility and task optimality, achieving superior performance and transferability.

🔹 Publication Date: Published on Jan 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13247
• PDF: https://arxiv.org/pdf/2601.13247
• Project Page: https://zjunlp.github.io/project/WorldMind/
• Github: https://github.com/zjunlp/WorldMind

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#AI #DataScience #MachineLearning #HuggingFace #Research
Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey

📝 Summary:
Large language models face significant challenges in software issue resolution, prompting the development of autonomous coding agents through various training-free and training-based methodologies. AI...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11655
• PDF: https://arxiv.org/pdf/2601.11655
• Project Page: https://deepsoftwareanalytics.github.io/Awesome-Issue-Resolution/
• Github: https://github.com/DeepSoftwareAnalytics/Awesome-Issue-Resolution

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#AI #DataScience #MachineLearning #HuggingFace #Research
A BERTology View of LLM Orchestrations: Token- and Layer-Selective Probes for Efficient Single-Pass Classification

📝 Summary:
Lightweight probes trained on hidden states of LLMs enable efficient classification tasks without additional computational overhead, improving safety and sentiment analysis performance. AI-generated s...

🔹 Publication Date: Published on Jan 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13288
• PDF: https://arxiv.org/pdf/2601.13288

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#AI #DataScience #MachineLearning #HuggingFace #Research
Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization

📝 Summary:
Being-H0.5 is a Vision-Language-Action model that enables robust cross-embodiment generalization through human-centric learning and a Mixture-of-Transformers architecture with specialized embodiment h...

🔹 Publication Date: Published on Jan 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2601.12993
• PDF: https://arxiv.org/pdf/2601.12993
• Project Page: https://research.beingbeyond.com/being-h05
• Github: https://github.com/BeingBeyond/Being-H

🔹 Models citing this paper:
https://huggingface.co/BeingBeyond/Being-H05-2B
https://huggingface.co/BeingBeyond/Being-H05-2B_libero
https://huggingface.co/BeingBeyond/Being-H05-2B_robocasa

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#AI #DataScience #MachineLearning #HuggingFace #Research