✨UI-S1: Advancing GUI Automation via Semi-online Reinforcement Learning

📝 Summary:
UI-S1 introduces Semi-online RL for GUI automation, simulating online RL on offline trajectories to overcome current method limitations. It achieved SOTA performance on dynamic benchmarks, bridging offline training efficiency and online reasoning.

🔹 Publication Date: Published on Sep 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.11543
• PDF: https://arxiv.org/pdf/2509.11543
• Github: https://github.com/X-PLUG/MobileAgent/tree/main/UI-S1

🔹 Models citing this paper:
• https://huggingface.co/mPLUG/UI-S1-7B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/mPLUG/UI_S1_dataset

==================================

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#ReinforcementLearning #GUIAutomation #AI #MachineLearning #IntelligentAgents

✨PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC

📝 Summary:
PC-Agent is a hierarchical multi-agent framework improving MLLM-based GUI agents for complex PC tasks. It uses an Active Perception Module and a hierarchical decision-making architecture with Manager, Progress, and Decision agents. A Reflection agent provides feedback. It achieved a 32% task succ...

🔹 Publication Date: Published on Feb 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.14282
• PDF: https://arxiv.org/pdf/2502.14282
• Github: https://github.com/X-PLUG/MobileAgent/tree/main/PC-Agent

✨ Spaces citing this paper:
• https://huggingface.co/spaces/junyangwang0410/PC-Agent

==================================

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#MultiAgentSystems #AIAgents #MLLMs #PCAutomation #DeepLearning

✨Look Before You Leap: A GUI-Critic-R1 Model for Pre-Operative Error Diagnosis in GUI Automation

📝 Summary:
GUI automation faces critical errors. This paper introduces GUI-Critic-R1, a pre-operative critic model using Suggestion-aware Gradient Relative Policy Optimization, to provide feedback and diagnose errors before execution. It significantly improves critic accuracy, enhancing automation reliabili...

🔹 Publication Date: Published on Jun 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.04614
• PDF: https://arxiv.org/pdf/2506.04614
• Github: https://github.com/X-PLUG/MobileAgent

🔹 Models citing this paper:
• https://huggingface.co/BonnieOne/GUI-Critic-R1

==================================

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#GUIAutomation #ErrorDiagnosis #AI #MachineLearning #SoftwareTesting

✨Mobile-Agent-v3: Foundamental Agents for GUI Automation

📝 Summary:
GUI-Owl and Mobile-Agent-v3 are open-source GUI agent models achieving state-of-the-art performance on GUI benchmarks. GUI-Owl introduces large-scale environment infrastructure, diverse agent capabilities, and scalable reinforcement learning, with Mobile-Agent-v3 further improving these results.

🔹 Publication Date: Published on Aug 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15144
• PDF: https://arxiv.org/pdf/2508.15144
• Project Page: https://github.com/X-PLUG/MobileAgent
• Github: https://github.com/X-PLUG/MobileAgent

🔹 Models citing this paper:
• https://huggingface.co/mPLUG/GUI-Owl-7B
• https://huggingface.co/mPLUG/GUI-Owl-32B
• https://huggingface.co/mPLUG/GUI-Owl-7B-Desktop-RL

==================================

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#GUIAgent #Automation #ReinforcementLearning #AIResearch #OpenSourceAI

✨MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection

📝 Summary:
MSRNet proposes a Multi-Scale Recursive Network for camouflaged object detection. It uses a Pyramid Vision Transformer and recursive feature refinement to overcome challenges with small and multiple objects, achieving state-of-the-art results.

🔹 Publication Date: Published on Nov 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12810
• PDF: https://arxiv.org/pdf/2511.12810

🔹 Models citing this paper:
• https://huggingface.co/linaa98/MSRNet

==================================

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#CamouflagedObjectDetection #ObjectDetection #ComputerVision #DeepLearning #AIResearch

✨Representational Stability of Truth in Large Language Models

📝 Summary:
This paper introduces representational stability to assess how robustly LLMs encode truth. It found that stability is more influenced by epistemic familiarity than linguistic form. Unfamiliar statements cause larger shifts in truth judgments, highlighting a diagnostic for auditing LLMs.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19166
• PDF: https://arxiv.org/pdf/2511.19166

✨ Datasets citing this paper:
• https://huggingface.co/datasets/carlomarxx/trilemma-of-truth
• https://huggingface.co/datasets/samanthadies/representational_stability

==================================

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#LLMs #RepresentationalStability #AITruthfulness #AISafety #AIResearch

✨Upsample Anything: A Simple and Hard to Beat Baseline for Feature Upsampling

📝 Summary:
Upsample Anything is a novel test-time optimization framework that enhances low-resolution features to high-resolution outputs without training. It learns an anisotropic Gaussian kernel per image, acting as a universal edge-aware operator. This method achieves state-of-the-art results in tasks li...

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16301
• PDF: https://arxiv.org/pdf/2511.16301

==================================

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#Upsampling #ComputerVision #ImageProcessing #DeepLearning #AI

✨EvoVLA: Self-Evolving Vision-Language-Action Model

📝 Summary:
EvoVLA is a self-supervised VLA framework tackling stage hallucination in long-horizon robotic manipulation. It uses triplet contrastive learning, pose-based exploration, and memory to prevent shortcuts. EvoVLA significantly improves success, sample efficiency, and reduces hallucination in sim an...

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16166
• PDF: https://arxiv.org/pdf/2511.16166
• Project Page: https://aigeeksgroup.github.io/EvoVLA/
• Github: https://aigeeksgroup.github.io/EvoVLA/

==================================

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#Robotics #VisionLanguageAction #SelfSupervisedLearning #AI #DeepLearning

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✨One4D: Unified 4D Generation and Reconstruction via Decoupled LoRA Control

📝 Summary:
One4D is a unified framework for 4D generation and reconstruction, producing synchronized RGB frames and pointmaps. It uses Unified Masked Conditioning for varying input sparsities and Decoupled LoRA Control to achieve high-quality results across diverse tasks.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18922
• PDF: https://arxiv.org/pdf/2511.18922
• Project Page: https://mizhenxing.github.io/One4D
• Github: https://mizhenxing.github.io/One4D

==================================

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#4DGeneration #4DReconstruction #ComputerVision #DeepLearning #GenerativeAI

✨Beyond Multiple Choice: Verifiable OpenQA for Robust Vision-Language RFT

📝 Summary:
ReVeL converts multiple-choice questions to verifiable open-form questions to address unreliable MCQA metrics and answer guessing. This framework improves data efficiency and robustness for multimodal language models, revealing significant score inflation in MCQA benchmarks.

🔹 Publication Date: Published on Nov 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17405
• PDF: https://arxiv.org/pdf/2511.17405
• Github: https://flageval-baai.github.io/ReVeL/

==================================

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#OpenQA #VisionLanguage #LanguageModels #AIEvaluation #MachineLearning

✨Language Model Council: Benchmarking Foundation Models on Highly Subjective Tasks by Consensus

📝 Summary:
Benchmarking LLMs on subjective tasks like emotional intelligence is challenging. The Language Model Council LMC uses a democratic process with 20 LLMs to formulate, administer, and evaluate tests. This yields more robust, less biased rankings that align better with human leaderboards.

🔹 Publication Date: Published on Jun 12, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2406.08598
• PDF: https://arxiv.org/pdf/2406.08598
• Github: https://github.com/llm-council/llm-council

✨ Datasets citing this paper:
• https://huggingface.co/datasets/llm-council/emotional_application

✨ Spaces citing this paper:
• https://huggingface.co/spaces/llm-council/llm-council
• https://huggingface.co/spaces/llm-council/sandbox

==================================

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#LLM #Benchmarking #AIEvaluation #FoundationModels #ConsensusAI

✨SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation

📝 Summary:
SteadyDancer is an Image-to-Video framework that solves identity drift and motion control challenges in human image animation. It achieves robust first-frame preservation via condition reconciliation, adaptive pose, and hierarchical training, outperforming others while using fewer resources.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19320
• PDF: https://arxiv.org/pdf/2511.19320
• Project Page: https://mcg-nju.github.io/steadydancer-web
• Github: https://github.com/MCG-NJU/SteadyDancer

🔹 Models citing this paper:
• https://huggingface.co/MCG-NJU/SteadyDancer-14B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/MCG-NJU/X-Dance

==================================

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#HumanImageAnimation #ImageToVideo #FirstFramePreservation #GenerativeAI #ComputerVision

✨GigaWorld-0: World Models as Data Engine to Empower Embodied AI

📝 Summary:
GigaWorld-0 is a unified world model framework that generates high-quality, diverse, and physically plausible VLA data by integrating video and 3D modeling. This synthetic data enables embodied AI models to achieve strong real-world performance on physical robots without any real-world training.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19861
• PDF: https://arxiv.org/pdf/2511.19861

==================================

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#EmbodiedAI #WorldModels #SyntheticData #AI #Robotics

✨Unified all-atom molecule generation with neural fields

📝 Summary:
FuncBind uses neural fields and computer vision models to generate diverse all-atom molecules across various systems, from small molecules to antibodies. This modality-agnostic framework achieves competitive performance in structure-conditioned molecular design and can generate novel binders.

🔹 Publication Date: Published on Nov 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15906
• PDF: https://arxiv.org/pdf/2511.15906
• Github: https://github.com/prescient-design/funcbind/

🔹 Models citing this paper:
• https://huggingface.co/mkirchmeyer/funcbind

==================================

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#MoleculeGeneration #NeuralFields #DrugDiscovery #AIforScience #ComputationalChemistry

✨Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward

📝 Summary:
UniSandbox evaluates Unified Multimodal Models, revealing a gap between understanding and generation in reasoning and knowledge transfer. Chain-of-Thought and self-training effectively bridge this gap, providing insights for future model design.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20561
• PDF: https://arxiv.org/pdf/2511.20561
• Github: https://github.com/PKU-YuanGroup/UniSandBox

==================================

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#MultimodalAI #AIUnderstanding #ChainOfThought #LLMs #AIResearch

✨MedSAM3: Delving into Segment Anything with Medical Concepts

📝 Summary:
MedSAM-3 is a text-promptable medical segmentation model fine-tuned on SAM 3 using semantic conceptual labels. It enables precise, open-vocabulary text-based segmentation of anatomical structures and integrates MLLMs for advanced reasoning. This approach significantly outperforms existing models ...

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19046
• PDF: https://arxiv.org/pdf/2511.19046
• Github: https://github.com/Joey-S-Liu/MedSAM3

==================================

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#MedicalAI #ImageSegmentation #DeepLearning #MLLMs #FoundationModels

✨HunyuanOCR Technical Report

📝 Summary:
HunyuanOCR is a lightweight Vision-Language Model for OCR, using a unified end-to-end architecture ViT + LLM. It achieves state-of-the-art performance in diverse tasks, outperforming larger models and commercial APIs, powered by data-driven and RL strategies.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19575
• PDF: https://arxiv.org/pdf/2511.19575
• Github: https://github.com/Tencent-Hunyuan/HunyuanOCR

==================================

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#OCR #VisionLanguageModel #LLM #AI #MachineLearning

✨iMontage: Unified, Versatile, Highly Dynamic Many-to-many Image Generation

📝 Summary:
iMontage repurposes pre-trained video models to generate high-quality, diverse image sets. It uses a unified framework and minimal adaptation, combining temporal coherence with image diversity for natural transitions and expanded dynamics across many tasks.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20635
• PDF: https://arxiv.org/pdf/2511.20635
• Project Page: https://kr1sjfu.github.io/iMontage-web/

==================================

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#ImageGeneration #DeepLearning #ComputerVision #AIMethods #VideoModels

✨PhysChoreo: Physics-Controllable Video Generation with Part-Aware Semantic Grounding

📝 Summary:
PhysChoreo generates physically realistic and controllable videos from a single image. It reconstructs part-aware physical properties and simulates dynamic behavior, outperforming existing methods.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20562
• PDF: https://arxiv.org/pdf/2511.20562

==================================

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#VideoGeneration #PhysicalSimulation #ComputerVision #DeepLearning #AIResearch