✨Pillar-0: A New Frontier for Radiology Foundation Models

📝 Summary:
Pillar-0 is a new radiology foundation model pretrained on diverse CT/MRI scans, utilizing RATE for scalable label extraction. It significantly outperforms existing models across various radiology tasks and extends to new applications like lung cancer risk prediction and brain hemorrhage detection.

🔹 Publication Date: Published on Nov 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17803
• PDF: https://arxiv.org/pdf/2511.17803
• Github: https://github.com/YalaLab/rate-evals

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#Radiology #FoundationModels #AI #MedicalImaging #MachineLearning

✨Multi-Agent Deep Research: Training Multi-Agent Systems with M-GRPO

📝 Summary:
Training multi-agent systems with distinct LLMs faces optimization challenges. M-GRPO, a hierarchical GRPO extension, addresses this by aligning heterogeneous trajectories and decoupling agent training. This improves stability and sample efficiency for tool-augmented reasoning tasks.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13288
• PDF: https://arxiv.org/pdf/2511.13288

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#MultiAgentSystems #ReinforcementLearning #DeepLearning #LLM #AI

✨Fidelity-Aware Recommendation Explanations via Stochastic Path Integration

📝 Summary:
SPINRec improves recommendation explanation fidelity by using stochastic path integration and baseline sampling, capturing both observed and unobserved interactions. It consistently outperforms prior methods, setting a new benchmark for faithful explainability in recommender systems.

🔹 Publication Date: Published on Nov 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18047
• PDF: https://arxiv.org/pdf/2511.18047

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

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#RecommenderSystems #ExplainableAI #MachineLearning #AI #DataScience

✨Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems

📝 Summary:
A Sparse Autoencoder extracts interaction-aware monosemantic concepts from recommender embeddings. Its prediction-aware training aligns these with model predictions, enabling controllable personalization and interpretability.

🔹 Publication Date: Published on Nov 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18024
• PDF: https://arxiv.org/pdf/2511.18024

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#RecommenderSystems #DeepLearning #AI #Interpretability #Personalization

✨MIST: Mutual Information Via Supervised Training

📝 Summary:
MIST is a data-driven neural network that estimates mutual information. Trained on synthetic data, it uses attention and quantile regression for uncertainty. It outperforms classical methods, offering faster and more reliable MI estimation.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18945
• PDF: https://arxiv.org/pdf/2511.18945
• Github: https://github.com/grgera/mist

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#MutualInformation #NeuralNetworks #MachineLearning #DeepLearning #DataScience

✨AICC: Parse HTML Finer, Make Models Better -- A 7.3T AI-Ready Corpus Built by a Model-Based HTML Parser

📝 Summary:
This paper introduces MinerU-HTML, a novel language model-based HTML parser that semantically extracts web content, preserving structure better than heuristic methods. It constructs the 7.3T AICC corpus, demonstrating that models trained on AICC significantly outperform those from other parsers, ...

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16397
• PDF: https://arxiv.org/pdf/2511.16397

✨ Datasets citing this paper:
• https://huggingface.co/datasets/opendatalab/AICC

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

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#AI #HTMLParsing #Corpus #LanguageModels #WebData

✨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

💸 PacketSDK--A New Way To Make Revenue From Your Apps

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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