✨Agents of Chaos

📝 Summary:
Autonomous language-model-powered agents in a live laboratory environment exhibited numerous security and governance vulnerabilities including unauthorized actions, information disclosure, and system ...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20021
• PDF: https://arxiv.org/pdf/2602.20021

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨tttLRM: Test-Time Training for Long Context and Autoregressive 3D Reconstruction

📝 Summary:
A novel 3D reconstruction model called tttLRM uses a Test-Time Training layer to enable efficient, scalable autoregressive reconstruction with linear complexity, achieving better results than existing...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20160
• PDF: https://arxiv.org/pdf/2602.20160
• Project Page: https://cwchenwang.github.io/tttLRM
• Github: https://cwchenwang.github.io/tttLRM/

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨A Very Big Video Reasoning Suite

📝 Summary:
A large-scale video reasoning dataset and benchmark are introduced to study video intelligence capabilities beyond visual quality, enabling systematic analysis of spatiotemporal reasoning and generali...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20159
• PDF: https://arxiv.org/pdf/2602.20159
• Project Page: https://video-reason.com/

🔹 Models citing this paper:
• https://huggingface.co/Video-Reason/VBVR-Wan2.2

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Video-Reason/VBVR-Bench-Data

✨ Spaces citing this paper:
• https://huggingface.co/spaces/Video-Reason/VBVR-Bench-Leaderboard

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning

📝 Summary:
A hybrid knowledge-injection framework combines general reasoning large language models with time-series LLMs through reinforcement learning-based verifiable rewards to enhance time-series diagnostic ...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19455
• PDF: https://arxiv.org/pdf/2602.19455

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨K-Search: LLM Kernel Generation via Co-Evolving Intrinsic World Model

📝 Summary:
K-Search uses a co-evolving world model to optimize GPU kernels by separating high-level planning from low-level implementation, achieving significant performance improvements over existing evolutiona...

🔹 Publication Date: Published on Feb 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19128
• PDF: https://arxiv.org/pdf/2602.19128

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting

📝 Summary:
AAVGen is a generative AI framework that designs AAV capsids with improved traits through protein language models, supervised fine-tuning, and reinforcement learning techniques. AI-generated summary A...

🔹 Publication Date: Published on Feb 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18915
• PDF: https://arxiv.org/pdf/2602.18915
• Github: https://github.com/mohammad-gh009/AAVGen

🔹 Models citing this paper:
• https://huggingface.co/Moreza009/AAV-Kidney-Tropism
• https://huggingface.co/Moreza009/AAV-Thermostability
• https://huggingface.co/Moreza009/AAV-Fitness

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Moreza009/AAV_datasets

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SkillOrchestra: Learning to Route Agents via Skill Transfer

📝 Summary:
SkillOrchestra is a skill-aware orchestration framework for compound AI systems that routes agents efficiently. It learns fine-grained skills and models agent competence and cost, selecting agents based on inferred skill demands and a performance-cost trade-off. This approach achieves superior re...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19672
• PDF: https://arxiv.org/pdf/2602.19672
• Github: https://github.com/jiayuww/SkillOrchestra

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#AI #DataScience #MachineLearning #HuggingFace #Research

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✨AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer

📝 Summary:
AssetFormer is an autoregressive Transformer model generating modular 3D assets from text descriptions. It adapts language model techniques to handle design constraints and enhance asset quality. This streamlines asset creation for professional development and user-generated content.

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12100
• PDF: https://arxiv.org/pdf/2602.12100
• Github: https://github.com/Advocate99/AssetFormer

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#3DGeneration #GenerativeAI #Transformer #AI3D #DeepLearning

✨ManCAR: Manifold-Constrained Latent Reasoning with Adaptive Test-Time Computation for Sequential Recommendation

📝 Summary:
ManCAR prevents latent drift in sequential recommendation by constraining latent reasoning to a collaborative manifold derived from user interactions. This grounds reasoning in a feasible space, improving accuracy and using adaptive test-time computation.

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20093
• PDF: https://arxiv.org/pdf/2602.20093
• Github: https://github.com/FuCongResearchSquad/ManCAR

✨ Datasets citing this paper:
• https://huggingface.co/datasets/PIIR/ManCAR

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#SequentialRecommendation #MachineLearning #RecommenderSystems #AIResearch #DataScience

✨Learning Cross-View Object Correspondence via Cycle-Consistent Mask Prediction

📝 Summary:
This paper presents a conditional binary segmentation framework for robust cross-view object correspondence. It uses cycle-consistency training to create view-invariant representations without ground-truth annotations. This approach achieves state-of-the-art performance on relevant benchmarks.

🔹 Publication Date: Published on Feb 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18996
• PDF: https://arxiv.org/pdf/2602.18996
• Github: https://github.com/shannany0606/CCMP

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#ComputerVision #MachineLearning #ObjectCorrespondence #ImageSegmentation #SelfSupervisedLearning

✨SimVLA: A Simple VLA Baseline for Robotic Manipulation

📝 Summary:
SimVLA introduces a streamlined baseline for Vision-Language-Action models. It achieves state-of-the-art performance with only 0.5 billion parameters, outperforming larger models on simulations and matching real-robot results. This simple design provides a clearer, reproducible reference for VLA ...

🔹 Publication Date: Published on Feb 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18224
• PDF: https://arxiv.org/pdf/2602.18224
• Project Page: https://frontierrobo.github.io/SimVLA
• Github: https://github.com/LUOyk1999/SimVLA

🔹 Models citing this paper:
• https://huggingface.co/YuankaiLuo/SimVLA-LIBERO

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#VLA #Robotics #AI #MachineLearning #DeepLearning

✨VLANeXt: Recipes for Building Strong VLA Models

📝 Summary:
This paper systematically analyzes Vision-Language-Action VLA models through a unified framework, distilling 12 key design principles. The resulting VLANeXt model achieves superior performance on benchmarks and strong real-world generalization.

🔹 Publication Date: Published on Feb 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18532
• PDF: https://arxiv.org/pdf/2602.18532
• Project Page: https://dravenalg.github.io/VLANeXt/
• Github: https://github.com/DravenALG/awesome-vla

🔹 Models citing this paper:
• https://huggingface.co/DravenALG/VLANeXt

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#VLANeXt #VLAModels #ComputerVision #Robotics #AIResearch

✨RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning

📝 Summary:
RoboCurate enhances synthetic robot learning data by evaluating action quality through simulator replay consistency. It also augments observation diversity via image editing and video transfer techniques. This leads to substantial improvements in robot task success rates compared to using real da...

🔹 Publication Date: Published on Feb 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18742
• PDF: https://arxiv.org/pdf/2602.18742
• Project Page: https://seungkukim.github.io/robocurate/

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#RobotLearning #Robotics #SyntheticData #DataAugmentation #AI

✨Nacrith: Neural Lossless Compression via Ensemble Context Modeling and High-Precision CDF Coding

📝 Summary:
Nacrith is a lossless compression system combining an LLM, lightweight predictors, and arithmetic coding. It introduces innovations like enhanced CDF precision and a hybrid binary format, achieving state-of-the-art compression efficiency, significantly outperforming existing methods on various da...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19626
• PDF: https://arxiv.org/pdf/2602.19626
• Project Page: https://github.com/robtacconelli/Nacrith-GPU
• Github: https://github.com/robtacconelli/Nacrith-GPU

✨ Datasets citing this paper:
• https://huggingface.co/datasets/robtacconelli/smollm2-135M-GGUF

✨ Spaces citing this paper:
• https://huggingface.co/spaces/robtacconelli/Nacrith-GPU

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

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#LosslessCompression #NeuralNetworks #LLM #DataCompression #MachineLearning

✨Large Causal Models for Temporal Causal Discovery

📝 Summary:
Large Causal Models LCMs introduce a foundation-model approach for temporal causal discovery. They combine diverse synthetic and realistic time-series data, enabling scalable learning, improved generalization, and fast, single-pass inference beyond traditional methods.

🔹 Publication Date: Published on Feb 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18662
• PDF: https://arxiv.org/pdf/2602.18662

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

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#CausalDiscovery #FoundationModels #MachineLearning #TimeSeries #DataScience

✨ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning

📝 Summary:
ImplicitRDP is an end-to-end visual-force diffusion policy that integrates asynchronous vision and force sensing using structural slow-fast learning and virtual-target regularization. It improves reactivity and success in contact-rich manipulation tasks.

🔹 Publication Date: Published on Dec 11, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10946
• PDF: https://arxiv.org/pdf/2512.10946
• Project Page: https://implicit-rdp.github.io
• Github: https://github.com/Chen-Wendi/ImplicitRDP

🔹 Models citing this paper:
• https://huggingface.co/WendiChen/ImplicitRDP_model

✨ Datasets citing this paper:
• https://huggingface.co/datasets/WendiChen/ImplicitRDP_dataset

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

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#Robotics #DiffusionPolicy #MachineLearning #Manipulation #ComputerVision

✨Contact-Anchored Proprioceptive Odometry for Quadruped Robots

📝 Summary:
This purely proprioceptive state estimator uses IMU and motor data, treating contacting legs as kinematic anchors. It leverages footfall positions and height clustering to reduce pose and elevation drift without external sensors, showing low errors on quadruped robots.

🔹 Publication Date: Published on Feb 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.17393
• PDF: https://arxiv.org/pdf/2602.17393
• Project Page: https://github.com/ShineMinxing/Ros2Go2Estimator
• Github: https://github.com/ShineMinxing/Ros2Go2Estimator

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#Robotics #QuadrupedRobots #StateEstimation #Odometry #ProprioceptiveSensing

✨DODO: Discrete OCR Diffusion Models

📝 Summary:
DODO introduces block discrete diffusion for optical character recognition to overcome the slowness of autoregressive models. It achieves near state-of-the-art accuracy and up to 3x faster inference by processing text in blocks, which resolves instability issues of global diffusion for precise OC...

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16872
• PDF: https://arxiv.org/pdf/2602.16872

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations

📝 Summary:
Agentic memory systems for LLM agents face empirical challenges including inadequate benchmarks, misaligned metrics, and performance variability that limit their practical effectiveness. AI-generated ...

🔹 Publication Date: Published on Feb 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19320
• PDF: https://arxiv.org/pdf/2602.19320
• Github: https://github.com/FredJiang0324/Anatomy-of-Agentic-Memory

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

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#AI #DataScience #MachineLearning #HuggingFace #Research