✨ReIn: Conversational Error Recovery with Reasoning Inception
📝 Summary:
Conversational agents with tool integration face challenges from user-induced errors, but a test-time intervention method called Reasoning Inception (ReIn) enables error recovery by injecting external...
🔹 Publication Date: Published on Feb 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.17022
• PDF: https://arxiv.org/pdf/2602.17022
==================================
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📝 Summary:
Conversational agents with tool integration face challenges from user-induced errors, but a test-time intervention method called Reasoning Inception (ReIn) enables error recovery by injecting external...
🔹 Publication Date: Published on Feb 19
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.17022
• PDF: https://arxiv.org/pdf/2602.17022
==================================
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❤1
✨Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions
📝 Summary:
An adaptive group elicitation framework combines LLM information gain with graph neural networks for population predictions. It selects questions and respondents, imputing missing data under budget limits to improve prediction accuracy with fewer queries.
🔹 Publication Date: Published on Feb 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2602.14279
• PDF: https://arxiv.org/pdf/2602.14279
• Github: https://github.com/ZDCSlab/Group-Adaptive-Elicitation
==================================
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📝 Summary:
An adaptive group elicitation framework combines LLM information gain with graph neural networks for population predictions. It selects questions and respondents, imputing missing data under budget limits to improve prediction accuracy with fewer queries.
🔹 Publication Date: Published on Feb 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2602.14279
• PDF: https://arxiv.org/pdf/2602.14279
• Github: https://github.com/ZDCSlab/Group-Adaptive-Elicitation
==================================
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❤1
✨Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges
📝 Summary:
LLM-based judges using natural-language rubrics for evaluation can exhibit systematic preference drift from minor rubric modifications, which can be exploited to manipulate alignment pipelines and deg...
🔹 Publication Date: Published on Feb 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2602.13576
• PDF: https://arxiv.org/pdf/2602.13576
• Github: https://github.com/ZDCSlab/Rubrics-as-an-Attack-Surface
🔹 Models citing this paper:
• https://huggingface.co/ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt
• https://huggingface.co/ZDCSlab/ripd-ultra-real-gemma2-2b-it-biased-bt
• https://huggingface.co/ZDCSlab/ripd-ultra-real-llama3-8b-instruct-seed-bt
✨ Datasets citing this paper:
• https://huggingface.co/datasets/ZDCSlab/ripd-dataset
==================================
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📝 Summary:
LLM-based judges using natural-language rubrics for evaluation can exhibit systematic preference drift from minor rubric modifications, which can be exploited to manipulate alignment pipelines and deg...
🔹 Publication Date: Published on Feb 14
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2602.13576
• PDF: https://arxiv.org/pdf/2602.13576
• Github: https://github.com/ZDCSlab/Rubrics-as-an-Attack-Surface
🔹 Models citing this paper:
• https://huggingface.co/ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt
• https://huggingface.co/ZDCSlab/ripd-ultra-real-gemma2-2b-it-biased-bt
• https://huggingface.co/ZDCSlab/ripd-ultra-real-llama3-8b-instruct-seed-bt
✨ Datasets citing this paper:
• https://huggingface.co/datasets/ZDCSlab/ripd-dataset
==================================
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❤1
✨TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics
📝 Summary:
TOPReward is a novel temporal value function that estimates robotic task progress using pretrained video VLM internal token logits. It achieves superior zero-shot performance across over 130 real-world tasks and multiple robots, greatly outperforming baselines.
🔹 Publication Date: Published on Feb 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19313
• PDF: https://arxiv.org/pdf/2602.19313
• Project Page: https://topreward.github.io/webpage/
• Github: https://github.com/TOPReward/TOPReward
==================================
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📝 Summary:
TOPReward is a novel temporal value function that estimates robotic task progress using pretrained video VLM internal token logits. It achieves superior zero-shot performance across over 130 real-world tasks and multiple robots, greatly outperforming baselines.
🔹 Publication Date: Published on Feb 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19313
• PDF: https://arxiv.org/pdf/2602.19313
• Project Page: https://topreward.github.io/webpage/
• Github: https://github.com/TOPReward/TOPReward
==================================
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❤1
✨Mobile-O: Unified Multimodal Understanding and Generation on Mobile Device
📝 Summary:
A compact vision-language-diffusion model called Mobile-O enables efficient unified multimodal understanding and generation on mobile devices through specialized architecture design and optimized trai...
🔹 Publication Date: Published on Feb 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20161
• PDF: https://arxiv.org/pdf/2602.20161
• Project Page: https://amshaker.github.io/Mobile-O/
• Github: https://github.com/Amshaker/Mobile-O
==================================
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📝 Summary:
A compact vision-language-diffusion model called Mobile-O enables efficient unified multimodal understanding and generation on mobile devices through specialized architecture design and optimized trai...
🔹 Publication Date: Published on Feb 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20161
• PDF: https://arxiv.org/pdf/2602.20161
• Project Page: https://amshaker.github.io/Mobile-O/
• Github: https://github.com/Amshaker/Mobile-O
==================================
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❤1
✨DSDR: Dual-Scale Diversity Regularization for Exploration in LLM Reasoning
📝 Summary:
DSDR is a reinforcement learning framework that enhances large language model reasoning by promoting diversity at both global and local levels through dual-scale regularization techniques. AI-generate...
🔹 Publication Date: Published on Feb 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19895
• PDF: https://arxiv.org/pdf/2602.19895
==================================
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📝 Summary:
DSDR is a reinforcement learning framework that enhances large language model reasoning by promoting diversity at both global and local levels through dual-scale regularization techniques. AI-generate...
🔹 Publication Date: Published on Feb 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19895
• PDF: https://arxiv.org/pdf/2602.19895
==================================
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❤1
✨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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📝 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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❤1
✨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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📝 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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❤1
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✨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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📝 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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❤1
✨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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📝 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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❤1
✨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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📝 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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❤1
✨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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📝 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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❤1
✨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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📝 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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❤1
Forwarded from Machine Learning with Python
🎯 2026 IT Certification Prep Kit – Free!
🔥Whether you're preparing for #Python, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #AI, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification – SPOTO has got you covered!
✅ What’s Inside:
・Free Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS courses: https://bit.ly/3M9h5su
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🔥Whether you're preparing for #Python, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #AI, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification – SPOTO has got you covered!
✅ What’s Inside:
・Free Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS courses: https://bit.ly/3M9h5su
・IT Certs E-book: https://bit.ly/3Mlu5ez
・IT Exams Skill Test: https://bit.ly/3NVrgRU
・Free Cloud Study Guide: https://bit.ly/4kgFVDs
・Free AI material and support tools:https://bit.ly/46qvpDX
👉 Become Part of Our IT Learning Circle! resources and support:
https://chat.whatsapp.com/FlG2rOYVySLEHLKXF3nKGB
💬 Want exam help? Chat with an admin now!
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❤1
✨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
📝 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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❤1
✨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
📝 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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❤1
✨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
📝 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
❤1
✨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
📝 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
❤1
✨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
📝 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
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✨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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For more data science resources:
✓ https://t.iss.one/DataScienceT
#RobotLearning #Robotics #SyntheticData #DataAugmentation #AI
📝 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/
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#RobotLearning #Robotics #SyntheticData #DataAugmentation #AI
❤1