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

📝 Summary:
This paper introduces GUI-Owl and Mobile-Agent-v3, open-source GUI agent models and frameworks. Mobile-Agent-v3 achieves new state-of-the-art performance on GUI automation benchmarks like AndroidWorld and OSWorld by building on GUI-Owl's innovations in environment infrastructure, agent capabiliti...

🔹 Publication Date: Published on Aug 21, 2025

🔹 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 #AI #OpenSource #MachineLearning

✨Decoding as Optimisation on the Probability Simplex: From Top-K to Top-P (Nucleus) to Best-of-K Samplers

📝 Summary:
This paper redefines decoding as an optimization problem on the probability simplex balancing model scores with structural preferences. This unifies existing methods and enables new decoders like Best-of-K, improving accuracy in tasks such as mathematical reasoning.

🔹 Publication Date: Published on Feb 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18292
• PDF: https://arxiv.org/pdf/2602.18292

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#DecodingStrategies #Optimization #LLMs #MathematicalReasoning #MachineLearning

✨Adam Improves Muon: Adaptive Moment Estimation with Orthogonalized Momentum

📝 Summary:
NAMO and NAMO-D are new optimizers combining orthogonalized momentum with Adam-type noise adaptation. They show improved convergence and better performance on LLM pretraining than AdamW and Muon, with NAMO-D adding neuron-wise adaptation for further gains.

🔹 Publication Date: Published on Feb 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.17080
• PDF: https://arxiv.org/pdf/2602.17080

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#MachineLearning #DeepLearning #LLM #Optimizers #Adam

✨Avey-B

📝 Summary:
This paper reformulates the Avey architecture for encoder-only tasks, introducing innovations like decoupled parameterizations and neural compression. The new model consistently outperforms Transformer-based encoders on token classification and information retrieval, also scaling more efficiently...

🔹 Publication Date: Published on Feb 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.15814
• PDF: https://arxiv.org/pdf/2602.15814
• Github: https://github.com/rimads/avey-b

🔹 Models citing this paper:
• https://huggingface.co/avey-ai/avey-b1-base-exp
• https://huggingface.co/avey-ai/avey-b1-large-exp

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research