✨ShowUI-π: Flow-based Generative Models as GUI Dexterous Hands

📝 Summary:
ShowUI-π is the first flow-based generative model for GUI agents, unifying discrete clicks and continuous drag actions. It achieves smooth, stable trajectories and significantly outperforms prior agents on ScreenDrag, a new benchmark for GUI drag capabilities.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24965
• PDF: https://arxiv.org/pdf/2512.24965
• Project Page: https://showlab.github.io/showui-pi
• Github: https://github.com/showlab/showui-pi

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨EpiCaR: Knowing What You Don't Know Matters for Better Reasoning in LLMs

📝 Summary:
LLM self-training improves reasoning but causes overconfidence. EpiCaR solves this by jointly optimizing reasoning performance and calibration through epistemic learning and self-evaluation. It achieves better accuracy and calibration, reduces inference compute by 3X, and generalizes well to new ...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06786
• PDF: https://arxiv.org/pdf/2601.06786

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#LLMs #AI #MachineLearning #Reasoning #Calibration

✨Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models

📝 Summary:
The Engram module introduces conditional memory as a new sparsity axis for Transformers, improving knowledge lookup and reasoning. It outperforms MoE, boosting performance across domains by offloading static knowledge and enhancing efficiency.

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07372
• PDF: https://arxiv.org/pdf/2601.07372
• Github: https://github.com/deepseek-ai/Engram

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#LLM #AI #MachineLearning #Transformers #Sparsity

✨VideoLoom: A Video Large Language Model for Joint Spatial-Temporal Understanding

📝 Summary:
VideoLoom is a unified video large language model that achieves state-of-the-art performance in spatial-temporal video understanding through a specialized dataset and benchmark. AI-generated summary T...

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07290
• PDF: https://arxiv.org/pdf/2601.07290
• Github: https://github.com/JPShi12/VideoLoom

🔹 Models citing this paper:
• https://huggingface.co/JPShi/VideoLoom-4B
• https://huggingface.co/JPShi/VideoLoom-8B

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✨UM-Text: A Unified Multimodal Model for Image Understanding

📝 Summary:
A unified multimodal model for visual text editing that understands natural language instructions and maintains stylistic consistency with reference images through visual language modeling and context...

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08321
• PDF: https://arxiv.org/pdf/2601.08321

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✨GeoMotionGPT: Geometry-Aligned Motion Understanding with Large Language Models

📝 Summary:
GeoMotionGPT introduces a framework aligning motion token geometry with language model embeddings using orthogonal constraints and sparse projection. This unified geometric basis enhances LLM motion reasoning, achieving a 20% performance improvement on HumanML3D.

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07632
• PDF: https://arxiv.org/pdf/2601.07632
• Project Page: https://huggingface.co/papers?q=sparse%20projection
• Github: https://github.com/JYe16/GeoMotionGPT

🔹 Models citing this paper:
• https://huggingface.co/zy22b/GeoMotionGPT

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨The Agent's First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios

📝 Summary:
EvoEnv is a new dynamic evaluation environment for MLLMs. It assesses agent robustness in real-world tasks, focusing on context-aware scheduling, active exploration, and continuous learning. Current MLLMs show significant deficiencies in these dynamic scenarios.

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08173
• PDF: https://arxiv.org/pdf/2601.08173
• Github: https://github.com/KnowledgeXLab/EvoEnv

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✨Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning

📝 Summary:
Fast-ThinkAct is an efficient vision-language-action framework that reduces inference latency by 89.3% through compact latent reasoning while maintaining long-horizon planning and few-shot adaptation ...

🔹 Publication Date: Published on Jan 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09708
• PDF: https://arxiv.org/pdf/2601.09708
• Project Page: https://jasper0314-huang.github.io/fast-thinkact/
• Github: https://jasper0314-huang.github.io/fast-thinkact/

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✨A^3-Bench: Benchmarking Memory-Driven Scientific Reasoning via Anchor and Attractor Activation

📝 Summary:
Scientific reasoning relies not only on logical inference but also on activating prior knowledge and experiential structures. Memory can efficiently reuse knowledge and enhance reasoning consistency a...

🔹 Publication Date: Published on Jan 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09274
• PDF: https://arxiv.org/pdf/2601.09274

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨MAXS: Meta-Adaptive Exploration with LLM Agents

📝 Summary:
MAXS is a meta-adaptive reasoning framework for LLM agents that improves multi-tool reasoning through lookahead strategies and trajectory convergence mechanisms, balancing global effectiveness and com...

🔹 Publication Date: Published on Jan 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09259
• PDF: https://arxiv.org/pdf/2601.09259

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

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