✨Qwen2.5 Technical Report

📝 Summary:
Qwen2.5, an enhanced series of large language models, demonstrates superior performance across various benchmarks and use cases through extensive pre-training and advanced post-training techniques. AI...

🔹 Publication Date: Published on Dec 19, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2412.15115
• PDF: https://arxiv.org/pdf/2412.15115
• Github: https://github.com/QwenLM/Qwen2.5

🔹 Models citing this paper:
• https://huggingface.co/Qwen/QwQ-32B
• https://huggingface.co/Qwen/QwQ-32B-GGUF
• https://huggingface.co/Qwen/QwQ-32B-AWQ

✨ Datasets citing this paper:
• https://huggingface.co/datasets/HuggingFaceTB/smoltalk2

✨ Spaces citing this paper:
• https://huggingface.co/spaces/modelscope/DocResearch
• https://huggingface.co/spaces/ITHwangg/candle-qwen25-wasm-demo
• https://huggingface.co/spaces/GuminiResearch/Gumini_sLLM_Report

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✨V_1: Unifying Generation and Self-Verification for Parallel Reasoners

📝 Summary:
V1 unifies generation and verification for complex reasoning tasks. It leverages models' superior ability in pairwise self-verification over independent scoring, improving performance and efficiency in code generation and math.

🔹 Publication Date: Published on Mar 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04304
• PDF: https://arxiv.org/pdf/2603.04304
• Project Page: https://harmandotpy.github.io/v1-verification/
• Github: https://github.com/HarmanDotpy/pairwise-self-verification

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#AI #LLMs #MachineLearning #CodeGeneration #AIReasoning

✨Underwater Camouflaged Object Tracking Meets Vision-Language SAM2

📝 Summary:
A new large-scale multi-modal underwater camouflaged object tracking dataset, UW-COT220, was introduced. Evaluations showed SAM2 improved tracking performance over SAM. A novel vision-language framework, VL-SAM2, achieved state-of-the-art results on both underwater and open-air object tracking da...

🔹 Publication Date: Published on Sep 25, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2409.16902
• PDF: https://arxiv.org/pdf/2409.16902
• Github: https://github.com/983632847/awesome-multimodal-object-tracking

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✨MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier

📝 Summary:
MOOSE-Star enables tractable training for generative scientific reasoning by tackling its intractable combinatorial complexity. It uses decomposed subtasks, hierarchical search to reduce complexity to logarithmic, and bounded composition, allowing scalable training and inference.

🔹 Publication Date: Published on Mar 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03756
• PDF: https://arxiv.org/pdf/2603.03756
• Github: https://github.com/ZonglinY/MOOSE-Star

🔹 Models citing this paper:
• https://huggingface.co/ZonglinY/MOOSE-Star-HC-R1D-7B
• https://huggingface.co/ZonglinY/MOOSE-Star-IR-R1D-7B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ZonglinY/TOMATO-Star
• https://huggingface.co/datasets/ZonglinY/TOMATO-Star-SFT-Data-R1D-32B

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✨HiFi-Inpaint: Towards High-Fidelity Reference-Based Inpainting for Generating Detail-Preserving Human-Product Images

📝 Summary:
HiFi-Inpaint generates high-fidelity human-product images using shared enhancement attention and detail-aware loss with a new 40K-image dataset. AI-generated summary Human-product images , which showc...

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02210
• PDF: https://arxiv.org/pdf/2603.02210
• Project Page: https://correr-zhou.github.io/HiFi-Inpaint/
• Github: https://github.com/Correr-Zhou/HiFi-Inpaint

✨ Datasets citing this paper:
• https://huggingface.co/datasets/donghao-zhou/HP-Image-40K

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✨DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval

📝 Summary:
DARE is a retrieval model that improves R package retrieval by embedding data distribution information into function representations. It significantly outperforms existing models, enabling more reliable R code generation and statistical analysis.

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04743
• PDF: https://arxiv.org/pdf/2603.04743
• Project Page: https://ama-cmfai.github.io/DARE_webpage/
• Github: https://ama-cmfai.github.io/DARE_webpage/

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✨Locality-Attending Vision Transformer

📝 Summary:
Vision transformers are enhanced for segmentation tasks through a Gaussian kernel modulation that improves local attention while maintaining classification performance. AI-generated summary Vision tra...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04892
• PDF: https://arxiv.org/pdf/2603.04892
• Github: https://github.com/sinahmr/LocAtViT

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✨RealWonder: Real-Time Physical Action-Conditioned Video Generation

📝 Summary:
RealWonder enables real-time action-conditioned video generation by integrating 3D reconstruction, physics simulation, and a distilled video generator to simulate physical consequences of 3D actions. ...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05449
• PDF: https://arxiv.org/pdf/2603.05449
• Project Page: https://liuwei283.github.io/RealWonder/
• Github: https://github.com/liuwei283/RealWonder

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✨KARL: Knowledge Agents via Reinforcement Learning

📝 Summary:
A reinforcement learning system for enterprise search agents achieves superior performance through diverse training data generation and multi-task learning approaches. AI-generated summary We present ...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05218
• PDF: https://arxiv.org/pdf/2603.05218

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✨Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling

📝 Summary:
Timer-S1 is a scalable Mixture-of-Experts time series model with 8.3B parameters that uses serial scaling and novel TimeMoE blocks to improve long-term forecasting accuracy. AI-generated summary We in...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04791
• PDF: https://arxiv.org/pdf/2603.04791

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