✨SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?
📝 Summary:
SWE-fficiency is a new benchmark evaluating how language models optimize real-world software repositories for performance on actual workloads. Agents must identify bottlenecks and generate correct code patches matching expert speedup. Current agents significantly underperform, struggling with loc...
🔹 Publication Date: Published on Nov 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06090
• PDF: https://arxiv.org/pdf/2511.06090
• Project Page: https://swefficiency.com/
==================================
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#LLM #SoftwareOptimization #PerformanceTuning #AIagents #Benchmarking
📝 Summary:
SWE-fficiency is a new benchmark evaluating how language models optimize real-world software repositories for performance on actual workloads. Agents must identify bottlenecks and generate correct code patches matching expert speedup. Current agents significantly underperform, struggling with loc...
🔹 Publication Date: Published on Nov 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06090
• PDF: https://arxiv.org/pdf/2511.06090
• Project Page: https://swefficiency.com/
==================================
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#LLM #SoftwareOptimization #PerformanceTuning #AIagents #Benchmarking
✨LUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAs
📝 Summary:
LUT-LLM is an FPGA accelerator for LLM inference that leverages on-chip memory to shift computation from arithmetic to memory-based operations via table lookups. This innovative approach achieves 1.66x lower latency than AMD MI210 and 1.72x higher energy efficiency than NVIDIA A100 for a 1.7B LLM.
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06174
• PDF: https://arxiv.org/pdf/2511.06174
==================================
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#LLM #FPGA #AI #DeepLearning #AIHardware
📝 Summary:
LUT-LLM is an FPGA accelerator for LLM inference that leverages on-chip memory to shift computation from arithmetic to memory-based operations via table lookups. This innovative approach achieves 1.66x lower latency than AMD MI210 and 1.72x higher energy efficiency than NVIDIA A100 for a 1.7B LLM.
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06174
• PDF: https://arxiv.org/pdf/2511.06174
==================================
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#LLM #FPGA #AI #DeepLearning #AIHardware
✨DRIVE: Data Curation Best Practices for Reinforcement Learning with Verifiable Reward in Competitive Code Generation
📝 Summary:
This study develops a two-stage reinforcement learning method for competitive code generation. It uses tailored data curation and a hard-focus curriculum, achieving state-of-the-art performance on competitive programming benchmarks.
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06307
• PDF: https://arxiv.org/pdf/2511.06307
==================================
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#ReinforcementLearning #CodeGeneration #DataCuration #MachineLearning #AIResearch
📝 Summary:
This study develops a two-stage reinforcement learning method for competitive code generation. It uses tailored data curation and a hard-focus curriculum, achieving state-of-the-art performance on competitive programming benchmarks.
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06307
• PDF: https://arxiv.org/pdf/2511.06307
==================================
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#ReinforcementLearning #CodeGeneration #DataCuration #MachineLearning #AIResearch
✨SofT-GRPO: Surpassing Discrete-Token LLM Reinforcement Learning via Gumbel-Reparameterized Soft-Thinking Policy Optimization
📝 Summary:
SofT-GRPO is a novel algorithm that enhances soft-thinking in LLMs by integrating Gumbel noise and Gumbel-Softmax. This method successfully reinforces soft-thinking policies, enabling LLMs to outperform discrete-token reinforcement learning approaches, especially on complex tasks.
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06411
• PDF: https://arxiv.org/pdf/2511.06411
🔹 Models citing this paper:
• https://huggingface.co/zz1358m/SofT-GRPO-master
==================================
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#LLM #ReinforcementLearning #AI #MachineLearning #DeepLearning
📝 Summary:
SofT-GRPO is a novel algorithm that enhances soft-thinking in LLMs by integrating Gumbel noise and Gumbel-Softmax. This method successfully reinforces soft-thinking policies, enabling LLMs to outperform discrete-token reinforcement learning approaches, especially on complex tasks.
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06411
• PDF: https://arxiv.org/pdf/2511.06411
🔹 Models citing this paper:
• https://huggingface.co/zz1358m/SofT-GRPO-master
==================================
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#LLM #ReinforcementLearning #AI #MachineLearning #DeepLearning
✨Diffusion-SDPO: Safeguarded Direct Preference Optimization for Diffusion Models
📝 Summary:
Diffusion-SDPO improves text-to-image quality by fixing a flaw in standard DPO where preferred output error can increase. It uses a safeguarded update to adaptively scale the loser gradient, ensuring the preferred output's error never increases. This leads to consistent quality gains across bench...
🔹 Publication Date: Published on Nov 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.03317
• PDF: https://arxiv.org/pdf/2511.03317
• Github: https://github.com/AIDC-AI/Diffusion-SDPO
🔹 Models citing this paper:
• https://huggingface.co/AIDC-AI/Diffusion-SDPO
==================================
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#DiffusionModels #DPO #TextToImage #GenerativeAI #AI
📝 Summary:
Diffusion-SDPO improves text-to-image quality by fixing a flaw in standard DPO where preferred output error can increase. It uses a safeguarded update to adaptively scale the loser gradient, ensuring the preferred output's error never increases. This leads to consistent quality gains across bench...
🔹 Publication Date: Published on Nov 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.03317
• PDF: https://arxiv.org/pdf/2511.03317
• Github: https://github.com/AIDC-AI/Diffusion-SDPO
🔹 Models citing this paper:
• https://huggingface.co/AIDC-AI/Diffusion-SDPO
==================================
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#DiffusionModels #DPO #TextToImage #GenerativeAI #AI
✨VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Models
📝 Summary:
VADER is an LLM framework enhancing video anomaly understanding. It integrates keyframe object relations and visual cues to provide detailed, causally grounded descriptions and robust question answering, advancing explainable anomaly analysis.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07299
• PDF: https://arxiv.org/pdf/2511.07299
==================================
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#LLM #VideoAnalytics #AnomalyDetection #Causality #ExplainableAI
📝 Summary:
VADER is an LLM framework enhancing video anomaly understanding. It integrates keyframe object relations and visual cues to provide detailed, causally grounded descriptions and robust question answering, advancing explainable anomaly analysis.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07299
• PDF: https://arxiv.org/pdf/2511.07299
==================================
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#LLM #VideoAnalytics #AnomalyDetection #Causality #ExplainableAI
✨MPJudge: Towards Perceptual Assessment of Music-Induced Paintings
📝 Summary:
MPJudge is a new framework for assessing music-induced paintings. It integrates music features into a visual encoder using a modulation-based fusion mechanism, outperforming existing emotion models by directly modeling perceptual coherence. It also identifies music-relevant regions better.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07137
• PDF: https://arxiv.org/pdf/2511.07137
==================================
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#MusicAndArt #ComputerVision #MachineLearning #DeepLearning #MultimodalAI
📝 Summary:
MPJudge is a new framework for assessing music-induced paintings. It integrates music features into a visual encoder using a modulation-based fusion mechanism, outperforming existing emotion models by directly modeling perceptual coherence. It also identifies music-relevant regions better.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07137
• PDF: https://arxiv.org/pdf/2511.07137
==================================
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#MusicAndArt #ComputerVision #MachineLearning #DeepLearning #MultimodalAI
✨Do LLMs Feel? Teaching Emotion Recognition with Prompts, Retrieval, and Curriculum Learning
📝 Summary:
PRC-Emo is a new framework that significantly improves LLMs' emotion recognition in conversations. It combines prompt engineering, demonstration retrieval, and curriculum learning, achieving state-of-the-art results on benchmark datasets.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07061
• PDF: https://arxiv.org/pdf/2511.07061
• Github: https://github.com/LiXinran6/PRC-Emo
==================================
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#LLM #EmotionRecognition #NLP #AIResearch #MachineLearning
📝 Summary:
PRC-Emo is a new framework that significantly improves LLMs' emotion recognition in conversations. It combines prompt engineering, demonstration retrieval, and curriculum learning, achieving state-of-the-art results on benchmark datasets.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07061
• PDF: https://arxiv.org/pdf/2511.07061
• Github: https://github.com/LiXinran6/PRC-Emo
==================================
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#LLM #EmotionRecognition #NLP #AIResearch #MachineLearning
✨10 Open Challenges Steering the Future of Vision-Language-Action Models
📝 Summary:
This paper identifies 10 principal challenges in vision-language-action VLA models, including multimodality, reasoning, and safety. It also explores emerging trends like spatial understanding and data synthesis. The goal is to accelerate VLA model development and wider acceptance.
🔹 Publication Date: Published on Nov 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.05936
• PDF: https://arxiv.org/pdf/2511.05936
==================================
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#VLA #AI #MachineLearning #ComputerVision #NLP
📝 Summary:
This paper identifies 10 principal challenges in vision-language-action VLA models, including multimodality, reasoning, and safety. It also explores emerging trends like spatial understanding and data synthesis. The goal is to accelerate VLA model development and wider acceptance.
🔹 Publication Date: Published on Nov 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.05936
• PDF: https://arxiv.org/pdf/2511.05936
==================================
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#VLA #AI #MachineLearning #ComputerVision #NLP
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✨Qwen-Image Technical Report
📝 Summary:
Qwen-Image is an image generation model that significantly advances complex text rendering through a comprehensive data pipeline and progressive training across languages. It also improves precise image editing via a dual-encoding mechanism and multi-task training for enhanced consistency and vis...
🔹 Publication Date: Published on Aug 4
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/qwen-image-technical-report
• PDF: https://arxiv.org/pdf/2508.02324
• Github: https://github.com/QwenLM/Qwen-Image
🔹 Models citing this paper:
• https://huggingface.co/Qwen/Qwen-Image
• https://huggingface.co/Qwen/Qwen-Image-Edit
• https://huggingface.co/Qwen/Qwen-Image-Edit-2509
✨ Spaces citing this paper:
• https://huggingface.co/spaces/linoyts/Qwen-Image-Edit-Angles
• https://huggingface.co/spaces/tori29umai/Qwen-Image-2509-MultipleAngles
• https://huggingface.co/spaces/linoyts/Qwen-Image-Edit-next-scene
==================================
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#ImageGeneration #AI #DeepLearning #ComputerVision #TextToImage
📝 Summary:
Qwen-Image is an image generation model that significantly advances complex text rendering through a comprehensive data pipeline and progressive training across languages. It also improves precise image editing via a dual-encoding mechanism and multi-task training for enhanced consistency and vis...
🔹 Publication Date: Published on Aug 4
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/qwen-image-technical-report
• PDF: https://arxiv.org/pdf/2508.02324
• Github: https://github.com/QwenLM/Qwen-Image
🔹 Models citing this paper:
• https://huggingface.co/Qwen/Qwen-Image
• https://huggingface.co/Qwen/Qwen-Image-Edit
• https://huggingface.co/Qwen/Qwen-Image-Edit-2509
✨ Spaces citing this paper:
• https://huggingface.co/spaces/linoyts/Qwen-Image-Edit-Angles
• https://huggingface.co/spaces/tori29umai/Qwen-Image-2509-MultipleAngles
• https://huggingface.co/spaces/linoyts/Qwen-Image-Edit-next-scene
==================================
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#ImageGeneration #AI #DeepLearning #ComputerVision #TextToImage
Arxivexplained
Qwen-Image Technical Report - Explained Simply
By Chenfei Wu, Jiahao Li, Jingren Zhou et al.. # Qwen-Image: Breaking Through AI's Text and Image Editing Barriers
**The Problem:** Current AI ima...
**The Problem:** Current AI ima...
✨Reasoning with Confidence: Efficient Verification of LLM Reasoning Steps via Uncertainty Heads
📝 Summary:
This paper introduces lightweight UHeads, transformer-based uncertainty quantification heads, to efficiently verify LLM reasoning steps. UHeads estimate uncertainty from the LLM's internal states, outperforming larger verification models while being scalable and effective across various domains.
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06209
• PDF: https://arxiv.org/pdf/2511.06209
==================================
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#LLM #AI #MachineLearning #UncertaintyQuantification #ModelVerification
📝 Summary:
This paper introduces lightweight UHeads, transformer-based uncertainty quantification heads, to efficiently verify LLM reasoning steps. UHeads estimate uncertainty from the LLM's internal states, outperforming larger verification models while being scalable and effective across various domains.
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06209
• PDF: https://arxiv.org/pdf/2511.06209
==================================
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#LLM #AI #MachineLearning #UncertaintyQuantification #ModelVerification
✨Omni-AVSR: Towards Unified Multimodal Speech Recognition with Large Language Models
📝 Summary:
Omni-AVSR is a unified audio-visual LLM that efficiently supports ASR, VSR, and AVSR. It uses multi-granularity training and parameter-efficient adaptation to achieve high accuracy while significantly reducing resource use compared to separate models.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07253
• PDF: https://arxiv.org/pdf/2511.07253
• Project Page: https://umbertocappellazzo.github.io/Omni-AVSR
• Github: https://github.com/umbertocappellazzo/Omni-AVSR
==================================
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#SpeechRecognition #LLM #MultimodalAI #DeepLearning #AIResearch
📝 Summary:
Omni-AVSR is a unified audio-visual LLM that efficiently supports ASR, VSR, and AVSR. It uses multi-granularity training and parameter-efficient adaptation to achieve high accuracy while significantly reducing resource use compared to separate models.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07253
• PDF: https://arxiv.org/pdf/2511.07253
• Project Page: https://umbertocappellazzo.github.io/Omni-AVSR
• Github: https://github.com/umbertocappellazzo/Omni-AVSR
==================================
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#SpeechRecognition #LLM #MultimodalAI #DeepLearning #AIResearch
✨Ariadne: A Controllable Framework for Probing and Extending VLM Reasoning Boundaries
📝 Summary:
Ariadne is a framework using synthetic mazes and RLVR to enhance VLM visual-centric spatial reasoning. It expanded VLM capabilities, raising accuracy from 0 percent to over 50 percent, and significantly improved zero-shot generalization on real-world benchmarks.
🔹 Publication Date: Published on Nov 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.00710
• PDF: https://arxiv.org/pdf/2511.00710
• Project Page: https://mingheshen.github.io/Ariadne/
🔹 Models citing this paper:
• https://huggingface.co/KOKKKOKK/Ariadne
==================================
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#VLM #AI #MachineLearning #ComputerVision #SpatialReasoning
📝 Summary:
Ariadne is a framework using synthetic mazes and RLVR to enhance VLM visual-centric spatial reasoning. It expanded VLM capabilities, raising accuracy from 0 percent to over 50 percent, and significantly improved zero-shot generalization on real-world benchmarks.
🔹 Publication Date: Published on Nov 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.00710
• PDF: https://arxiv.org/pdf/2511.00710
• Project Page: https://mingheshen.github.io/Ariadne/
🔹 Models citing this paper:
• https://huggingface.co/KOKKKOKK/Ariadne
==================================
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#VLM #AI #MachineLearning #ComputerVision #SpatialReasoning
✨Ovi: Twin Backbone Cross-Modal Fusion for Audio-Video Generation
📝 Summary:
Ovi is a unified audio-video generation model using twin-DiT modules with blockwise cross-modal fusion. This innovative design ensures natural synchronization and high-quality multimodal outputs, simplifying previous multi-stage approaches.
🔹 Publication Date: Published on Sep 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.01284
• PDF: https://arxiv.org/pdf/2510.01284
• Project Page: https://aaxwaz.github.io/Ovi
• Github: https://github.com/character-ai/Ovi
🔹 Models citing this paper:
• https://huggingface.co/chetwinlow1/Ovi
• https://huggingface.co/rkfg/Ovi-fp8_quantized
✨ Spaces citing this paper:
• https://huggingface.co/spaces/akhaliq/Ovi
• https://huggingface.co/spaces/deddytoyota/Ovi
• https://huggingface.co/spaces/alexnasa/Ovi-ZEROGPU
==================================
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#AudioVideoGeneration #MultimodalAI #DeepLearning #CrossModalFusion #AIResearch
📝 Summary:
Ovi is a unified audio-video generation model using twin-DiT modules with blockwise cross-modal fusion. This innovative design ensures natural synchronization and high-quality multimodal outputs, simplifying previous multi-stage approaches.
🔹 Publication Date: Published on Sep 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.01284
• PDF: https://arxiv.org/pdf/2510.01284
• Project Page: https://aaxwaz.github.io/Ovi
• Github: https://github.com/character-ai/Ovi
🔹 Models citing this paper:
• https://huggingface.co/chetwinlow1/Ovi
• https://huggingface.co/rkfg/Ovi-fp8_quantized
✨ Spaces citing this paper:
• https://huggingface.co/spaces/akhaliq/Ovi
• https://huggingface.co/spaces/deddytoyota/Ovi
• https://huggingface.co/spaces/alexnasa/Ovi-ZEROGPU
==================================
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#AudioVideoGeneration #MultimodalAI #DeepLearning #CrossModalFusion #AIResearch
arXiv.org
Ovi: Twin Backbone Cross-Modal Fusion for Audio-Video Generation
Audio-video generation has often relied on complex multi-stage architectures or sequential synthesis of sound and visuals. We introduce Ovi, a unified paradigm for audio-video generation that...
✨NURBGen: High-Fidelity Text-to-CAD Generation through LLM-Driven NURBS Modeling
📝 Summary:
NURBGen generates high-fidelity 3D CAD models directly from text using Non-Uniform Rational B-Splines NURBS. It fine-tunes an LLM to translate text into NURBS parameters, enabling robust modeling with a hybrid representation. NURBGen outperforms existing text-to-CAD methods in geometric fidelity ...
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06194
• PDF: https://arxiv.org/pdf/2511.06194
==================================
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#TextToCAD #LLM #NURBS #3DModeling #GenerativeAI
📝 Summary:
NURBGen generates high-fidelity 3D CAD models directly from text using Non-Uniform Rational B-Splines NURBS. It fine-tunes an LLM to translate text into NURBS parameters, enabling robust modeling with a hybrid representation. NURBGen outperforms existing text-to-CAD methods in geometric fidelity ...
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06194
• PDF: https://arxiv.org/pdf/2511.06194
==================================
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#TextToCAD #LLM #NURBS #3DModeling #GenerativeAI
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✨Grounding Computer Use Agents on Human Demonstrations
📝 Summary:
GroundCUA is a large desktop grounding dataset built from expert human demonstrations. It enables GroundNext models to achieve state-of-the-art performance in mapping instructions to UI elements with less training data and strong agentic capabilities.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07332
• PDF: https://arxiv.org/pdf/2511.07332
• Project Page: https://groundcua.github.io/
• Github: https://groundcua.github.io/
==================================
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#AI #Agents #HCI #Datasets #HumanDemonstrations
📝 Summary:
GroundCUA is a large desktop grounding dataset built from expert human demonstrations. It enables GroundNext models to achieve state-of-the-art performance in mapping instructions to UI elements with less training data and strong agentic capabilities.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07332
• PDF: https://arxiv.org/pdf/2511.07332
• Project Page: https://groundcua.github.io/
• Github: https://groundcua.github.io/
==================================
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#AI #Agents #HCI #Datasets #HumanDemonstrations