✨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 denoscriptions 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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✓ https://news.1rj.ru/str/DataScienceT
#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 denoscriptions 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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✓ https://news.1rj.ru/str/DataScienceT
#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
❤1
✨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
✨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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✓ https://news.1rj.ru/str/DataScienceT
#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
✨Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence
📝 Summary:
This work converts pretrained non-recurrent language models into depth-recurrent ones. Using a curriculum of recurrences improves performance on tasks like mathematics at a lower compute budget compared to standard post-training.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07384
• PDF: https://arxiv.org/pdf/2511.07384
• Github: https://github.com/mcleish7/retrofitting-recurrence
✨ Datasets citing this paper:
• https://huggingface.co/datasets/smcleish/retrofitting-llama-fineweb-edu-tokenized
==================================
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#LLM #DeepLearning #AIResearch #NeuralNetworks #ComputationalEfficiency
📝 Summary:
This work converts pretrained non-recurrent language models into depth-recurrent ones. Using a curriculum of recurrences improves performance on tasks like mathematics at a lower compute budget compared to standard post-training.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07384
• PDF: https://arxiv.org/pdf/2511.07384
• Github: https://github.com/mcleish7/retrofitting-recurrence
✨ Datasets citing this paper:
• https://huggingface.co/datasets/smcleish/retrofitting-llama-fineweb-edu-tokenized
==================================
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#LLM #DeepLearning #AIResearch #NeuralNetworks #ComputationalEfficiency
✨RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments
📝 Summary:
RLVE improves language model reasoning by dynamically adjusting problem difficulty in verifiable environments. This adaptive approach significantly outperforms static environments and traditional RL, yielding a 3.37% average improvement on reasoning benchmarks.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07317
• PDF: https://arxiv.org/pdf/2511.07317
• Github: https://github.com/Zhiyuan-Zeng/RLVE
🔹 Models citing this paper:
• https://huggingface.co/hamishivi/Nemotron-Research-Reasoning-Qwen-1.5B-v2-RLVE
• https://huggingface.co/hamishivi/OpenThinker3-1.5B-RLVE
==================================
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#ReinforcementLearning #LLMs #AI #AIReasoning #AdaptiveLearning
📝 Summary:
RLVE improves language model reasoning by dynamically adjusting problem difficulty in verifiable environments. This adaptive approach significantly outperforms static environments and traditional RL, yielding a 3.37% average improvement on reasoning benchmarks.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07317
• PDF: https://arxiv.org/pdf/2511.07317
• Github: https://github.com/Zhiyuan-Zeng/RLVE
🔹 Models citing this paper:
• https://huggingface.co/hamishivi/Nemotron-Research-Reasoning-Qwen-1.5B-v2-RLVE
• https://huggingface.co/hamishivi/OpenThinker3-1.5B-RLVE
==================================
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#ReinforcementLearning #LLMs #AI #AIReasoning #AdaptiveLearning
✨Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for Multilingual and Cross-Lingual Tasks
📝 Summary:
Llama-Embed-Nemotron-8B is an open-source text embedding model achieving state-of-the-art performance, especially in multilingual tasks. Its success comes from a novel data mix and detailed ablation studies, making it a universal solution.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07025
• PDF: https://arxiv.org/pdf/2511.07025
🔹 Models citing this paper:
• https://huggingface.co/nvidia/llama-embed-nemotron-8b
==================================
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#TextEmbeddings #MultilingualNLP #CrossLingual #LanguageModels #AIResearch
📝 Summary:
Llama-Embed-Nemotron-8B is an open-source text embedding model achieving state-of-the-art performance, especially in multilingual tasks. Its success comes from a novel data mix and detailed ablation studies, making it a universal solution.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07025
• PDF: https://arxiv.org/pdf/2511.07025
🔹 Models citing this paper:
• https://huggingface.co/nvidia/llama-embed-nemotron-8b
==================================
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#TextEmbeddings #MultilingualNLP #CrossLingual #LanguageModels #AIResearch
✨Long Grounded Thoughts: Distilling Compositional Visual Reasoning Chains at Scale
📝 Summary:
Researchers developed a new framework to generate over 1M high-quality synthetic vision-centric reasoning questions with complex traces. Finetuning models on this data significantly improves vision-centric performance and surprisingly boosts text and audio reasoning, demonstrating strong cross-mo...
🔹 Publication Date: Published on Nov 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.05705
• PDF: https://arxiv.org/pdf/2511.05705
==================================
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#VisualReasoning #AI #MachineLearning #MultimodalAI #ComputerVision
📝 Summary:
Researchers developed a new framework to generate over 1M high-quality synthetic vision-centric reasoning questions with complex traces. Finetuning models on this data significantly improves vision-centric performance and surprisingly boosts text and audio reasoning, demonstrating strong cross-mo...
🔹 Publication Date: Published on Nov 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.05705
• PDF: https://arxiv.org/pdf/2511.05705
==================================
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#VisualReasoning #AI #MachineLearning #MultimodalAI #ComputerVision
✨Reinforcement Learning Improves Traversal of Hierarchical Knowledge in LLMs
📝 Summary:
Reinforcement learning improves LLMs ability to recall hierarchical knowledge without degrading existing facts. It enhances models procedural skills in navigating knowledge, rather than changing the knowledge representation itself. This leads to better performance on structured prompting and deep...
🔹 Publication Date: Published on Nov 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.05933
• PDF: https://arxiv.org/pdf/2511.05933
==================================
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#ReinforcementLearning #LLMs #ArtificialIntelligence #DeepLearning #KnowledgeRetrieval
📝 Summary:
Reinforcement learning improves LLMs ability to recall hierarchical knowledge without degrading existing facts. It enhances models procedural skills in navigating knowledge, rather than changing the knowledge representation itself. This leads to better performance on structured prompting and deep...
🔹 Publication Date: Published on Nov 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.05933
• PDF: https://arxiv.org/pdf/2511.05933
==================================
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#ReinforcementLearning #LLMs #ArtificialIntelligence #DeepLearning #KnowledgeRetrieval