✨Virtual Width Networks

📝 Summary:
Virtual Width Networks VWN enhance model efficiency by expanding representational width without increasing computational cost. VWN accelerates optimization and improves loss reduction, showing a log-linear scaling relation between virtual width and loss.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11238
• PDF: https://arxiv.org/pdf/2511.11238

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#NeuralNetworks #DeepLearning #ModelEfficiency #MachineLearning #AI

✨GGBench: A Geometric Generative Reasoning Benchmark for Unified Multimodal Models

📝 Summary:
GGBench is a new benchmark for evaluating geometric generative reasoning in unified multimodal models. It addresses a critical gap by assessing integrated cognitive processes, requiring language comprehension and precise visual generation to actively construct solutions. This sets a rigorous stan...

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11134
• PDF: https://arxiv.org/pdf/2511.11134

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#GGBench #MultimodalAI #GeometricReasoning #GenerativeAI #AIResearch

✨DiscoX: Benchmarking Discourse-Level Translation task in Expert Domains

📝 Summary:
A new benchmark, DiscoX, and evaluation system, Metric-S, are introduced for discourse-level, expert Chinese-English translation. Findings show advanced LLMs still fall short of human performance, underscoring challenges in professional machine translation.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10984
• PDF: https://arxiv.org/pdf/2511.10984

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#MachineTranslation #NLP #LLM #Benchmarking #AI

✨CATS-V2V: A Real-World Vehicle-to-Vehicle Cooperative Perception Dataset with Complex Adverse Traffic Scenarios

📝 Summary:
CATS-V2V is a new real-world dataset for V2V cooperative perception, focusing on complex adverse traffic scenarios. It provides extensive synchronized sensor data, including LiDAR and cameras, from two vehicles across diverse conditions. This dataset supports autonomous driving research.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11168
• PDF: https://arxiv.org/pdf/2511.11168

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#V2V #AutonomousDriving #CooperativePerception #Dataset #ADAS

✨UI2Code^N: A Visual Language Model for Test-Time Scalable Interactive UI-to-Code Generation

📝 Summary:
UI2Code^N is a visual language model trained for interactive UI-to-code generation, editing, and polishing. It uses multi-turn feedback to achieve state-of-the-art performance among open-source models, comparable to leading closed-source solutions.

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.08195
• PDF: https://arxiv.org/pdf/2511.08195
• Project Page: https://zheny2751-dotcom.github.io/ui2code-n.github.io/
• Github: https://zheny2751-dotcom.github.io/ui2code-n.github.io/

🔹 Models citing this paper:
• https://huggingface.co/zai-org/UI2Code_N

✨ Spaces citing this paper:
• https://huggingface.co/spaces/zai-org/UI2Code_N-demo-case

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#UI2Code #VisualLanguageModels #CodeGeneration #AI #SoftwareEngineering

✨MarsRL: Advancing Multi-Agent Reasoning System via Reinforcement Learning with Agentic Pipeline Parallelism

📝 Summary:
MarsRL enhances multi-agent reasoning systems by jointly optimizing all agents through reinforcement learning and agentic pipeline parallelism. This novel approach significantly boosts open-source LLM accuracy on complex tasks, even outperforming larger models on benchmarks like AIME2025.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11373
• PDF: https://arxiv.org/pdf/2511.11373
• Github: https://github.com/liushulinle/MarsRL

🔹 Models citing this paper:
• https://huggingface.co/forestliutc/MarsRL

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#ReinforcementLearning #MultiAgentSystems #LLM #AIResearch #MachineLearning

✨AIonopedia: an LLM agent orchestrating multimodal learning for ionic liquid discovery

📝 Summary:
AIonopedia is an LLM agent that orchestrates multimodal learning for Ionic Liquid discovery. It enables accurate property predictions and molecular design through hierarchical search, validated by real-world wet-lab experiments, significantly accelerating IL discovery.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11257
• PDF: https://arxiv.org/pdf/2511.11257

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#LLMAgents #IonicLiquids #MultimodalLearning #MaterialsScience #AIforScience

✨SpatialThinker: Reinforcing 3D Reasoning in Multimodal LLMs via Spatial Rewards

📝 Summary:
SpatialThinker is a new 3D-aware MLLM that uses RL and dense spatial rewards to significantly improve spatial understanding. It integrates structured spatial grounding and multi-step reasoning, outperforming existing models and GPT-4o on spatial VQA and real-world benchmarks.

🔹 Publication Date: Published on Nov 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07403
• PDF: https://arxiv.org/pdf/2511.07403
• Github: https://github.com/hunarbatra/SpatialThinker

🔹 Models citing this paper:
• https://huggingface.co/OX-PIXL/SpatialThinker-3B
• https://huggingface.co/OX-PIXL/SpatialThinker-7B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/OX-PIXL/STVQA-7K

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#MultimodalLLM #3DReasoning #ReinforcementLearning #AIResearch #ComputerVision

✨DoPE: Denoising Rotary Position Embedding

📝 Summary:
DoPE improves Transformer length generalization by detecting and mitigating noisy frequency bands in positional embeddings. This training-free method enhances retrieval accuracy and reasoning stability across extended contexts up to 64K tokens.

🔹 Publication Date: Published on Nov 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.09146
• PDF: https://arxiv.org/pdf/2511.09146
• Project Page: https://The-physical-picture-of-LLMs.github.io

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#Transformers #PositionalEmbedding #LLMs #DeepLearning #AIResearch

✨LiteAttention: A Temporal Sparse Attention for Diffusion Transformers

📝 Summary:
LiteAttention accelerates video generation by exploiting temporal coherence in diffusion attention. It propagates skip decisions for non-essential attention tiles across denoising steps, eliminating redundant computations. This achieves substantial speedups without quality loss.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11062
• PDF: https://arxiv.org/pdf/2511.11062
• Github: https://github.com/moonmath-ai/LiteAttention

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#DiffusionModels #VideoGeneration #Transformers #SparseAttention #ComputationalEfficiency

✨Large Language Models for Scientific Idea Generation: A Creativity-Centered Survey

📝 Summary:
This survey examines methods for using large language models to generate scientific ideas, categorizing them into five families and aligning them with creativity frameworks to improve scientific sound...

🔹 Publication Date: Published on Nov 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07448
• PDF: https://arxiv.org/pdf/2511.07448

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Qwen3 Technical Report

📝 Summary:
Qwen3 is a new series of large language models integrating thinking and non-thinking modes for unified performance and efficiency. It achieves state-of-the-art results across diverse tasks and expands multilingual support to 119 languages.

🔹 Publication Date: Published on May 14

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/qwen3-technical-report
• PDF: https://arxiv.org/pdf/2505.09388
• Project Page: https://qwenlm.github.io/blog/qwen3/
• Github: https://github.com/QwenLM/Qwen3

🔹 Models citing this paper:
• https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct
• https://huggingface.co/Qwen/Qwen3-235B-A22B
• https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct

✨ Spaces citing this paper:
• https://huggingface.co/spaces/modelscope/DocResearch
• https://huggingface.co/spaces/enzostvs/deepsite
• https://huggingface.co/spaces/multimodalart/Eigen-Banana

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

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#LLM #AI #MultilingualAI #NLP #Qwen3

✨WEAVE: Unleashing and Benchmarking the In-context Interleaved Comprehension and Generation

📝 Summary:
WEAVE introduces a suite with a large dataset and benchmark to assess multi-turn context-dependent image generation and editing in multimodal models. It enables new capabilities like visual memory in models while exposing current limitations in these complex tasks.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11434
• PDF: https://arxiv.org/pdf/2511.11434
• Project Page: https://weichow23.github.io/weave/
• Github: https://github.com/weichow23/weave

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

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#MultimodalAI #ImageGeneration #GenerativeAI #ComputerVision #AIResearch

✨HI-TransPA: Hearing Impairments Translation Personal Assistant

📝 Summary:
HI-TransPA, an instruction-driven audio-visual personal assistant, uses Omni-Model paradigm to translate and dialogue by fusing speech with lip dynamics, achieving state-of-the-art performance in assi...

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.09915
• PDF: https://arxiv.org/pdf/2511.09915

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Simulating the Visual World with Artificial Intelligence: A Roadmap

📝 Summary:
Video generation is evolving towards foundation models that integrate world simulation and rendering to produce physically plausible and interactive videos. AI-generated summary The landscape of video...

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.08585
• PDF: https://arxiv.org/pdf/2511.08585
• Github: https://github.com/ziqihuangg/Awesome-From-Video-Generation-to-World-Model

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Workload Schedulers -- Genesis, Algorithms and Differences

📝 Summary:
This paper categorizes modern workload schedulers into three classes: OS, Cluster, and Big Data. It details their evolution, algorithms, and differences. The conclusion highlights similarities in scheduling strategy design across both local and distributed systems.

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10258
• PDF: https://arxiv.org/pdf/2511.10258

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#WorkloadScheduling #OperatingSystems #DistributedComputing #SchedulingAlgorithms #ComputerScience

✨MediaPipe: A Framework for Building Perception Pipelines

📝 Summary:
MediaPipe is a framework for building perception applications, offering tools to combine components, prototype, and measure performance across platforms. It helps developers iteratively improve AI models with reproducible results.

🔹 Publication Date: Published on Jun 14, 2019

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/1906.08172
• PDF: https://arxiv.org/pdf/1906.08172
• Github: https://github.com/google-ai-edge/mediapipe

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Building the Web for Agents: A Declarative Framework for Agent-Web Interaction

📝 Summary:
VOIX is a web framework using declarative HTML tags like tool and context for websites to explicitly define AI agent capabilities. This enables reliable, privacy-preserving, and secure agent interaction with human-oriented interfaces, fostering the Agentic Web.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11287
• PDF: https://arxiv.org/pdf/2511.11287

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#AgenticWeb #AIAgents #WebFramework #DeclarativeAI #FutureofWeb

✨Don't Waste It: Guiding Generative Recommenders with Structured Human Priors via Multi-head Decoding

📝 Summary:
A framework integrates human priors into end-to-end generative recommenders, enhancing accuracy and beyond-accuracy objectives by leveraging lightweight adapter heads and hierarchical composition stra...

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10492
• PDF: https://arxiv.org/pdf/2511.10492
• Github: https://github.com/zhykoties/Multi-Head-Recommendation-with-Human-Priors

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

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#AI #DataScience #MachineLearning #HuggingFace #Research