✨CC30k: A Citation Contexts Dataset for Reproducibility-Oriented Sentiment Analysis

📝 Summary:
CC30k is a new dataset of 30,000 machine learning paper citation contexts, labeled with reproducibility-oriented sentiments. It enables large language models to better predict paper reproducibility, filling a crucial gap in computational reproducibility studies.

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07790
• PDF: https://arxiv.org/pdf/2511.07790

✨ Datasets citing this paper:
• https://huggingface.co/datasets/rochanaro/CC30k

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#MachineLearning #Reproducibility #LLM #SentimentAnalysis #DataScience

✨MM-CRITIC: A Holistic Evaluation of Large Multimodal Models as Multimodal Critique

📝 Summary:
MM-CRITIC is a new benchmark evaluating Large Multimodal Models critique abilities across various dimensions and tasks. It uses expert-informed ground answers and GPT-4o for reliable scoring. This benchmark provides a comprehensive assessment of leading LMMs' critique capabilities.

🔹 Publication Date: Published on Nov 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.09067
• PDF: https://arxiv.org/pdf/2511.09067

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#LMMs #MultimodalAI #AIEvaluation #Benchmarking #AIResearch

✨Beyond Outlining: Heterogeneous Recursive Planning for Adaptive Long-form Writing with Language Models

📝 Summary:
This paper proposes an AI agent framework for adaptive long-form writing. It uses recursive task decomposition and dynamically integrates retrieval, reasoning, and composition, overcoming rigid outline-based methods. The framework consistently outperforms state-of-the-art approaches.

🔹 Publication Date: Published on Mar 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.08275
• PDF: https://arxiv.org/pdf/2503.08275
• Github: https://github.com/principia-ai/WriteHERE

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#AI #LanguageModels #LongformWriting #NLP #GenerativeAI

🤖🧠 Steel Browser: The Open-Source Browser API Powering AI Agents and Automation

🗓️ 16 Nov 2025
📚 AI News & Trends

The evolution of artificial intelligence has ushered in a new era of automation where AI agents can perform complex digital tasks with minimal human intervention. However, one of the biggest challenges for developers building these systems is browser automation managing sessions, proxies, cookies and debugging environments. This is where Steel Browser comes into play. Steel ...

#SteelBrowser #OpenSource #BrowserAutomation #AIAgents #WebScraping #DigitalAutomation

✨Transformer Explainer: Interactive Learning of Text-Generative Models

📝 Summary:
Transformer Explainer is an interactive web tool for non-experts to understand the GPT-2 model. It allows real-time experimentation with user input, visualizing how internal components predict text. This broadens access to education about modern generative AI.

🔹 Publication Date: Published on Aug 8, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2408.04619
• PDF: https://arxiv.org/pdf/2408.04619
• Project Page: https://poloclub.github.io/transformer-explainer/
• Github: https://github.com/helblazer811/ManimML

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#AI #GenerativeAI #Transformers #AIeducation #ExplainableAI

🤖🧠 Skyvern: The Future of Browser Automation Powered by AI and Computer Vision

🗓️ 16 Nov 2025
📚 AI News & Trends

In today’s fast-evolving digital landscape, automation plays a crucial role in enhancing productivity, efficiency and innovation. Yet, traditional browser automation tools often struggle with complexity, maintenance and reliability. They rely heavily on DOM parsing, XPaths and rigid noscripts that easily break when websites change their layout. Enter Skyvern, an open-source, AI-driven browser automation platform developed ...

#Skyvern #BrowserAutomation #AIDriven #ComputerVision #OpenSource #WebAutomation

🤖🧠 OpenAI Evals: The Framework Transforming LLM Evaluation and Benchmarking

🗓️ 16 Nov 2025
📚 AI News & Trends

As large language models (LLMs) continue to reshape industries from education and healthcare to marketing and software development – the need for reliable evaluation methods has never been greater. With new models constantly emerging, developers and researchers require a standardized system to test, compare and understand model performance across real-world scenarios. This is where OpenAI ...

#OpenAIEvals #LLMEvaluation #Benchmarking #LargeLanguageModels #AIResearch #ModelEvaluation

🤖🧠 Context Engineering 2.0: Redefining Human–Machine Understanding

🗓️ 16 Nov 2025
📚 AI News & Trends

As artificial intelligence advances, machines are becoming increasingly capable of understanding and responding to human language. Yet, one crucial challenge remains how can machines truly understand the context behind human intentions? This question forms the foundation of context engineering, a discipline that focuses on designing, organizing and managing contextual information so that AI systems can ...

#ContextEngineering #AIEducation #HumanMachineUnderstanding #AIContext #NaturalLanguageProcessing #AIModels

✨EmoVid: A Multimodal Emotion Video Dataset for Emotion-Centric Video Understanding and Generation

📝 Summary:
EmoVid is a new multimodal, emotion-annotated video dataset designed for creative media like cartoons and movies. It bridges emotion understanding with video generation, significantly improving emotional expression and quality in generated videos. EmoVid establishes a new benchmark for affective ...

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11002
• PDF: https://arxiv.org/pdf/2511.11002

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#EmoVid #MultimodalAI #EmotionAI #VideoGeneration #VideoUnderstanding

✨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