✨Agentic Refactoring: An Empirical Study of AI Coding Agents

📝 Summary:
A study of AI agent-generated refactoring in Java projects found agents frequently perform low-level consistency edits. Driven by maintainability and readability, these refactorings lead to small but significant improvements in code quality metrics like class size and complexity.

🔹 Publication Date: Published on Nov 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.04824
• PDF: https://arxiv.org/pdf/2511.04824

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#AIagents #CodeRefactoring #SoftwareEngineering #CodeQuality #AIResearch

✨LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls

📝 Summary:
LoopTool is an automated framework that closes the data-training loop for LLMs. It iteratively refines data and models to improve tool-use capabilities, achieving state-of-the-art results and surpassing larger models cost-effectively.

🔹 Publication Date: Published on Nov 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.09148
• PDF: https://arxiv.org/pdf/2511.09148
• Github: https://github.com/Rednote-ExperienceAI-Lab/LoopTool

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#LLM #AI #MachineLearning #DataScience #ToolUse

✨MathSE: Improving Multimodal Mathematical Reasoning via Self-Evolving Iterative Reflection and Reward-Guided Fine-Tuning

📝 Summary:
MathSE improves MLLMs math reasoning by iteratively refining them. It uses inference, reflection, and reward-based feedback instead of static datasets. This significantly boosts performance on tough benchmarks, outperforming leading open-source models.

🔹 Publication Date: Published on Nov 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06805
• PDF: https://arxiv.org/pdf/2511.06805
• Project Page: https://zheny2751-dotcom.github.io/MathSE.github.io/
• Github: https://github.com/zheny2751-dotcom/MathSE

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#MathematicalReasoning #MLLMs #AI #MachineLearning #ReinforcementLearning

✨WebVIA: A Web-based Vision-Language Agentic Framework for Interactive and Verifiable UI-to-Code Generation

📝 Summary:
WebVIA is an agentic framework that automates interactive UI-to-Code generation and validation. It overcomes static UI code limitations by generating verifiable, executable HTML/CSS/JavaScript, outperforming base models in accuracy and interactivity.

🔹 Publication Date: Published on Nov 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06251
• PDF: https://arxiv.org/pdf/2511.06251
• Project Page: https://zheny2751-dotcom.github.io/webvia.github.io/
• Github: https://github.com/zheny2751-dotcom/WebVIA

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#AICodeGeneration #UIGeneration #WebDevelopment #VisionLanguageAI #AgenticAI

✨Toward the Frontiers of Reliable Diffusion Sampling via Adversarial Sinkhorn Attention Guidance

📝 Summary:
ASAG is a novel diffusion guidance method that uses optimal transport and the Sinkhorn algorithm to adversarially disrupt attention scores. It weakens misleading attention alignments by injecting an adversarial cost, improving sample quality, controllability, and fidelity without model retraining.

🔹 Publication Date: Published on Nov 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07499
• PDF: https://arxiv.org/pdf/2511.07499

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#DiffusionModels #AdversarialAI #OptimalTransport #GenerativeAI #DeepLearning

✨Stemming Hallucination in Language Models Using a Licensing Oracle

📝 Summary:
This study presents the Licensing Oracle, an architectural solution to eliminate language model hallucinations. It enforces truth constraints via formal validation against structured knowledge graphs, achieving perfect abstention precision and zero false answers where statistical methods fail.

🔹 Publication Date: Published on Nov 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06073
• PDF: https://arxiv.org/pdf/2511.06073

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#LLM #AIHallucination #KnowledgeGraphs #NLP #AIResearch

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✨Efficient Guided Generation for Large Language Models

📝 Summary:
This paper introduces an efficient method to guide large language model text generation. It uses regular expressions and context-free grammars with minimal added overhead, making guided generation practical.

🔹 Publication Date: Published on Jul 19, 2023

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2307.09702
• PDF: https://arxiv.org/pdf/2307.09702
• Github: https://github.com/normal-computing/outlines

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#LLMs #TextGeneration #NLP #AI #DeepLearning

✨MADD: Multi-Agent Drug Discovery Orchestra

📝 Summary:
MADD is a multi-agent system integrating LLMs and specialized models to enhance hit identification in drug discovery. It builds customized pipelines from natural language queries, demonstrating superior performance and accessibility for researchers.

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.08217
• PDF: https://arxiv.org/pdf/2511.08217
• Github: https://github.com/sb-ai-lab/MADD

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ITMO-NSS/MADD_Benchmark_and_results

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#DrugDiscovery #MultiAgentSystems #LLMs #AI #AIforScience

✨Adapting Web Agents with Synthetic Supervision

📝 Summary:
SynthAgent enhances web agent adaptation by improving synthetic data quality. It refines synthesized tasks and cleans collected trajectories to prevent hallucinations and noise. This dual refinement approach enables better performance on new websites.

🔹 Publication Date: Published on Nov 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06101
• PDF: https://arxiv.org/pdf/2511.06101
• Project Page: https://github.com/aiming-lab/SynthAgent
• Github: https://github.com/aiming-lab/SynthAgent

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#WebAgents #SyntheticData #MachineLearning #AIResearch #DataQuality

✨Motif 2 12.7B technical report

📝 Summary:
Motif-2-12.7B is an efficient LLM combining Grouped Differential Attention and system-level optimizations. It achieves competitive performance across diverse benchmarks with a smaller model size.

🔹 Publication Date: Published on Nov 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07464
• PDF: https://arxiv.org/pdf/2511.07464

🔹 Models citing this paper:
• https://huggingface.co/Motif-Technologies/optimizer
• https://huggingface.co/Motif-Technologies/Motif-2-12.7B-Instruct
• https://huggingface.co/Motif-Technologies/Motif-2-12.7B-Base

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

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#LLM #AI #DeepLearning #EfficientAI #AttentionMechanisms

✨Depth Anything 3: Recovering the Visual Space from Any Views

📝 Summary:
Depth Anything 3 DA3 predicts spatially consistent geometry from any visual inputs, even without known camera poses. It uses a plain transformer backbone and a singular depth-ray prediction target. DA3 achieves new state-of-the-art results on a visual geometry benchmark, outperforming previous mo...

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10647
• PDF: https://arxiv.org/pdf/2511.10647

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#ComputerVision #DepthEstimation #AIResearch #Transformers #3DReconstruction

✨MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples

📝 Summary:
MuSc-V2 framework improves zero-shot anomaly detection by leveraging mutual scoring and similarity aggregation in both 2D and 3D data, achieving significant performance gains over existing benchmarks....

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10047
• PDF: https://arxiv.org/pdf/2511.10047

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨UniVA: Universal Video Agent towards Open-Source Next-Generation Video Generalist

📝 Summary:
UniVA is an open-source multi-agent framework that unifies video understanding, segmentation, editing, and generation. It uses a Plan-and-Act architecture with hierarchical memory to enable complex, iterative video workflows. This system aims to advance agentic video intelligence.

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.08521
• PDF: https://arxiv.org/pdf/2511.08521
• Project Page: https://univa.online/
• Github: https://github.com/univa-agent/univa

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#VideoAI #AIagents #GenerativeAI #ComputerVision #OpenSource

✨Black-Box On-Policy Distillation of Large Language Models

📝 Summary:
Generative Adversarial Distillation GAD is a new black-box on-policy method for distilling LLMs. GAD trains a student generator and a discriminator for adaptive feedback, surpassing traditional distillation. It enables student LLMs to perform comparably to proprietary teachers.

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10643
• PDF: https://arxiv.org/pdf/2511.10643

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

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#LLMs #AIDistillation #MachineLearning #GenerativeAI #DeepLearning

✨AlphaResearch: Accelerating New Algorithm Discovery with Language Models

📝 Summary:
AlphaResearch is an autonomous agent that discovers new algorithms using a dual research environment. It achieved a 2/8 win rate against human researchers and found a best-of-known solution for the packing circles problem, showing LLMs potential for algorithm discovery.

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.08522
• PDF: https://arxiv.org/pdf/2511.08522
• Github: https://github.com/answers111/alpha-research

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#AlgorithmDiscovery #LLMs #AIResearch #AutonomousAgents #MachineLearning

✨Music Flamingo: Scaling Music Understanding in Audio Language Models

📝 Summary:
Music Flamingo, a large audio-language model, advances music understanding through fine-tuning on a rich dataset and post-training with novel methods, achieving state-of-the-art results across various...

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10289
• PDF: https://arxiv.org/pdf/2511.10289

🔹 Models citing this paper:
• https://huggingface.co/nvidia/music-flamingo-hf

✨ Spaces citing this paper:
• https://huggingface.co/spaces/nvidia/music-flamingo

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Superpositional Gradient Descent: Harnessing Quantum Principles for Model Training

📝 Summary:
Superpositional Gradient Descent SGD is a new quantum-inspired optimizer. It uses quantum superposition to enhance gradient updates, leading to faster convergence and lower final loss in LLM training than AdamW.

🔹 Publication Date: Published on Nov 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.01918
• PDF: https://arxiv.org/pdf/2511.01918
• Github: https://github.com/The-Aqua-Labs/Superpositional-Gradient-Descent

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#MachineLearning #AI #LLM #QuantumInspired #Optimization

✨One Small Step in Latent, One Giant Leap for Pixels: Fast Latent Upscale Adapter for Your Diffusion Models

📝 Summary:
LUA performs efficient super-resolution directly in diffusion models' latent space. This lightweight module enables faster, high-quality image synthesis by upscaling before VAE decoding, cutting time versus pixel-space methods, and generalizing across VAEs.

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10629
• PDF: https://arxiv.org/pdf/2511.10629

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#DiffusionModels #SuperResolution #LatentSpace #ImageGeneration #AIResearch

✨Benchmarking Diversity in Image Generation via Attribute-Conditional Human Evaluation

📝 Summary:
This paper introduces a framework to robustly evaluate diversity in text-to-image models. It uses a novel human evaluation template, curated prompts with variation factors, and systematic analysis of image embeddings to rank models and identify diversity weaknesses.

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10547
• PDF: https://arxiv.org/pdf/2511.10547

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#ImageGeneration #TextToImage #AIDiversity #Benchmarking #HumanEvaluation