✨What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity

📝 Summary:
Ideation diversity significantly enhances AI research agent performance. Higher ideation diversity leads to stronger results on the MLE-bench benchmark across different models and scaffolds. This finding holds across various performance metrics.

🔹 Publication Date: Published on Nov 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15593
• PDF: https://arxiv.org/pdf/2511.15593

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#AIResearch #IdeationDiversity #MachineLearning #AIagents #AIPerformance

✨V-ReasonBench: Toward Unified Reasoning Benchmark Suite for Video Generation Models

📝 Summary:
V-ReasonBench is a new benchmark to evaluate generative video models' reasoning across structured problem-solving, spatial cognition, pattern inference, and physical dynamics. It uses diverse tasks to reveal dimension-wise differences in models, aiming to support development of human-aligned reas...

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16668
• PDF: https://arxiv.org/pdf/2511.16668
• Project Page: https://oahzxl.github.io/VReasonBench/
• Github: https://github.com/yangluo7/V-ReasonBench

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#VideoGeneration #AIReasoning #GenerativeAI #Benchmarking #MachineLearning

✨Video-as-Answer: Predict and Generate Next Video Event with Joint-GRPO

📝 Summary:
VANS is a new model for Video-Next-Event Prediction VNEP that generates dynamic, visually and semantically accurate video responses. It uses reinforcement learning to align a Vision-Language Model with a Video Diffusion Model, achieving state-of-the-art performance.

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16669
• PDF: https://arxiv.org/pdf/2511.16669
• Project Page: https://video-as-answer.github.io/
• Github: https://github.com/KlingTeam/VANS

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#VideoAI #GenerativeAI #MachineLearning #ComputerVision #DeepLearning

✨Scaling Spatial Intelligence with Multimodal Foundation Models

📝 Summary:
SenseNova-SI is a new scaled multimodal foundation model that achieves superior spatial intelligence. By using 8 million diverse data samples, it sets unprecedented performance on various spatial benchmarks. The models are publicly released to foster further research.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13719
• PDF: https://arxiv.org/pdf/2511.13719
• Project Page: https://huggingface.co/sensenova/SenseNova-SI-1.1-InternVL3-8B
• Github: https://github.com/OpenSenseNova/SenseNova-SI

🔹 Models citing this paper:
• https://huggingface.co/sensenova/SenseNova-SI-InternVL3-8B
• https://huggingface.co/sensenova/SenseNova-SI-InternVL3-2B
• https://huggingface.co/sensenova/SenseNova-SI-1.1-InternVL3-2B

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#MultimodalAI #FoundationModels #SpatialIntelligence #ComputerVision #AI

✨Step-Audio-R1 Technical Report

📝 Summary:
Step-Audio-R1 is the first audio reasoning model. It uses Modality-Grounded Reasoning Distillation to achieve strong audio reasoning, outperforming previous models. This demonstrates that reasoning capabilities are transferable across different modalities.

🔹 Publication Date: Published on Nov 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15848
• PDF: https://arxiv.org/pdf/2511.15848

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#AudioReasoning #MultimodalAI #AIResearch #MachineLearning #AudioAI

✨First Frame Is the Place to Go for Video Content Customization

📝 Summary:
The first frame in video generation models functions as a conceptual memory buffer, storing visual elements for later reuse. This enables robust video content customization with minimal training examples, without major model changes.

🔹 Publication Date: Published on Nov 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15700
• PDF: https://arxiv.org/pdf/2511.15700
• Project Page: https://firstframego.github.io
• Github: http://firstframego.github.io

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#VideoGeneration #GenerativeAI #ComputerVision #DeepLearning #AICustomization

✨MiMo-Embodied: X-Embodied Foundation Model Technical Report

📝 Summary:
MiMo-Embodied is the first cross-embodied foundation model. It achieves state-of-the-art performance in both autonomous driving and embodied AI, demonstrating positive transfer through multi-stage learning and fine-tuning.

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16518
• PDF: https://arxiv.org/pdf/2511.16518
• Github: https://github.com/XiaomiMiMo/MiMo-Embodied

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#FoundationModels #EmbodiedAI #AutonomousDriving #AI #Robotics

✨SAM 3D: 3Dfy Anything in Images

📝 Summary:
SAM 3D reconstructs 3D objects from single images, predicting geometry, texture, and layout. It uses a multi-stage training framework with synthetic pretraining and real-world alignment, breaking the 3D data barrier and achieving high human preference.

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16624
• PDF: https://arxiv.org/pdf/2511.16624
• Project Page: https://ai.meta.com/sam3d/
• Github: https://github.com/facebookresearch/sam-3d-objects

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#3DReconstruction #ComputerVision #AI #DeepLearning #SingleImage3D

✨Thinking-while-Generating: Interleaving Textual Reasoning throughout Visual Generation

📝 Summary:
Thinking-while-Generating TwiG interleaves textual reasoning throughout the visual generation process. This on-the-fly multimodal interaction guides and reflects on visual content as it is created, resulting in more context-aware and semantically rich outputs.

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16671
• PDF: https://arxiv.org/pdf/2511.16671
• Project Page: https://think-while-gen.github.io/
• Github: https://github.com/ZiyuGuo99/Thinking-while-Generating

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

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#GenerativeAI #MultimodalAI #ComputerVision #NLP #AIResearch

✨Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs

📝 Summary:
Nemotron Elastic embeds multiple submodels within a single large language model, significantly reducing training costs by 360x compared to training separate models. This framework allows zero-shot extraction of optimized submodels for various deployment budgets without additional training or fine...

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16664
• PDF: https://arxiv.org/pdf/2511.16664
• Project Page: https://huggingface.co/nvidia/Nemotron-Elastic-12B

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#LLM #AI #MachineLearning #DeepLearning #EfficientAI

✨TimeViper: A Hybrid Mamba-Transformer Vision-Language Model for Efficient Long Video Understanding

📝 Summary:
TimeViper is a hybrid Mamba-Transformer vision-language model for efficient long video understanding. It introduces a TransV module to compress redundant vision tokens into instruction tokens, enabling it to process over 10,000 frames. This achieves state-of-the-art performance while offering new...

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16595
• PDF: https://arxiv.org/pdf/2511.16595
• Project Page: https://xuboshen.github.io/TimeViper/

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#TimeViper #VisionLanguageModels #VideoUnderstanding #MambaTransformer #DeepLearning

✨SAM2S: Segment Anything in Surgical Videos via Semantic Long-term Tracking

📝 Summary:
SAM2S is a foundation model enhancing interactive video object segmentation in surgery. It leverages a new large benchmark, robust memory, and temporal learning to achieve superior accuracy 80.42 J and F and real-time performance in surgical video analysis.

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16618
• PDF: https://arxiv.org/pdf/2511.16618
• Project Page: https://jinlab-imvr.github.io/SAM2S
• Github: https://github.com/jinlab-imvr/SAM2S

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

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#SurgicalAI #MedicalImaging #ComputerVision #FoundationModels #DeepLearning

✨NaTex: Seamless Texture Generation as Latent Color Diffusion

📝 Summary:
NaTex directly generates 3D textures using latent color diffusion and geometry-aware models. It predicts texture color in 3D space, outperforming prior methods in coherence and alignment by avoiding 2D multi-view limitations.

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16317
• PDF: https://arxiv.org/pdf/2511.16317
• Project Page: https://natex-ldm.github.io/
• Github: https://natex-ldm.github.io/

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

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#TextureGeneration #DiffusionModels #3DGraphics #ComputerVision #DeepLearning

✨PartUV: Part-Based UV Unwrapping of 3D Meshes

📝 Summary:
PartUV is a novel UV unwrapping pipeline for noisy AI-generated 3D meshes. It uses part decomposition and geometric heuristics to generate significantly fewer, part-aligned charts with low distortion. PartUV outperforms existing methods in chart count and seam length on diverse datasets.

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16659
• PDF: https://arxiv.org/pdf/2511.16659
• Project Page: https://www.zhaoningwang.com/PartUV/

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

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#UVUnwrapping #3DMeshes #ComputerGraphics #GeometricProcessing #AI

✨TurkColBERT: A Benchmark of Dense and Late-Interaction Models for Turkish Information Retrieval

📝 Summary:
TurkColBERT, the first benchmark for Turkish IR, shows late-interaction models significantly outperform dense encoders. They offer superior parameter efficiency, faster indexing, and better performance for Turkish retrieval tasks.

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16528
• PDF: https://arxiv.org/pdf/2511.16528

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

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#InformationRetrieval #TurkishNLP #MachineLearning #DeepLearning #Benchmarking

✨SRPO: Self-Referential Policy Optimization for Vision-Language-Action Models

📝 Summary:
SRPO is a VLA-RL framework that eliminates the need for expert demonstrations. It assigns progress-wise rewards to failed trajectories using latent world representations and the models own successes. This achieved 99.2% success on LIBERO, a significant improvement.

🔹 Publication Date: Published on Nov 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15605
• PDF: https://arxiv.org/pdf/2511.15605

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

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#ReinforcementLearning #VLAModels #PolicyOptimization #AIResearch #MachineLearning

✨Draft and Refine with Visual Experts

📝 Summary:
The Draft and Refine DnR framework improves visual grounding in LVLMs. It uses a novel question-conditioned utilization metric to measure visual evidence reliance. DnR refines responses with external visual experts, reducing hallucinations and boosting accuracy.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11005
• PDF: https://arxiv.org/pdf/2511.11005
• Github: https://github.com/EavnJeong/Draft-and-Refine-with-Visual-Experts

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#LVLMs #VisualGrounding #AIHallucinations #ComputerVision #DeepLearning

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✨BioBench: A Blueprint to Move Beyond ImageNet for Scientific ML Benchmarks

📝 Summary:
ImageNet accuracy poorly predicts performance on scientific imagery. BioBench is a new ecology vision benchmark unifying diverse tasks, kingdoms, and modalities with 3.1M images, offering a better evaluation for scientific ML.

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16315
• PDF: https://arxiv.org/pdf/2511.16315
• Project Page: https://samuelstevens.me/biobench
• Github: https://github.com/samuelstevens/biobench

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#BioBench #MachineLearning #ComputerVision #ScientificML #Ecology

✨EntroPIC: Towards Stable Long-Term Training of LLMs via Entropy Stabilization with Proportional-Integral Control

📝 Summary:
EntroPIC stabilizes entropy during long-term LLM training by adaptively tuning loss coefficients with Proportional-Integral Control. This novel method ensures efficient exploration and prevents sub-optimal behaviors, leading to stable and optimal reinforcement learning for LLMs.

🔹 Publication Date: Published on Nov 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15248
• PDF: https://arxiv.org/pdf/2511.15248
• Project Page: https://huggingface.co/spaces/yangkaiSIGS/entropic
• Github: https://github.com/yk7333/EntroPIC

🔹 Models citing this paper:
• https://huggingface.co/hunterbown/shannon-control-unit

✨ Spaces citing this paper:
• https://huggingface.co/spaces/yangkaiSIGS/entropic

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

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#LLM #MachineLearning #ReinforcementLearning #ControlTheory #DeepLearning