✨GroupRank: A Groupwise Reranking Paradigm Driven by Reinforcement Learning

📝 Summary:
GroupRank introduces a novel groupwise reranking paradigm addressing limitations of pointwise and listwise methods. It processes queries with document groups to assign comparative relevance scores, combining flexibility with global context. Trained via reinforcement learning and synthesized data,...

🔹 Publication Date: Published on Nov 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11653
• PDF: https://arxiv.org/pdf/2511.11653

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#Reranking #ReinforcementLearning #InformationRetrieval #MachineLearning #DataScience

✨TiViBench: Benchmarking Think-in-Video Reasoning for Video Generative Models

📝 Summary:
TiViBench is a new benchmark assessing image-to-video models reasoning across four dimensions and 24 tasks. Commercial models show stronger reasoning potential. VideoTPO, a test-time strategy, significantly enhances performance, advancing reasoning in video generation.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13704
• PDF: https://arxiv.org/pdf/2511.13704
• Project Page: https://haroldchen19.github.io/TiViBench-Page/
• Github: https://haroldchen19.github.io/TiViBench-Page/

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#VideoGeneration #AIBenchmark #ComputerVision #DeepLearning #AIResearch

✨PhysX-Anything: Simulation-Ready Physical 3D Assets from Single Image

📝 Summary:
PhysX-Anything generates simulation-ready physical 3D assets from single images, crucial for embodied AI. It uses a novel VLM-based model and an efficient 3D representation, enabling direct use in robotic policy learning.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13648
• PDF: https://arxiv.org/pdf/2511.13648
• Project Page: https://physx-anything.github.io/
• Github: https://github.com/ziangcao0312/PhysX-Anything

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Caoza/PhysX-Mobility

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#EmbodiedAI #3DReconstruction #Robotics #ComputerVision #AIResearch

✨Part-X-MLLM: Part-aware 3D Multimodal Large Language Model

📝 Summary:
Part-X-MLLM is a 3D multimodal large language model that unifies diverse 3D tasks by generating structured programs from RGB point clouds and language prompts. It outputs part-level data and edit commands, enabling state-of-the-art 3D generation and editing through one interface.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13647
• PDF: https://arxiv.org/pdf/2511.13647
• Project Page: https://chunshi.wang/Part-X-MLLM/

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#3D #MLLM #GenerativeAI #ComputerVision #AIResearch

✨OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation

📝 Summary:
OlmoEarth is a novel multimodal spatio-temporal foundation model for Earth observation data. It employs new self-supervised learning methods to achieve state-of-the-art performance on many tasks. It is deployed as a platform for non-profits and NGOs.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13655
• PDF: https://arxiv.org/pdf/2511.13655
• Project Page: https://olmoearth.allenai.org/
• Github: https://github.com/allenai/olmoearth_pretrain

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#EarthObservation #FoundationModels #AI #RemoteSensing #SelfSupervisedLearning

✨Live-SWE-agent: Can Software Engineering Agents Self-Evolve on the Fly?

📝 Summary:
Live-SWE-agent is the first live software engineering agent that autonomously and continuously evolves itself on-the-fly during runtime. It starts with basic tools and refines its own implementation while solving problems. It achieves 75.4% on SWE-bench Verified and 45.8% on SWE-Bench Pro, outper...

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13646
• PDF: https://arxiv.org/pdf/2511.13646

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#SoftwareEngineering #AI #AutonomousAgents #SelfEvolvingAI #LiveSWEagent

✨WebCoach: Self-Evolving Web Agents with Cross-Session Memory Guidance

📝 Summary:
WebCoach introduces a self-evolving framework for web agents with persistent cross-session memory. It uses a WebCondenser, External Memory Store, and a Coach to learn from past experiences without retraining. This significantly improves task success and enables smaller models to match larger LLM ...

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12997
• PDF: https://arxiv.org/pdf/2511.12997

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#WebAgents #AI #MachineLearning #LLM #MemoryAI

✨MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling

📝 Summary:
MiroThinker v1.0 is an open-source research agent introducing 'interactive scaling.' It trains models with reinforcement learning for deeper agent-environment interactions, performing up to 600 tool calls per task. This achieves state-of-the-art performance and establishes interaction depth as a ...

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11793
• PDF: https://arxiv.org/pdf/2511.11793
• Project Page: https://dr.miromind.ai/
• Github: https://github.com/MiroMindAI/MiroThinker

🔹 Models citing this paper:
• https://huggingface.co/miromind-ai/MiroThinker-v1.0-72B
• https://huggingface.co/miromind-ai/MiroThinker-v1.0-8B
• https://huggingface.co/miromind-ai/MiroThinker-v1.0-30B

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#MiroThinker #ResearchAgents #ReinforcementLearning #OpenSourceAI #LLM

✨P1: Mastering Physics Olympiads with Reinforcement Learning

📝 Summary:
P1 is a family of open-source physics reasoning models trained via reinforcement learning. P1-235B-A22B achieved Gold-medal performance at IPhO 2025 and won 12 other competitions. These models also show strong generalizability on other reasoning tasks.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13612
• PDF: https://arxiv.org/pdf/2511.13612
• Project Page: https://prime-rl.github.io/P1/
• Github: https://github.com/PRIME-RL/P1

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#ReinforcementLearning #Physics #AI #MachineLearning #OpenSource

✨MicroVQA++: High-Quality Microscopy Reasoning Dataset with Weakly Supervised Graphs for Multimodal Large Language Model

📝 Summary:
MicroVQA plus plus is a new high-quality microscopy VQA dataset built via a three-stage process. This includes HiCQA-Graph, a novel filtering method using NLI, CLIP, and MLLM signals. The dataset enables strong microscopy reasoning performance for MLLMs.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11407
• PDF: https://arxiv.org/pdf/2511.11407
• Github: https://github.com/ieellee/MicroVQA-PlusPlus

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#MLLM #Microscopy #VQA #AIResearch #Dataset

✨Souper-Model: How Simple Arithmetic Unlocks State-of-the-Art LLM Performance

📝 Summary:
SoCE is a novel model souping technique that boosts LLM performance. It uses non-uniform weighted averaging of expert models identified for specific benchmark categories, unlike uniform methods. This leads to state-of-the-art results and improved robustness.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13254
• PDF: https://arxiv.org/pdf/2511.13254

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#LLMs #ModelSouping #MachineLearning #AI #StateOfTheArt

✨Instella: Fully Open Language Models with Stellar Performance

📝 Summary:
Instella is a family of fully open language models trained on open data. It achieves state-of-the-art among fully open models and competes with leading open-weight LLMs. Specialized variants for long context and math reasoning are also offered.

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10628
• PDF: https://arxiv.org/pdf/2511.10628
• Github: https://github.com/AMD-AGI/Instella

🔹 Models citing this paper:
• https://huggingface.co/amd/AMD-OLMo
• https://huggingface.co/amd/Instella-3B-Instruct
• https://huggingface.co/amd/Instella-3B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/amd/Instella-Long
• https://huggingface.co/datasets/amd/Instella-GSM8K-synthetic

✨ Spaces citing this paper:
• https://huggingface.co/spaces/DexterSptizu/AMD-OLMo-1B
• https://huggingface.co/spaces/universeofml/DeepFocusTrain

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#LLMs #OpenSource #AI #MachineLearning #NLP

✨Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs

📝 Summary:
EvoSynth is a new framework that autonomously engineers and evolves novel, code-based jailbreak methods for LLMs, moving beyond prompt refinement. It uses self-correction to create diverse and highly successful attacks, achieving 85.5% ASR against robust models.

🔹 Publication Date: Published on Nov 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12710
• PDF: https://arxiv.org/pdf/2511.12710

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#LLMs #JailbreakAttacks #AISecurity #EvolutionaryAlgorithms #AIResearch

✨Dynamic Reflections: Probing Video Representations with Text Alignment

📝 Summary:
This work presents the first comprehensive study on video-text representation alignment. It reveals alignment depends on data richness and correlates with downstream task performance, suggesting its value for general video understanding. This introduces video-text alignment as a zero-shot method ...

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02767
• PDF: https://arxiv.org/pdf/2511.02767
• Github: https://video-prh.github.io/

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#VideoUnderstanding #TextAlignment #VideoTextAI #ZeroShotLearning #RepresentationLearning

✨Back to Basics: Let Denoising Generative Models Denoise

📝 Summary:
Denoising diffusion models should predict clean images directly, not noise, leveraging the data manifold assumption. The paper introduces JiT, a model using simple, large-patch Transformers that achieves competitive generative results on ImageNet.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13720
• PDF: https://arxiv.org/pdf/2511.13720
• Github: https://github.com/LTH14/JiT

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#DiffusionModels #GenerativeAI #DeepLearning #ComputerVision #AIResearch

✨Genomic Next-Token Predictors are In-Context Learners

📝 Summary:
In-context learning ICL emerges organically in genomic sequences through large-scale predictive training, mirroring its behavior in language models. This first evidence suggests ICL is a general phenomenon of large-scale modeling, not exclusive to human language.

🔹 Publication Date: Published on Nov 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12797
• PDF: https://arxiv.org/pdf/2511.12797

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#Genomics #InContextLearning #AI #MachineLearning #LLMs

✨A Decentralized Retrieval Augmented Generation System with Source Reliabilities Secured on Blockchain

📝 Summary:
This paper proposes a decentralized RAG system using a blockchain-based mechanism to score data source reliability. It dynamically evaluates sources, boosting performance by 10.7% compared to centralized systems and achieving 56% cost savings in unreliable environments.

🔹 Publication Date: Published on Nov 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07577
• PDF: https://arxiv.org/pdf/2511.07577
• Github: https://github.com/yining610/Reliable-dRAG

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#RAG #Blockchain #DecentralizedAI #GenerativeAI #AIResearch

✨UFO^3: Weaving the Digital Agent Galaxy

📝 Summary:
UFO^3 unifies diverse digital devices into a single orchestration fabric, enabling AI agents to collaborate seamlessly across platforms. It models tasks dynamically for asynchronous execution, achieving efficient, resilient, and accurate cross-device task orchestration with improved parallelism a...

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11332
• PDF: https://arxiv.org/pdf/2511.11332
• Project Page: https://microsoft.github.io/UFO/
• Github: https://github.com/microsoft/UFO/

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#AIAgents #TaskOrchestration #DistributedSystems #EdgeAI #MultiAgentSystems

✨UnSAMv2: Self-Supervised Learning Enables Segment Anything at Any Granularity

📝 Summary:
UnSAMv2 enables continuous segmentation granularity control for the SAM model without human annotations. It uses self-supervised learning on unlabeled data to discover mask-granularity pairs and a novel control embedding. UnSAMv2 significantly enhances SAM-2s performance across various segmentati...

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13714
• PDF: https://arxiv.org/pdf/2511.13714
• Project Page: https://yujunwei04.github.io/UnSAMv2-Project-Page/
• Github: https://github.com/yujunwei04/UnSAMv2

✨ Spaces citing this paper:
• https://huggingface.co/spaces/yujunwei04/UnSAMv2

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

For more data science resources:
✓ https://news.1rj.ru/str/DataScienceT

#AI #ComputerVision #SelfSupervisedLearning #ImageSegmentation #DeepLearning