✨RAG-Anything: All-in-One RAG Framework

📝 Summary:
RAG-Anything is a unified framework extending RAG to all modalities, not just text. It integrates cross-modal relationships and semantic matching via dual-graph construction and hybrid retrieval. This significantly improves performance on complex multimodal benchmarks.

🔹 Publication Date: Published on Oct 14

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/rag-anything-all-in-one-rag-framework
• PDF: https://arxiv.org/pdf/2510.12323
• Github: https://github.com/HKUDS/RAG-Anything

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

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#RAG #MultimodalAI #MachineLearning #InformationRetrieval #GraphAI

✨PokeeResearch: Effective Deep Research via Reinforcement Learning from AI Feedback and Robust Reasoning Scaffold

📝 Summary:
PokeeResearch-7B is a 7B-parameter deep research agent achieving state-of-the-art results using Reinforcement Learning from AI Feedback RLAIF. Its chain-of-thought reasoning scaffold enhances robustness and alignment, producing an efficient, resilient, and research-grade AI.

🔹 Publication Date: Published on Oct 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.15862
• PDF: https://arxiv.org/pdf/2510.15862
• Github: https://github.com/Pokee-AI/PokeeResearchOSS

🔹 Models citing this paper:
• https://huggingface.co/PokeeAI/pokee_research_7b
• https://huggingface.co/Mungert/pokee_research_7b-GGUF

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

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#AI #ReinforcementLearning #LLM #MachineLearning #AIResearch

✨FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning

📝 Summary:
FAPO improves LLM reasoning by penalizing flawed-positive rollouts, which are unreliable reasoning patterns. This secures early gains while shifting optimization toward reliable reasoning later, enhancing correctness and stability.

🔹 Publication Date: Published on Oct 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.22543
• PDF: https://arxiv.org/pdf/2510.22543
• Project Page: https://fapo-rl.github.io/
• Github: https://fapo-rl.github.io

🔹 Models citing this paper:
• https://huggingface.co/dyyyyyyyy/FAPO-32B
• https://huggingface.co/dyyyyyyyy/FAPO-GenRM-4B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/dyyyyyyyy/FAPO-Critic
• https://huggingface.co/datasets/dyyyyyyyy/FAPO-Reasoning-Dataset

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

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#LLM #AI #ReinforcementLearning #DeepLearning #Reasoning

✨The Unreasonable Effectiveness of Scaling Agents for Computer Use

📝 Summary:
Behavior Best-of-N bBoN improves computer-use agent reliability by generating multiple rollouts and selecting them via behavior narratives. This method achieves state-of-the-art performance on OSWorld and generalizes across operating systems, demonstrating effective CUA scaling.

🔹 Publication Date: Published on Oct 2

🔹 Paper Links:
• arXiv Page: http://arxiv.org/abs/2510.02250
• PDF: https://arxiv.org/pdf/2510.02250
• Project Page: https://www.simular.ai/articles/agent-s3
• Github: http://github.com/simular-ai/Agent-S

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

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#AIAgents #AIScaling #OperatingSystems #BehavioralAI #AIResearch

✨Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents

📝 Summary:
Agent S2 is a compositional framework for computer use agents that delegates tasks across generalist and specialist models. Using Mixture-of-Grounding and Proactive Hierarchical Planning, it achieves state-of-the-art performance on diverse benchmarks and operating systems.

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.00906
• PDF: https://arxiv.org/pdf/2504.00906
• Project Page: https://www.simular.ai/articles/agent-s2-technical-review
• Github: https://github.com/simular-ai/Agent-S

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

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#AIAgents #MachineLearning #AI #GeneralistSpecialist #AutonomousSystems

✨Pico-Banana-400K: A Large-Scale Dataset for Text-Guided Image Editing

📝 Summary:
Pico-Banana-400K is a new 400K-image dataset for text-guided image editing, built from real photos. It offers diverse edit types, high quality, and specialized subsets for multi-turn, preference-based, and long-short instruction editing, enabling comprehensive model development.

🔹 Publication Date: Published on Oct 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.19808
• PDF: https://arxiv.org/pdf/2510.19808
• Github: https://github.com/apple/pico-banana-400k

🔹 Models citing this paper:
• https://huggingface.co/eigen-ai-labs/eigen-banana-qwen-image-edit

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

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#ImageEditing #TextGuidedEditing #Dataset #ComputerVision #AI

✨MIRIX: Multi-Agent Memory System for LLM-Based Agents

📝 Summary:
MIRIX is a modular multi-agent memory system for LLM-based agents that integrates diverse memory types and a dynamic framework. It significantly enhances memory capabilities for multimodal and long-form conversations. MIRIX achieves superior performance on challenging benchmarks, outperforming ex...

🔹 Publication Date: Published on Jul 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.07957
• PDF: https://arxiv.org/pdf/2507.07957
• Project Page: https://mirix.io/
• Github: https://github.com/Mirix-AI/MIRIX

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

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#LLM #MultiAgentSystems #AISystems #MemorySystems #AI

✨Cache-to-Cache: Direct Semantic Communication Between Large Language Models

📝 Summary:
C2C enables direct semantic communication between LLMs by projecting and fusing their KV-caches, overcoming text-based communication limits. This method preserves rich semantics, improving accuracy by 3-5% and achieving a 2x speedup over traditional text communication.

🔹 Publication Date: Published on Oct 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.03215
• PDF: https://arxiv.org/pdf/2510.03215
• Project Page: https://fuvty.github.io/C2C_Project_Page/
• Github: https://github.com/thu-nics/C2C

🔹 Models citing this paper:
• https://huggingface.co/nics-efc/C2C_Fuser

✨ Spaces citing this paper:
• https://huggingface.co/spaces/fuvty/C2C_demo
• https://huggingface.co/spaces/nics-efc/C2C_demo

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

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#LLM #SemanticCommunication #AI #DeepLearning #NLP

✨Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery

📝 Summary:
Skyfall-GS synthesizes large-scale, explorable 3D urban scenes by combining satellite imagery for geometry and diffusion models for realistic textures. This framework offers improved cross-view consistent geometry and photorealistic appearances without needing costly 3D annotations.

🔹 Publication Date: Published on Oct 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.15869
• PDF: https://arxiv.org/pdf/2510.15869
• Project Page: https://skyfall-gs.jayinnn.dev/
• Github: https://github.com/jayin92/skyfall-gs

🔹 Models citing this paper:
• https://huggingface.co/jayinnn/Skyfall-GS-ply

✨ Datasets citing this paper:
• https://huggingface.co/datasets/jayinnn/Skyfall-GS-eval
• https://huggingface.co/datasets/jayinnn/Skyfall-GS-datasets

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

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#3DReconstruction #ComputerVision #SatelliteImagery #DiffusionModels #UrbanModeling

✨Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents

📝 Summary:
Easy Dataset is a framework that synthesizes LLM fine-tuning data from unstructured documents using a GUI and LLMs. It generates domain-specific question-answer pairs with human oversight. This improves LLM performance in specific domains while retaining general knowledge.

🔹 Publication Date: Published on Jul 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.04009
• PDF: https://arxiv.org/pdf/2507.04009
• Github: https://github.com/ConardLi/easy-dataset

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

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#LLM #DataSynthesis #FineTuning #AI #NLP

✨InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

📝 Summary:
InternVL3 introduces a native multimodal pre-training paradigm, jointly learning from multimodal and text data to overcome conventional alignment challenges. This unified approach, combined with advanced techniques, achieves state-of-the-art performance on multimodal tasks, rivaling proprietary m...

🔹 Publication Date: Published on Apr 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.10479
• PDF: https://arxiv.org/pdf/2504.10479
• Project Page: https://internvl.github.io/blog/2025-04-11-InternVL-3.0/

🔹 Models citing this paper:
• https://huggingface.co/OpenGVLab/InternVL3-78B
• https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B
• https://huggingface.co/OpenGVLab/InternVL3-8B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/OpenGVLab/MMPR-v1.2-prompts

✨ Spaces citing this paper:
• https://huggingface.co/spaces/AntResearchNLP/ViLaBench
• https://huggingface.co/spaces/TIGER-Lab/MEGA-Bench
• https://huggingface.co/spaces/prithivMLmods/Tiny-VLMs-Lab

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

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#MultimodalAI #DeepLearning #AIResearch #OpenSourceAI #GenerativeAI

✨A decoder-only foundation model for time-series forecasting

📝 Summary:
This paper introduces a decoder-only foundation model, adapted from large language models, for time-series forecasting. It achieves near-optimal zero-shot performance on diverse datasets across various time scales and granularities.

🔹 Publication Date: Published on Oct 14, 2023

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2310.10688
• PDF: https://arxiv.org/pdf/2310.10688
• Github: https://github.com/google-research/timesfm

🔹 Models citing this paper:
• https://huggingface.co/google/timesfm-1.0-200m
• https://huggingface.co/google/timesfm-2.0-500m-pytorch
• https://huggingface.co/google/timesfm-2.5-200m-pytorch

✨ Spaces citing this paper:
• https://huggingface.co/spaces/autogluon/fev-leaderboard
• https://huggingface.co/spaces/JayLacoma/Trader_Technical_Indicators
• https://huggingface.co/spaces/pavel321/huggingface-cli-completion

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

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#TimeSeriesForecasting #FoundationModels #MachineLearning #DeepLearning #AI

✨RLinf-VLA: A Unified and Efficient Framework for VLA+RL Training

📝 Summary:
RLinf-VLA is a unified framework for scalable reinforcement learning training of vision-language-action models, overcoming supervised fine-tuning limitations. It offers a 1.6x-1.8x speedup, supports diverse architectures and algorithms, and shows strong generalization in simulation and on a real ...

🔹 Publication Date: Published on Oct 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.06710
• PDF: https://arxiv.org/pdf/2510.06710
• Project Page: https://rlinf.readthedocs.io/en/latest/
• Github: https://github.com/RLinf/RLinf

🔹 Models citing this paper:
• https://huggingface.co/RLinf/RLinf-math-7B

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

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#ReinforcementLearning #VLA #Robotics #AIResearch #MachineLearning

✨ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation

📝 Summary:
ChronoEdit ensures physical consistency in image editing by reframing it as a video generation problem. It uses pretrained video models and temporal reasoning tokens to imagine plausible physical transformations between edited images. This approach significantly improves realism and visual fideli...

🔹 Publication Date: Published on Oct 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.04290
• PDF: https://arxiv.org/pdf/2510.04290
• Project Page: https://research.nvidia.com/labs/toronto-ai/chronoedit
• Github: https://github.com/nv-tlabs/ChronoEdit

🔹 Models citing this paper:
• https://huggingface.co/nvidia/ChronoEdit-14B-Diffusers
• https://huggingface.co/vantagewithai/ChronoEdit-GGUF
• https://huggingface.co/vantagewithai/ChronoEdit-fp8-scaled

✨ Spaces citing this paper:
• https://huggingface.co/spaces/nvidia/ChronoEdit
• https://huggingface.co/spaces/JarlJarle/nvidia-ChronoEdit-14B-Diffusers

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

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#ImageEditing #VideoGeneration #TemporalReasoning #ComputerVision #AIResearch

✨Less is More: Recursive Reasoning with Tiny Networks

📝 Summary:
Tiny Recursive Model TRM uses a simple, two-layer network for recursive reasoning. It significantly outperforms larger language models on complex puzzle tasks like ARC-AGI, achieving high generalization with vastly fewer parameters. TRM demonstrates superior performance with minimal resources.

🔹 Publication Date: Published on Oct 6

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/less-is-more-recursive-reasoning-with-tiny-networks
• PDF: https://arxiv.org/pdf/2510.04871
• Project Page: https://alexiajm.github.io/2025/09/29/tiny_recursive_models.html
• Github: https://github.com/SamsungSAILMontreal/TinyRecursiveModels/issues/2

🔹 Models citing this paper:
• https://huggingface.co/wtfmahe/Samsung-TRM
• https://huggingface.co/ordlibrary/X402

✨ Datasets citing this paper:
• https://huggingface.co/datasets/emiliocantuc/sudoku-extreme-1k-aug-1000

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

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#RecursiveReasoning #TinyAI #EfficientAI #AIResearch #MachineLearning

✨Brain-IT: Image Reconstruction from fMRI via Brain-Interaction Transformer

📝 Summary:
Brain-IT reconstructs high-fidelity images from fMRI using a Brain Interaction Transformer. It surpasses current methods visually and objectively, and requires significantly less training data for new subjects.

🔹 Publication Date: Published on Oct 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.25976
• PDF: https://arxiv.org/pdf/2510.25976
• Project Page: https://amitzalcher.github.io/Brain-IT/
• Github: https://amitzalcher.github.io/Brain-IT/

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Amitz244/Brain-IT_Results

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

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#fMRI #ImageReconstruction #DeepLearning #Neuroscience #BrainIT