✨Kimi Linear: An Expressive, Efficient Attention Architecture

📝 Summary:
Kimi Linear is a new hybrid linear attention architecture that outperforms full attention in performance and efficiency across diverse scenarios. It leverages Kimi Delta Attention and Multi-Head Latent Attention, reducing KV cache by up to 75% and boosting decoding throughput by 6x.

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26692
• PDF: https://arxiv.org/pdf/2510.26692
• Github: https://github.com/MoonshotAI/Kimi-Linear

🔹 Models citing this paper:
• https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct
• https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Base
• https://huggingface.co/aiqtech/Kimi-Linear-48B-A3B-Instruct

✨ Spaces citing this paper:
• https://huggingface.co/spaces/Speedofmastery/orynxml-agents

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

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#AttentionMechanisms #LLM #AIResearch #DeepLearning #ModelEfficiency

✨PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model

📝 Summary:
PaddleOCR-VL is a new 0.9B vision-language model for document parsing. It uses a NaViT-style visual encoder and ERNIE-4.5, achieving state-of-the-art performance across 109 languages with minimal resources and fast inference. This model is highly suitable for practical deployment.

🔹 Publication Date: Published on Oct 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.14528
• PDF: https://arxiv.org/pdf/2510.14528
• Github: https://github.com/PaddlePaddle/PaddleOCR

🔹 Models citing this paper:
• https://huggingface.co/PaddlePaddle/PaddleOCR-VL
• https://huggingface.co/PaddlePaddle/PP-DocLayoutV2
• https://huggingface.co/lvyufeng/PaddleOCR-VL-0.9B

✨ Spaces citing this paper:
• https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL_Online_Demo
• https://huggingface.co/spaces/markobinario/PaddleOCR-VL_Online_Demo
• https://huggingface.co/spaces/waytoAGI/PaddleOCR-VL_Online_Demo

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

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#OCR #VisionLanguageModel #DocumentAI #DeepLearning #AI

✨Emu3.5: Native Multimodal Models are World Learners

📝 Summary:
Emu3.5 is a large-scale multimodal world model predicting next states in vision and language. It uses reinforcement learning and Discrete Diffusion Adaptation for efficient inference, delivering strong performance in multimodal tasks and world exploration.

🔹 Publication Date: Published on Oct 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.26583
• PDF: https://arxiv.org/pdf/2510.26583
• Project Page: https://emu.world/
• Github: https://github.com/baaivision/Emu3.5

🔹 Models citing this paper:
• https://huggingface.co/BAAI/Emu3.5
• https://huggingface.co/BAAI/Emu3.5-Image
• https://huggingface.co/BAAI/Emu3.5-VisionTokenizer

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

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#MultimodalAI #WorldModels #ReinforcementLearning #ComputerVision #NLP

✨DeepAnalyze: Agentic Large Language Models for Autonomous Data Science

📝 Summary:
DeepAnalyze-8B is an agentic LLM that autonomously completes the entire data science pipeline, from raw data to research reports. It employs curriculum-based training and data-grounded trajectory synthesis, outperforming larger, workflow-based agents. This open-source model advances autonomous da...

🔹 Publication Date: Published on Oct 19

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/deepanalyze-agentic-large-language-models-for-autonomous-data-science
• PDF: https://arxiv.org/pdf/2510.16872
• Project Page: https://ruc-deepanalyze.github.io/
• Github: https://github.com/ruc-datalab/DeepAnalyze

🔹 Models citing this paper:
• https://huggingface.co/RUC-DataLab/DeepAnalyze-8B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/RUC-DataLab/DataScience-Instruct-500K
• https://huggingface.co/datasets/fantos/DataScience-Instruct-500K

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

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#LLM #DataScience #AgenticAI #AutonomousAI #AI

✨LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

📝 Summary:
LlamaFactory is a unified framework for efficient, no-code fine-tuning of over 100 large language models. It provides a web-based user interface, LlamaBoard, to simplify customization for various tasks.

🔹 Publication Date: Published on Mar 20, 2024

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/llamafactory-unified-efficient-fine-tuning-of-100-language-models
• PDF: https://arxiv.org/pdf/2403.13372
• Project Page: https://huggingface.co/spaces/hiyouga/LLaMA-Board
• Github: https://github.com/hiyouga/LLaMA-Factory

🔹 Models citing this paper:
• https://huggingface.co/AELLM/Llama-3.2-Chibi-3B
• https://huggingface.co/GXMZU/Qwen3-14B-ai-expert-250925
• https://huggingface.co/XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k

✨ Spaces citing this paper:
• https://huggingface.co/spaces/Justinrune/LLaMA-Factory
• https://huggingface.co/spaces/featherless-ai/try-this-model
• https://huggingface.co/spaces/Darok/Featherless-Feud

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

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#LlamaFactory #LLM #FineTuning #AI #MachineLearning

✨TradingAgents: Multi-Agents LLM Financial Trading Framework

📝 Summary:
TradingAgents is a multi-agent LLM framework that simulates real-world trading firms with specialized, collaborative agents. This approach significantly improves trading performance metrics like cumulative returns and Sharpe ratio compared to baseline models.

🔹 Publication Date: Published on Dec 28, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2412.20138
• PDF: https://arxiv.org/pdf/2412.20138
• Github: https://github.com/tauricresearch/tradingagents

✨ Spaces citing this paper:
• https://huggingface.co/spaces/shanghengdu/LLM-Agent-Optimization-PaperList
• https://huggingface.co/spaces/Ervin2077/qiu

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

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#TradingAgents #MultiAgentLLM #FinancialTrading #AlgorithmicTrading #AI

✨OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation

📝 Summary:
OmniFlatten is a novel end-to-end GPT model enabling real-time natural full-duplex spoken dialogue. It achieves this by post-training a text LLM with a multi-stage process for speech-text generation, without modifying the original architecture.

🔹 Publication Date: Published on Oct 23, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2410.17799
• PDF: https://arxiv.org/pdf/2410.17799
• Github: https://github.com/karpathy/nanogpt

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#GPT #VoiceAI #NLP #LLM #DeepLearning

✨olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models

📝 Summary:
olmOCR is an open-source toolkit that uses a fine-tuned vision language model to convert PDFs into clean, structured text. It enables large-scale, cost-effective extraction of trillions of tokens for training language models.

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.18443
• PDF: https://arxiv.org/pdf/2502.18443
• Github: https://github.com/allenai/olmocr

✨ Datasets citing this paper:
• https://huggingface.co/datasets/davanstrien/test-olmocr2
• https://huggingface.co/datasets/davanstrien/newspapers-olmocr2
• https://huggingface.co/datasets/stckmn/ocr-output-Directive017-1761355297

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

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#OCR #VLMs #LLM #DataExtraction #OpenSource

✨MedRAX: Medical Reasoning Agent for Chest X-ray

📝 Summary:
MedRAX is a new AI agent that integrates CXR analysis tools and multimodal large language models. It answers complex medical queries without extra training, achieving state-of-the-art performance.

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.02673
• PDF: https://arxiv.org/pdf/2502.02673
• Github: https://github.com/bowang-lab/medrax

✨ Spaces citing this paper:
• https://huggingface.co/spaces/asbamit/MedRAX-main

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

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#AI #MedicalAI #LLM #Radiology #DeepLearning

✨Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

📝 Summary:
Mem0 is a memory-centric architecture with graph-based memory that enhances long-term conversational coherence in LLMs by efficiently extracting and consolidating information. It outperforms existing memory systems in accuracy, achieving 26% improvement over OpenAI, and significantly reduces comp...

🔹 Publication Date: Published on Apr 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.19413
• PDF: https://arxiv.org/pdf/2504.19413
• Github: https://github.com/mem0ai/mem0

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

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#AI #LLM #AIAgents #LongTermMemory #GraphMemory

✨IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System

📝 Summary:
IndexTTS enhances XTTS and Tortoise for TTS, improving naturalness and zero-shot voice cloning. It features hybrid character-pinyin modeling for Chinese and optimized vector quantization, resulting in more controllable usage, faster inference, and superior performance compared to other systems.

🔹 Publication Date: Published on Feb 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.05512
• PDF: https://arxiv.org/pdf/2502.05512
• Github: https://github.com/index-tts/index-tts

🔹 Models citing this paper:
• https://huggingface.co/IndexTeam/IndexTTS-2
• https://huggingface.co/IndexTeam/Index-TTS
• https://huggingface.co/Toxzic/indextts-colab

✨ Spaces citing this paper:
• https://huggingface.co/spaces/IndexTeam/IndexTTS
• https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena
• https://huggingface.co/spaces/jairwaal/image

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

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#TextToSpeech #ZeroShotLearning #VoiceCloning #AI #MachineLearning

✨PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

📝 Summary:
PyTorch FSDP is an industry-grade solution for efficient and scalable large model training. It enables significantly larger models with near-linear TFLOPS scalability, making advanced capabilities more accessible.

🔹 Publication Date: Published on Apr 21, 2023

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2304.11277
• PDF: https://arxiv.org/pdf/2304.11277
• Github: https://github.com/pytorch/pytorch/blob/main/torch/distributed/fsdp/fully_sharded_data_parallel.py

🔹 Models citing this paper:
• https://huggingface.co/databricks/dbrx-instruct
• https://huggingface.co/databricks/dbrx-base
• https://huggingface.co/Undi95/dbrx-base

✨ Spaces citing this paper:
• https://huggingface.co/spaces/nanotron/ultrascale-playbook
• https://huggingface.co/spaces/Ki-Seki/ultrascale-playbook-zh-cn
• https://huggingface.co/spaces/Gantrol/ultrascale-playbook-zh-cn

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

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#PyTorch #FSDP #DeepLearning #DistributedTraining #LargeModels

✨MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing

📝 Summary:
MinerU2.5 is a new 1.2B-parameter VLM for document parsing. It uses a coarse-to-fine, two-stage strategy: global layout analysis on downsampled images, then targeted content recognition on native-resolution crops. This achieves state-of-the-art accuracy efficiently for high-resolution documents.

🔹 Publication Date: Published on Sep 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.22186
• PDF: https://arxiv.org/pdf/2509.22186
• Project Page: https://opendatalab.github.io/MinerU/
• Github: https://github.com/opendatalab/MinerU

🔹 Models citing this paper:
• https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B
• https://huggingface.co/freakynit/MinerU2.5-2509-1.2B
• https://huggingface.co/Mungert/MinerU2.5-2509-1.2B-GGUF

✨ Spaces citing this paper:
• https://huggingface.co/spaces/opendatalab/MinerU
• https://huggingface.co/spaces/xiaoye-winters/MinerU-API
• https://huggingface.co/spaces/ApeAITW/MinerU_2.5_Test

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

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#VisionLanguageModel #DocumentAI #DeepLearning #ComputerVision #AIResearch

✨PyTorch Distributed: Experiences on Accelerating Data Parallel Training

📝 Summary:
This paper details PyTorch's distributed data parallel module, which accelerates large-scale model training. It uses techniques like gradient bucketing and computation-communication overlap to achieve near-linear scalability with 256 GPUs.

🔹 Publication Date: Published on Jun 28, 2020

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2006.15704
• PDF: https://arxiv.org/pdf/2006.15704
• Github: https://github.com/pytorch/pytorch/blob/master/torch/nn/parallel/distributed.py

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

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#PyTorch #DistributedTraining #DeepLearning #Scalability #HPC

✨MinerU: An Open-Source Solution for Precise Document Content Extraction

📝 Summary:
MinerU is an open-source tool that provides high-precision document content extraction. It uses fine-tuned models and pre/postprocessing rules to consistently achieve high performance across diverse document types.

🔹 Publication Date: Published on Sep 27, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2409.18839
• PDF: https://huggingface.co/spaces/Echo9k/PDF_reader
• Github: https://github.com/opendatalab/MinerU

✨ Spaces citing this paper:
• https://huggingface.co/spaces/opendatalab/MinerU
• https://huggingface.co/spaces/xiaoye-winters/MinerU-API
• https://huggingface.co/spaces/ApeAITW/MinerU_2.5_Test

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

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#DocumentExtraction #OpenSource #DataScience #NLP #AI