René's URL Explorer Experiment


Title: Trending Papers - Hugging Face

Open Graph Title: Trending Papers - Hugging Face

Description: Your daily dose of AI research from AK

Open Graph Description: Your daily dose of AI research from AK

Opengraph URL: https://huggingface.co/papers/trending

X: @huggingface

direct link

Domain: paperswithcode.com

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Hugging Facehttps://paperswithcode.com/
Modelshttps://paperswithcode.com/models
Datasetshttps://paperswithcode.com/datasets
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Docshttps://paperswithcode.com/docs
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Pricing https://paperswithcode.com/pricing
Log In https://paperswithcode.com/login
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AKhttps://paperswithcode.com/akhaliq
https://paperswithcode.com/papers
https://paperswithcode.com/papers/2601.20540
Advancing Open-source World Modelshttps://paperswithcode.com/papers/2601.20540
Robbyanthttps://paperswithcode.com/robbyant
Upvote 103 https://paperswithcode.com/login?next=%2Fpapers%2F2601.20540
GitHub 1.86khttps://github.com/Robbyant/lingbot-world/
arXiv Pagehttps://arxiv.org/abs/2601.20540
https://paperswithcode.com/papers/2601.20540
Advancing Open-source World Modelshttps://paperswithcode.com/papers/2601.20540
Robbyanthttps://paperswithcode.com/robbyant
Upvote 103 https://paperswithcode.com/login?next=%2Fpapers%2F2601.20540
GitHub 1.86khttps://github.com/Robbyant/lingbot-world/
arXiv Pagehttps://arxiv.org/abs/2601.20540
https://paperswithcode.com/papers/2508.03680
Agent Lightning: Train ANY AI Agents with Reinforcement Learninghttps://paperswithcode.com/papers/2508.03680
Upvote 129 https://paperswithcode.com/login?next=%2Fpapers%2F2508.03680
GitHub 13.2khttps://github.com/microsoft/agent-lightning
arXiv Pagehttps://arxiv.org/abs/2508.03680
https://paperswithcode.com/papers/2508.03680
Agent Lightning: Train ANY AI Agents with Reinforcement Learninghttps://paperswithcode.com/papers/2508.03680
Upvote 129 https://paperswithcode.com/login?next=%2Fpapers%2F2508.03680
GitHub 13.2khttps://github.com/microsoft/agent-lightning
arXiv Pagehttps://arxiv.org/abs/2508.03680
https://paperswithcode.com/papers/2502.11880
Bitnet.cpp: Efficient Edge Inference for Ternary LLMshttps://paperswithcode.com/papers/2502.11880
Upvote 5 https://paperswithcode.com/login?next=%2Fpapers%2F2502.11880
GitHub 27.6khttps://github.com/microsoft/BitNet/tree/paper
arXiv Pagehttps://arxiv.org/abs/2502.11880
https://paperswithcode.com/papers/2502.11880
Bitnet.cpp: Efficient Edge Inference for Ternary LLMshttps://paperswithcode.com/papers/2502.11880
Upvote 5 https://paperswithcode.com/login?next=%2Fpapers%2F2502.11880
GitHub 27.6khttps://github.com/microsoft/BitNet/tree/paper
arXiv Pagehttps://arxiv.org/abs/2502.11880
https://paperswithcode.com/papers/2504.12285
BitNet b1.58 2B4T Technical Reporthttps://paperswithcode.com/papers/2504.12285
Upvote 82 https://paperswithcode.com/login?next=%2Fpapers%2F2504.12285
GitHub 27.6khttps://github.com/microsoft/bitnet
arXiv Pagehttps://arxiv.org/abs/2504.12285
https://paperswithcode.com/papers/2504.12285
BitNet b1.58 2B4T Technical Reporthttps://paperswithcode.com/papers/2504.12285
Upvote 82 https://paperswithcode.com/login?next=%2Fpapers%2F2504.12285
GitHub 27.6khttps://github.com/microsoft/bitnet
arXiv Pagehttps://arxiv.org/abs/2504.12285
https://paperswithcode.com/papers/2510.13998
BitNet Distillationhttps://paperswithcode.com/papers/2510.13998
Microsoft Researchhttps://paperswithcode.com/MicrosoftResearch
Upvote 59 https://paperswithcode.com/login?next=%2Fpapers%2F2510.13998
GitHub 27.6khttps://github.com/microsoft/BitNet
arXiv Pagehttps://arxiv.org/abs/2510.13998
https://paperswithcode.com/papers/2510.13998
BitNet Distillationhttps://paperswithcode.com/papers/2510.13998
Microsoft Researchhttps://paperswithcode.com/MicrosoftResearch
Upvote 59 https://paperswithcode.com/login?next=%2Fpapers%2F2510.13998
GitHub 27.6khttps://github.com/microsoft/BitNet
arXiv Pagehttps://arxiv.org/abs/2510.13998
https://paperswithcode.com/papers/2601.15621
Qwen3-TTS Technical Reporthttps://paperswithcode.com/papers/2601.15621
Qwenhttps://paperswithcode.com/Qwen
Upvote 56 https://paperswithcode.com/login?next=%2Fpapers%2F2601.15621
GitHub 6.55khttps://github.com/QwenLM/Qwen3-TTS
arXiv Pagehttps://arxiv.org/abs/2601.15621
https://paperswithcode.com/papers/2601.15621
Qwen3-TTS Technical Reporthttps://paperswithcode.com/papers/2601.15621
Qwenhttps://paperswithcode.com/Qwen
Upvote 56 https://paperswithcode.com/login?next=%2Fpapers%2F2601.15621
GitHub 6.55khttps://github.com/QwenLM/Qwen3-TTS
arXiv Pagehttps://arxiv.org/abs/2601.15621
https://paperswithcode.com/papers/2510.14528
PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Modelhttps://paperswithcode.com/papers/2510.14528
PaddlePaddlehttps://paperswithcode.com/PaddlePaddle
Upvote 112 https://paperswithcode.com/login?next=%2Fpapers%2F2510.14528
GitHub 69.9khttps://github.com/PaddlePaddle/PaddleOCR
arXiv Pagehttps://arxiv.org/abs/2510.14528
https://paperswithcode.com/papers/2510.14528
PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Modelhttps://paperswithcode.com/papers/2510.14528
PaddlePaddlehttps://paperswithcode.com/PaddlePaddle
Upvote 112 https://paperswithcode.com/login?next=%2Fpapers%2F2510.14528
GitHub 69.9khttps://github.com/PaddlePaddle/PaddleOCR
arXiv Pagehttps://arxiv.org/abs/2510.14528
https://paperswithcode.com/papers/2601.20552
DeepSeek-OCR 2: Visual Causal Flowhttps://paperswithcode.com/papers/2601.20552
DeepSeekhttps://paperswithcode.com/deepseek-ai
Upvote 48 https://paperswithcode.com/login?next=%2Fpapers%2F2601.20552
GitHub 1.85khttps://github.com/deepseek-ai/DeepSeek-OCR-2
arXiv Pagehttps://arxiv.org/abs/2601.20552
https://paperswithcode.com/papers/2601.20552
DeepSeek-OCR 2: Visual Causal Flowhttps://paperswithcode.com/papers/2601.20552
DeepSeekhttps://paperswithcode.com/deepseek-ai
Upvote 48 https://paperswithcode.com/login?next=%2Fpapers%2F2601.20552
GitHub 1.85khttps://github.com/deepseek-ai/DeepSeek-OCR-2
arXiv Pagehttps://arxiv.org/abs/2601.20552
https://paperswithcode.com/papers/2510.09212
Stable Video Infinity: Infinite-Length Video Generation with Error Recyclinghttps://paperswithcode.com/papers/2510.09212
EPFL VITA Labhttps://paperswithcode.com/epfl-vita
Upvote 18 https://paperswithcode.com/login?next=%2Fpapers%2F2510.09212
GitHub 1.89khttps://github.com/vita-epfl/Stable-Video-Infinity
arXiv Pagehttps://arxiv.org/abs/2510.09212
https://paperswithcode.com/papers/2510.09212
Stable Video Infinity: Infinite-Length Video Generation with Error Recyclinghttps://paperswithcode.com/papers/2510.09212
EPFL VITA Labhttps://paperswithcode.com/epfl-vita
Upvote 18 https://paperswithcode.com/login?next=%2Fpapers%2F2510.09212
GitHub 1.89khttps://github.com/vita-epfl/Stable-Video-Infinity
arXiv Pagehttps://arxiv.org/abs/2510.09212
https://paperswithcode.com/papers/2509.22186
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsinghttps://paperswithcode.com/papers/2509.22186
Upvote 142 https://paperswithcode.com/login?next=%2Fpapers%2F2509.22186
GitHub 53.6khttps://github.com/opendatalab/MinerU
arXiv Pagehttps://arxiv.org/abs/2509.22186
https://paperswithcode.com/papers/2509.22186
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsinghttps://paperswithcode.com/papers/2509.22186
Upvote 142 https://paperswithcode.com/login?next=%2Fpapers%2F2509.22186
GitHub 53.6khttps://github.com/opendatalab/MinerU
arXiv Pagehttps://arxiv.org/abs/2509.22186
https://paperswithcode.com/papers/2511.12884
Agent READMEs: An Empirical Study of Context Files for Agentic Codinghttps://paperswithcode.com/papers/2511.12884
Upvote 21 https://paperswithcode.com/login?next=%2Fpapers%2F2511.12884
GitHub 16.6khttps://github.com/openai/agents.md
arXiv Pagehttps://arxiv.org/abs/2511.12884
https://paperswithcode.com/papers/2511.12884
Agent READMEs: An Empirical Study of Context Files for Agentic Codinghttps://paperswithcode.com/papers/2511.12884
Upvote 21 https://paperswithcode.com/login?next=%2Fpapers%2F2511.12884
GitHub 16.6khttps://github.com/openai/agents.md
arXiv Pagehttps://arxiv.org/abs/2511.12884
https://paperswithcode.com/papers/2409.18839
MinerU: An Open-Source Solution for Precise Document Content Extractionhttps://paperswithcode.com/papers/2409.18839
Upvote 38 https://paperswithcode.com/login?next=%2Fpapers%2F2409.18839
GitHub 53.6khttps://github.com/opendatalab/mineru
arXiv Pagehttps://arxiv.org/abs/2409.18839
https://paperswithcode.com/papers/2409.18839
MinerU: An Open-Source Solution for Precise Document Content Extractionhttps://paperswithcode.com/papers/2409.18839
Upvote 38 https://paperswithcode.com/login?next=%2Fpapers%2F2409.18839
GitHub 53.6khttps://github.com/opendatalab/mineru
arXiv Pagehttps://arxiv.org/abs/2409.18839
https://paperswithcode.com/papers/2601.18692
A Pragmatic VLA Foundation Modelhttps://paperswithcode.com/papers/2601.18692
Robbyanthttps://paperswithcode.com/robbyant
Upvote 45 https://paperswithcode.com/login?next=%2Fpapers%2F2601.18692
GitHub 594https://github.com/robbyant/lingbot-vla
arXiv Pagehttps://arxiv.org/abs/2601.18692
https://paperswithcode.com/papers/2601.18692
A Pragmatic VLA Foundation Modelhttps://paperswithcode.com/papers/2601.18692
Robbyanthttps://paperswithcode.com/robbyant
Upvote 45 https://paperswithcode.com/login?next=%2Fpapers%2F2601.18692
GitHub 594https://github.com/robbyant/lingbot-vla
arXiv Pagehttps://arxiv.org/abs/2601.18692
https://paperswithcode.com/papers/2503.11576
SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversionhttps://paperswithcode.com/papers/2503.11576
IBM Granitehttps://paperswithcode.com/ibm-granite
Upvote 138 https://paperswithcode.com/login?next=%2Fpapers%2F2503.11576
GitHub 51.9khttps://github.com/docling-project/docling
arXiv Pagehttps://arxiv.org/abs/2503.11576
https://paperswithcode.com/papers/2503.11576
SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversionhttps://paperswithcode.com/papers/2503.11576
IBM Granitehttps://paperswithcode.com/ibm-granite
Upvote 138 https://paperswithcode.com/login?next=%2Fpapers%2F2503.11576
GitHub 51.9khttps://github.com/docling-project/docling
arXiv Pagehttps://arxiv.org/abs/2503.11576
https://paperswithcode.com/papers/2601.20833
Idea2Story: An Automated Pipeline for Transforming Research Concepts into Complete Scientific Narrativeshttps://paperswithcode.com/papers/2601.20833
AgentAlphahttps://paperswithcode.com/AgentAlphaAGI
Upvote 162 https://paperswithcode.com/login?next=%2Fpapers%2F2601.20833
GitHub 394https://github.com/AgentAlphaAGI/Idea2Paper
arXiv Pagehttps://arxiv.org/abs/2601.20833
https://paperswithcode.com/papers/2601.20833
Idea2Story: An Automated Pipeline for Transforming Research Concepts into Complete Scientific Narrativeshttps://paperswithcode.com/papers/2601.20833
AgentAlphahttps://paperswithcode.com/AgentAlphaAGI
Upvote 162 https://paperswithcode.com/login?next=%2Fpapers%2F2601.20833
GitHub 394https://github.com/AgentAlphaAGI/Idea2Paper
arXiv Pagehttps://arxiv.org/abs/2601.20833
https://paperswithcode.com/papers/2601.10547
HeartMuLa: A Family of Open Sourced Music Foundation Modelshttps://paperswithcode.com/papers/2601.10547
Upvote 41 https://paperswithcode.com/login?next=%2Fpapers%2F2601.10547
GitHub 2.75khttps://github.com/HeartMuLa/heartlib
arXiv Pagehttps://arxiv.org/abs/2601.10547
https://paperswithcode.com/papers/2601.10547
HeartMuLa: A Family of Open Sourced Music Foundation Modelshttps://paperswithcode.com/papers/2601.10547
Upvote 41 https://paperswithcode.com/login?next=%2Fpapers%2F2601.10547
GitHub 2.75khttps://github.com/HeartMuLa/heartlib
arXiv Pagehttps://arxiv.org/abs/2601.10547
https://paperswithcode.com/papers/2504.08761
UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generationhttps://paperswithcode.com/papers/2504.08761
Upvote 7 https://paperswithcode.com/login?next=%2Fpapers%2F2504.08761
GitHub 5.02khttps://github.com/OpenBMB/UltraRAG
arXiv Pagehttps://arxiv.org/abs/2504.08761
https://paperswithcode.com/papers/2504.08761
UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generationhttps://paperswithcode.com/papers/2504.08761
Upvote 7 https://paperswithcode.com/login?next=%2Fpapers%2F2504.08761
GitHub 5.02khttps://github.com/OpenBMB/UltraRAG
arXiv Pagehttps://arxiv.org/abs/2504.08761
https://paperswithcode.com/papers/2601.17895
Masked Depth Modeling for Spatial Perceptionhttps://paperswithcode.com/papers/2601.17895
Robbyanthttps://paperswithcode.com/robbyant
Upvote 22 https://paperswithcode.com/login?next=%2Fpapers%2F2601.17895
GitHub 696https://github.com/Robbyant/lingbot-depth
arXiv Pagehttps://arxiv.org/abs/2601.17895
https://paperswithcode.com/papers/2601.17895
Masked Depth Modeling for Spatial Perceptionhttps://paperswithcode.com/papers/2601.17895
Robbyanthttps://paperswithcode.com/robbyant
Upvote 22 https://paperswithcode.com/login?next=%2Fpapers%2F2601.17895
GitHub 696https://github.com/Robbyant/lingbot-depth
arXiv Pagehttps://arxiv.org/abs/2601.17895
https://paperswithcode.com/papers/2309.06180
Efficient Memory Management for Large Language Model Serving with PagedAttentionhttps://paperswithcode.com/papers/2309.06180
Upvote 34 https://paperswithcode.com/login?next=%2Fpapers%2F2309.06180
GitHub 69.2khttps://github.com/vllm-project/vllm
arXiv Pagehttps://arxiv.org/abs/2309.06180
https://paperswithcode.com/papers/2309.06180
Efficient Memory Management for Large Language Model Serving with PagedAttentionhttps://paperswithcode.com/papers/2309.06180
Upvote 34 https://paperswithcode.com/login?next=%2Fpapers%2F2309.06180
GitHub 69.2khttps://github.com/vllm-project/vllm
arXiv Pagehttps://arxiv.org/abs/2309.06180
https://paperswithcode.com/papers/2508.19205
VibeVoice Technical Reporthttps://paperswithcode.com/papers/2508.19205
Microsoft Researchhttps://paperswithcode.com/MicrosoftResearch
Upvote 143 https://paperswithcode.com/login?next=%2Fpapers%2F2508.19205
GitHub 22.8khttps://github.com/microsoft/VibeVoice
arXiv Pagehttps://arxiv.org/abs/2508.19205
https://paperswithcode.com/papers/2508.19205
VibeVoice Technical Reporthttps://paperswithcode.com/papers/2508.19205
Microsoft Researchhttps://paperswithcode.com/MicrosoftResearch
Upvote 143 https://paperswithcode.com/login?next=%2Fpapers%2F2508.19205
GitHub 22.8khttps://github.com/microsoft/VibeVoice
arXiv Pagehttps://arxiv.org/abs/2508.19205
https://paperswithcode.com/papers/2601.21558
ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenashttps://paperswithcode.com/papers/2601.21558
Upvote 40 https://paperswithcode.com/login?next=%2Fpapers%2F2601.21558
GitHub 80https://github.com/LianjiaTech/astra
arXiv Pagehttps://arxiv.org/abs/2601.21558
https://paperswithcode.com/papers/2601.21558
ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenashttps://paperswithcode.com/papers/2601.21558
Upvote 40 https://paperswithcode.com/login?next=%2Fpapers%2F2601.21558
GitHub 80https://github.com/LianjiaTech/astra
arXiv Pagehttps://arxiv.org/abs/2601.21558
https://paperswithcode.com/papers/2412.20138
TradingAgents: Multi-Agents LLM Financial Trading Frameworkhttps://paperswithcode.com/papers/2412.20138
Upvote 15 https://paperswithcode.com/login?next=%2Fpapers%2F2412.20138
GitHub 28.9khttps://github.com/tauricresearch/tradingagents
arXiv Pagehttps://arxiv.org/abs/2412.20138
https://paperswithcode.com/papers/2412.20138
TradingAgents: Multi-Agents LLM Financial Trading Frameworkhttps://paperswithcode.com/papers/2412.20138
Upvote 15 https://paperswithcode.com/login?next=%2Fpapers%2F2412.20138
GitHub 28.9khttps://github.com/tauricresearch/tradingagents
arXiv Pagehttps://arxiv.org/abs/2412.20138
https://paperswithcode.com/papers/2601.20614
Harder Is Better: Boosting Mathematical Reasoning via Difficulty-Aware GRPO and Multi-Aspect Question Reformulationhttps://paperswithcode.com/papers/2601.20614
AMAP-MLhttps://paperswithcode.com/GD-ML
Upvote 115 https://paperswithcode.com/login?next=%2Fpapers%2F2601.20614
GitHub 105https://github.com/AMAP-ML/MathForge
arXiv Pagehttps://arxiv.org/abs/2601.20614
https://paperswithcode.com/papers/2601.20614
Harder Is Better: Boosting Mathematical Reasoning via Difficulty-Aware GRPO and Multi-Aspect Question Reformulationhttps://paperswithcode.com/papers/2601.20614
AMAP-MLhttps://paperswithcode.com/GD-ML
Upvote 115 https://paperswithcode.com/login?next=%2Fpapers%2F2601.20614
GitHub 105https://github.com/AMAP-ML/MathForge
arXiv Pagehttps://arxiv.org/abs/2601.20614
https://paperswithcode.com/papers/2504.19413
Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memoryhttps://paperswithcode.com/papers/2504.19413
Upvote 40 https://paperswithcode.com/login?next=%2Fpapers%2F2504.19413
GitHub 46.4khttps://github.com/mem0ai/mem0
arXiv Pagehttps://arxiv.org/abs/2504.19413
https://paperswithcode.com/papers/2504.19413
Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memoryhttps://paperswithcode.com/papers/2504.19413
Upvote 40 https://paperswithcode.com/login?next=%2Fpapers%2F2504.19413
GitHub 46.4khttps://github.com/mem0ai/mem0
arXiv Pagehttps://arxiv.org/abs/2504.19413
https://paperswithcode.com/papers/2412.00568
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learninghttps://paperswithcode.com/papers/2412.00568
Upvote 24 https://paperswithcode.com/login?next=%2Fpapers%2F2412.00568
GitHub 1.85khttps://github.com/PolymathicAI/the_well
arXiv Pagehttps://arxiv.org/abs/2412.00568
https://paperswithcode.com/papers/2412.00568
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learninghttps://paperswithcode.com/papers/2412.00568
Upvote 24 https://paperswithcode.com/login?next=%2Fpapers%2F2412.00568
GitHub 1.85khttps://github.com/PolymathicAI/the_well
arXiv Pagehttps://arxiv.org/abs/2412.00568
https://paperswithcode.com/papers/2406.07155
Scaling Large-Language-Model-based Multi-Agent Collaborationhttps://paperswithcode.com/papers/2406.07155
Upvote 3 https://paperswithcode.com/login?next=%2Fpapers%2F2406.07155
GitHub 29.2khttps://github.com/OpenBMB/ChatDev/tree/macnet
arXiv Pagehttps://arxiv.org/abs/2406.07155
https://paperswithcode.com/papers/2406.07155
Scaling Large-Language-Model-based Multi-Agent Collaborationhttps://paperswithcode.com/papers/2406.07155
Upvote 3 https://paperswithcode.com/login?next=%2Fpapers%2F2406.07155
GitHub 29.2khttps://github.com/OpenBMB/ChatDev/tree/macnet
arXiv Pagehttps://arxiv.org/abs/2406.07155
https://paperswithcode.com/papers/2502.06855
Self-Supervised Prompt Optimizationhttps://paperswithcode.com/papers/2502.06855
Upvote 15 https://paperswithcode.com/login?next=%2Fpapers%2F2502.06855
GitHub 63.8khttps://github.com/geekan/metagpt
arXiv Pagehttps://arxiv.org/abs/2502.06855
https://paperswithcode.com/papers/2502.06855
Self-Supervised Prompt Optimizationhttps://paperswithcode.com/papers/2502.06855
Upvote 15 https://paperswithcode.com/login?next=%2Fpapers%2F2502.06855
GitHub 63.8khttps://github.com/geekan/metagpt
arXiv Pagehttps://arxiv.org/abs/2502.06855
https://paperswithcode.com/papers/2511.22699
Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformerhttps://paperswithcode.com/papers/2511.22699
Tongyi-MAIhttps://paperswithcode.com/Tongyi-MAI
Upvote 235 https://paperswithcode.com/login?next=%2Fpapers%2F2511.22699
GitHub 9.76khttps://github.com/Tongyi-MAI/Z-Image
arXiv Pagehttps://arxiv.org/abs/2511.22699
https://paperswithcode.com/papers/2511.22699
Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformerhttps://paperswithcode.com/papers/2511.22699
Tongyi-MAIhttps://paperswithcode.com/Tongyi-MAI
Upvote 235 https://paperswithcode.com/login?next=%2Fpapers%2F2511.22699
GitHub 9.76khttps://github.com/Tongyi-MAI/Z-Image
arXiv Pagehttps://arxiv.org/abs/2511.22699
https://paperswithcode.com/papers/2511.22677
Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shieldhttps://paperswithcode.com/papers/2511.22677
Tongyi-MAIhttps://paperswithcode.com/Tongyi-MAI
Upvote 31 https://paperswithcode.com/login?next=%2Fpapers%2F2511.22677
GitHub 9.76khttps://github.com/Tongyi-MAI/Z-Image/tree/main
arXiv Pagehttps://arxiv.org/abs/2511.22677
https://paperswithcode.com/papers/2511.22677
Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shieldhttps://paperswithcode.com/papers/2511.22677
Tongyi-MAIhttps://paperswithcode.com/Tongyi-MAI
Upvote 31 https://paperswithcode.com/login?next=%2Fpapers%2F2511.22677
GitHub 9.76khttps://github.com/Tongyi-MAI/Z-Image/tree/main
arXiv Pagehttps://arxiv.org/abs/2511.22677
https://paperswithcode.com/papers/2403.13372
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelshttps://paperswithcode.com/papers/2403.13372
Upvote 176 https://paperswithcode.com/login?next=%2Fpapers%2F2403.13372
GitHub 66.8khttps://github.com/hiyouga/LLaMA-Factory
arXiv Pagehttps://arxiv.org/abs/2403.13372
https://paperswithcode.com/papers/2403.13372
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelshttps://paperswithcode.com/papers/2403.13372
Upvote 176 https://paperswithcode.com/login?next=%2Fpapers%2F2403.13372
GitHub 66.8khttps://github.com/hiyouga/LLaMA-Factory
arXiv Pagehttps://arxiv.org/abs/2403.13372
https://paperswithcode.com/papers/2505.19591
Multi-Agent Collaboration via Evolving Orchestrationhttps://paperswithcode.com/papers/2505.19591
Upvote 3 https://paperswithcode.com/login?next=%2Fpapers%2F2505.19591
GitHub 29.2khttps://github.com/OpenBMB/ChatDev/tree/puppeteer
arXiv Pagehttps://arxiv.org/abs/2505.19591
https://paperswithcode.com/papers/2505.19591
Multi-Agent Collaboration via Evolving Orchestrationhttps://paperswithcode.com/papers/2505.19591
Upvote 3 https://paperswithcode.com/login?next=%2Fpapers%2F2505.19591
GitHub 29.2khttps://github.com/OpenBMB/ChatDev/tree/puppeteer
arXiv Pagehttps://arxiv.org/abs/2505.19591
https://paperswithcode.com/papers/2601.02553
SimpleMem: Efficient Lifelong Memory for LLM Agentshttps://paperswithcode.com/papers/2601.02553
Upvote 36 https://paperswithcode.com/login?next=%2Fpapers%2F2601.02553
GitHub 2.63khttps://github.com/aiming-lab/SimpleMem
arXiv Pagehttps://arxiv.org/abs/2601.02553
https://paperswithcode.com/papers/2601.02553
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