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GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
Paper β’ 2508.06471 β’ Published β’ 192 -
NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model
Paper β’ 2508.14444 β’ Published β’ 38 -
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Paper β’ 2507.06261 β’ Published β’ 64 -
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
Paper β’ 2506.13585 β’ Published β’ 272
Collections
Discover the best community collections!
Collections including paper arxiv:2506.13585
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Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning
Paper β’ 2506.07044 β’ Published β’ 114 -
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning
Paper β’ 2506.09513 β’ Published β’ 100 -
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
Paper β’ 2505.24863 β’ Published β’ 97 -
Seedance 1.0: Exploring the Boundaries of Video Generation Models
Paper β’ 2506.09113 β’ Published β’ 104
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NCTC OSINT AGENT
π12Interact with an OSINT agent to gather intelligence
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MiniMax M1
π¬347Generate code from text prompts
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MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
Paper β’ 2506.13585 β’ Published β’ 272 -
General AI Assistant - MCP Client & Server
π€6Solves GAIA Benchmark & Humanity's Last Exam problems
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MiniMax M1
π¬347Generate code from text prompts
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MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
Paper β’ 2506.13585 β’ Published β’ 272 -
MiniMaxAI/MiniMax-M1-80k
Text Generation β’ 456B β’ Updated β’ 272 β’ β’ 685 -
MiniMaxAI/MiniMax-M1-40k
Text Generation β’ 456B β’ Updated β’ 14.3k β’ 180
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GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
Paper β’ 2508.06471 β’ Published β’ 192 -
GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
Paper β’ 2507.01006 β’ Published β’ 240 -
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Paper β’ 2507.06261 β’ Published β’ 64 -
SmallThinker: A Family of Efficient Large Language Models Natively Trained for Local Deployment
Paper β’ 2507.20984 β’ Published β’ 56
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MiniMaxAI/MiniMax-Text-01-hf
Text Generation β’ 456B β’ Updated β’ 10.3k β’ 8 -
MiniMaxAI/MiniMax-M1-80k-hf
Text Generation β’ 456B β’ Updated β’ 74 β’ 6 -
MiniMaxAI/MiniMax-M1-40k-hf
Text Generation β’ Updated β’ 73 β’ 10 -
MiniMaxAI/MiniMax-Text-01
Text Generation β’ 456B β’ Updated β’ 2.26k β’ 650
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Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers
Paper β’ 2506.14702 β’ Published β’ 3 -
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
Paper β’ 2506.13585 β’ Published β’ 272 -
Scaling Test-time Compute for LLM Agents
Paper β’ 2506.12928 β’ Published β’ 63 -
A Survey on Latent Reasoning
Paper β’ 2507.06203 β’ Published β’ 93
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s3: You Don't Need That Much Data to Train a Search Agent via RL
Paper β’ 2505.14146 β’ Published β’ 19 -
Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI
Paper β’ 2505.19443 β’ Published β’ 15 -
ARM: Adaptive Reasoning Model
Paper β’ 2505.20258 β’ Published β’ 45 -
Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles
Paper β’ 2505.19914 β’ Published β’ 43
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GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
Paper β’ 2508.06471 β’ Published β’ 192 -
NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model
Paper β’ 2508.14444 β’ Published β’ 38 -
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Paper β’ 2507.06261 β’ Published β’ 64 -
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
Paper β’ 2506.13585 β’ Published β’ 272
-
GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
Paper β’ 2508.06471 β’ Published β’ 192 -
GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
Paper β’ 2507.01006 β’ Published β’ 240 -
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Paper β’ 2507.06261 β’ Published β’ 64 -
SmallThinker: A Family of Efficient Large Language Models Natively Trained for Local Deployment
Paper β’ 2507.20984 β’ Published β’ 56
-
Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning
Paper β’ 2506.07044 β’ Published β’ 114 -
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning
Paper β’ 2506.09513 β’ Published β’ 100 -
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
Paper β’ 2505.24863 β’ Published β’ 97 -
Seedance 1.0: Exploring the Boundaries of Video Generation Models
Paper β’ 2506.09113 β’ Published β’ 104
-
MiniMaxAI/MiniMax-Text-01-hf
Text Generation β’ 456B β’ Updated β’ 10.3k β’ 8 -
MiniMaxAI/MiniMax-M1-80k-hf
Text Generation β’ 456B β’ Updated β’ 74 β’ 6 -
MiniMaxAI/MiniMax-M1-40k-hf
Text Generation β’ Updated β’ 73 β’ 10 -
MiniMaxAI/MiniMax-Text-01
Text Generation β’ 456B β’ Updated β’ 2.26k β’ 650
-
Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers
Paper β’ 2506.14702 β’ Published β’ 3 -
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
Paper β’ 2506.13585 β’ Published β’ 272 -
Scaling Test-time Compute for LLM Agents
Paper β’ 2506.12928 β’ Published β’ 63 -
A Survey on Latent Reasoning
Paper β’ 2507.06203 β’ Published β’ 93
-
NCTC OSINT AGENT
π12Interact with an OSINT agent to gather intelligence
-
MiniMax M1
π¬347Generate code from text prompts
-
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
Paper β’ 2506.13585 β’ Published β’ 272 -
General AI Assistant - MCP Client & Server
π€6Solves GAIA Benchmark & Humanity's Last Exam problems
-
MiniMax M1
π¬347Generate code from text prompts
-
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
Paper β’ 2506.13585 β’ Published β’ 272 -
MiniMaxAI/MiniMax-M1-80k
Text Generation β’ 456B β’ Updated β’ 272 β’ β’ 685 -
MiniMaxAI/MiniMax-M1-40k
Text Generation β’ 456B β’ Updated β’ 14.3k β’ 180
-
s3: You Don't Need That Much Data to Train a Search Agent via RL
Paper β’ 2505.14146 β’ Published β’ 19 -
Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI
Paper β’ 2505.19443 β’ Published β’ 15 -
ARM: Adaptive Reasoning Model
Paper β’ 2505.20258 β’ Published β’ 45 -
Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles
Paper β’ 2505.19914 β’ Published β’ 43