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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 84 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 152 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 24
Collections
Discover the best community collections!
Collections including paper arxiv:2506.16406
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Describe Anything: Detailed Localized Image and Video Captioning
Paper • 2504.16072 • Published • 63 -
EmbodiedCity: A Benchmark Platform for Embodied Agent in Real-world City Environment
Paper • 2410.09604 • Published -
Geospatial Mechanistic Interpretability of Large Language Models
Paper • 2505.03368 • Published • 10 -
Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation
Paper • 2505.02836 • Published • 7
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Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning
Paper • 2506.07044 • Published • 110 -
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning
Paper • 2506.09513 • Published • 98 -
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
Paper • 2505.24863 • Published • 96 -
Seedance 1.0: Exploring the Boundaries of Video Generation Models
Paper • 2506.09113 • Published • 99
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PRIMA.CPP: Speeding Up 70B-Scale LLM Inference on Low-Resource Everyday Home Clusters
Paper • 2504.08791 • Published • 134 -
TTRL: Test-Time Reinforcement Learning
Paper • 2504.16084 • Published • 120 -
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
Paper • 2504.17192 • Published • 114 -
Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights
Paper • 2506.16406 • Published • 124
-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 84 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 152 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 24
-
Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning
Paper • 2506.07044 • Published • 110 -
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning
Paper • 2506.09513 • Published • 98 -
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
Paper • 2505.24863 • Published • 96 -
Seedance 1.0: Exploring the Boundaries of Video Generation Models
Paper • 2506.09113 • Published • 99
-
Describe Anything: Detailed Localized Image and Video Captioning
Paper • 2504.16072 • Published • 63 -
EmbodiedCity: A Benchmark Platform for Embodied Agent in Real-world City Environment
Paper • 2410.09604 • Published -
Geospatial Mechanistic Interpretability of Large Language Models
Paper • 2505.03368 • Published • 10 -
Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation
Paper • 2505.02836 • Published • 7
-
PRIMA.CPP: Speeding Up 70B-Scale LLM Inference on Low-Resource Everyday Home Clusters
Paper • 2504.08791 • Published • 134 -
TTRL: Test-Time Reinforcement Learning
Paper • 2504.16084 • Published • 120 -
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
Paper • 2504.17192 • Published • 114 -
Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights
Paper • 2506.16406 • Published • 124