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InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning
Paper • 2502.11573 • Published • 9 -
Boosting Multimodal Reasoning with MCTS-Automated Structured Thinking
Paper • 2502.02339 • Published • 23 -
video-SALMONN-o1: Reasoning-enhanced Audio-visual Large Language Model
Paper • 2502.11775 • Published • 9 -
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Paper • 2412.18319 • Published • 39
Collections
Discover the best community collections!
Collections including paper arxiv:2411.10440
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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 • 86 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 156 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
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The Evolution of Multimodal Model Architectures
Paper • 2405.17927 • Published • 1 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 104 -
Efficient Architectures for High Resolution Vision-Language Models
Paper • 2501.02584 • Published -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 134
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URSA: Understanding and Verifying Chain-of-thought Reasoning in Multimodal Mathematics
Paper • 2501.04686 • Published • 53 -
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
Paper • 2501.09686 • Published • 41 -
LLaVA-CoT: Let Vision Language Models Reason Step-by-Step
Paper • 2411.10440 • Published • 131 -
TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding
Paper • 2502.19400 • Published • 47
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 31 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 15 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 45 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 24
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iVideoGPT: Interactive VideoGPTs are Scalable World Models
Paper • 2405.15223 • Published • 17 -
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
Paper • 2405.15574 • Published • 55 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 91 -
Matryoshka Multimodal Models
Paper • 2405.17430 • Published • 35
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Multimodal Inconsistency Reasoning (MMIR): A New Benchmark for Multimodal Reasoning Models
Paper • 2502.16033 • Published • 18 -
rippleripple/MMIR
Viewer • Updated • 534 • 118 • 2 -
LLaVA-CoT: Let Vision Language Models Reason Step-by-Step
Paper • 2411.10440 • Published • 131 -
GRIT: Teaching MLLMs to Think with Images
Paper • 2505.15879 • Published • 13
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Exploring the Potential of Encoder-free Architectures in 3D LMMs
Paper • 2502.09620 • Published • 26 -
The Evolution of Multimodal Model Architectures
Paper • 2405.17927 • Published • 1 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 104 -
Efficient Architectures for High Resolution Vision-Language Models
Paper • 2501.02584 • Published
-
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Paper • 2412.18319 • Published • 39 -
Token-Budget-Aware LLM Reasoning
Paper • 2412.18547 • Published • 46 -
Efficiently Serving LLM Reasoning Programs with Certaindex
Paper • 2412.20993 • Published • 36 -
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners
Paper • 2412.17256 • Published • 47
-
InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning
Paper • 2502.11573 • Published • 9 -
Boosting Multimodal Reasoning with MCTS-Automated Structured Thinking
Paper • 2502.02339 • Published • 23 -
video-SALMONN-o1: Reasoning-enhanced Audio-visual Large Language Model
Paper • 2502.11775 • Published • 9 -
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Paper • 2412.18319 • Published • 39
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 31 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 15 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 45 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 24
-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 86 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 156 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
-
iVideoGPT: Interactive VideoGPTs are Scalable World Models
Paper • 2405.15223 • Published • 17 -
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
Paper • 2405.15574 • Published • 55 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 91 -
Matryoshka Multimodal Models
Paper • 2405.17430 • Published • 35
-
Multimodal Inconsistency Reasoning (MMIR): A New Benchmark for Multimodal Reasoning Models
Paper • 2502.16033 • Published • 18 -
rippleripple/MMIR
Viewer • Updated • 534 • 118 • 2 -
LLaVA-CoT: Let Vision Language Models Reason Step-by-Step
Paper • 2411.10440 • Published • 131 -
GRIT: Teaching MLLMs to Think with Images
Paper • 2505.15879 • Published • 13
-
The Evolution of Multimodal Model Architectures
Paper • 2405.17927 • Published • 1 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 104 -
Efficient Architectures for High Resolution Vision-Language Models
Paper • 2501.02584 • Published -
Building and better understanding vision-language models: insights and future directions
Paper • 2408.12637 • Published • 134
-
Exploring the Potential of Encoder-free Architectures in 3D LMMs
Paper • 2502.09620 • Published • 26 -
The Evolution of Multimodal Model Architectures
Paper • 2405.17927 • Published • 1 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 104 -
Efficient Architectures for High Resolution Vision-Language Models
Paper • 2501.02584 • Published
-
URSA: Understanding and Verifying Chain-of-thought Reasoning in Multimodal Mathematics
Paper • 2501.04686 • Published • 53 -
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
Paper • 2501.09686 • Published • 41 -
LLaVA-CoT: Let Vision Language Models Reason Step-by-Step
Paper • 2411.10440 • Published • 131 -
TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding
Paper • 2502.19400 • Published • 47
-
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Paper • 2412.18319 • Published • 39 -
Token-Budget-Aware LLM Reasoning
Paper • 2412.18547 • Published • 46 -
Efficiently Serving LLM Reasoning Programs with Certaindex
Paper • 2412.20993 • Published • 36 -
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners
Paper • 2412.17256 • Published • 47