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InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning
Paper • 2502.11573 • Published • 8 -
Boosting Multimodal Reasoning with MCTS-Automated Structured Thinking
Paper • 2502.02339 • Published • 22 -
video-SALMONN-o1: Reasoning-enhanced Audio-visual Large Language Model
Paper • 2502.11775 • Published • 8 -
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Paper • 2412.18319 • Published • 37
Collections
Discover the best community collections!
Collections including paper arxiv:2503.12605
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 26 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 13 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 43 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 22
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Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
Paper • 2503.12605 • Published • 24 -
R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization
Paper • 2503.12937 • Published • 23 -
Reflect-DiT: Inference-Time Scaling for Text-to-Image Diffusion Transformers via In-Context Reflection
Paper • 2503.12271 • Published • 6
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RuCCoD: Towards Automated ICD Coding in Russian
Paper • 2502.21263 • Published • 122 -
Unified Reward Model for Multimodal Understanding and Generation
Paper • 2503.05236 • Published • 105 -
Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching
Paper • 2503.05179 • Published • 43 -
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
Paper • 2503.05592 • Published • 25
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Forget What You Know about LLMs Evaluations - LLMs are Like a Chameleon
Paper • 2502.07445 • Published • 11 -
ARR: Question Answering with Large Language Models via Analyzing, Retrieving, and Reasoning
Paper • 2502.04689 • Published • 7 -
Analyze Feature Flow to Enhance Interpretation and Steering in Language Models
Paper • 2502.03032 • Published • 58 -
Preference Leakage: A Contamination Problem in LLM-as-a-judge
Paper • 2502.01534 • Published • 39
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Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs
Paper • 2501.18585 • Published • 56 -
LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!
Paper • 2502.07374 • Published • 37 -
Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling
Paper • 2502.06703 • Published • 143 -
S*: Test Time Scaling for Code Generation
Paper • 2502.14382 • Published • 60
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Transformer^2: Self-adaptive LLMs
Paper • 2501.06252 • Published • 53 -
s1: Simple test-time scaling
Paper • 2501.19393 • Published • 112 -
Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling
Paper • 2502.06703 • Published • 143 -
Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models
Paper • 2501.12370 • Published • 11
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Analyzing The Language of Visual Tokens
Paper • 2411.05001 • Published • 24 -
Large Multi-modal Models Can Interpret Features in Large Multi-modal Models
Paper • 2411.14982 • Published • 16 -
Rethinking Token Reduction in MLLMs: Towards a Unified Paradigm for Training-Free Acceleration
Paper • 2411.17686 • Published • 20 -
On the Limitations of Vision-Language Models in Understanding Image Transforms
Paper • 2503.09837 • Published • 10