Scaling Laws for Native Multimodal Models Scaling Laws for Native Multimodal Models
Paper
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2504.07951
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Published
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28
Discussions
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Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability
Paper • 2504.08003 • Published • 49 -
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models
Paper • 2504.11468 • Published • 28 -
Towards Learning to Complete Anything in Lidar
Paper • 2504.12264 • Published • 10 -
Antidistillation Sampling
Paper • 2504.13146 • Published • 61 -
A Strategic Coordination Framework of Small LLMs Matches Large LLMs in Data Synthesis
Paper • 2504.12322 • Published • 28 -
Rethinking Reflection in Pre-Training
Paper • 2504.04022 • Published • 79 -
Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models
Paper • 2504.04823 • Published • 30 -
Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1)
Paper • 2504.03151 • Published • 14 -
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Paper • 2504.01990 • Published • 285 -
What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models
Paper • 2503.24235 • Published • 54 -
Entropy-Based Adaptive Weighting for Self-Training
Paper • 2503.23913 • Published • 4 -
Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency
Paper • 2504.18589 • Published • 11 -
Can MLLMs Guide Me Home? A Benchmark Study on Fine-Grained Visual Reasoning from Transit Maps
Paper • 2505.18675 • Published • 23 -
MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research
Paper • 2505.19955 • Published • 10 -
The Coverage Principle: A Framework for Understanding Compositional Generalization
Paper • 2505.20278 • Published • 7 -
Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI
Paper • 2505.19443 • Published • 15