9 citations · 19 across the 13 of their papers we have counts for
13 papers
DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
DeepSeek-AI, Anyi Xu, Bangcai Lin +315
We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models -- DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSe…
Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding
StepFun, :, Bin Wang +195
Large language models (LLMs) face low hardware efficiency during decoding, especially for long-context reasoning tasks. This paper introduces Step-3, a 321B-parameter VLM with hard…
Perception-R1: Pioneering Perception Policy with Reinforcement Learning
En Yu, Kangheng Lin, Liang Zhao +11
Inspired by the success of DeepSeek-R1, we explore the potential of rule-based reinforcement learning (RL) in MLLM post-training for perception policy learning. While promising, ou…
Perception in Reflection
Yana Wei, Liang Zhao, Kangheng Lin +8
We present a perception in reflection paradigm designed to transcend the limitations of current large vision-language models (LVLMs), which are expected yet often fail to achieve p…
Unhackable Temporal Rewarding for Scalable Video MLLMs
En Yu, Kangheng Lin, Liang Zhao +8
In the pursuit of superior video-processing MLLMs, we have encountered a perplexing paradox: the "anti-scaling law", where more data and larger models lead to worse performance. Th…
PerPO: Perceptual Preference Optimization via Discriminative Rewarding
Zining Zhu, Liang Zhao, Kangheng Lin +7
This paper presents Perceptual Preference Optimization (PerPO), a perception alignment method aimed at addressing the visual discrimination challenges in generative pre-trained mul…
Slow Perception: Let's Perceive Geometric Figures Step-by-step
Haoran Wei, Youyang Yin, Yumeng Li +6
Recently, "visual o1" began to enter people's vision, with expectations that this slow-thinking design can solve visual reasoning tasks, especially geometric math problems. However…
General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model
Haoran Wei, Chenglong Liu, Jinyue Chen +9
Traditional OCR systems (OCR-1.0) are increasingly unable to meet people's usage due to the growing demand for intelligent processing of man-made optical characters. In this paper,…
Focus Anywhere for Fine-grained Multi-page Document Understanding
Chenglong Liu, Haoran Wei, Jinyue Chen +7
Modern LVLMs still struggle to achieve fine-grained document understanding, such as OCR/translation/caption for regions of interest to the user, tasks that require the context of t…
OneChart: Purify the Chart Structural Extraction via One Auxiliary Token
Jinyue Chen, Lingyu Kong, Haoran Wei +6
Chart parsing poses a significant challenge due to the diversity of styles, values, texts, and so forth. Even advanced large vision-language models (LVLMs) with billions of paramet…
Small Language Model Meets with Reinforced Vision Vocabulary
Haoran Wei, Lingyu Kong, Jinyue Chen +6
Playing Large Vision Language Models (LVLMs) in 2023 is trendy among the AI community. However, the relatively large number of parameters (more than 7B) of popular LVLMs makes it d…
Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models
Haoran Wei, Lingyu Kong, Jinyue Chen +6
Modern Large Vision-Language Models (LVLMs) enjoy the same vision vocabulary -- CLIP, which can cover most common vision tasks. However, for some special vision task that needs den…
Merlin:Empowering Multimodal LLMs with Foresight Minds
En Yu, Liang Zhao, Yana Wei +8
Humans possess the remarkable ability to foresee the future to a certain extent based on present observations, a skill we term as foresight minds. However, this capability remains…