47 citations · 62 across the 16 of their papers we have counts for
16 papers
PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models
Zichao Lin, Yifeng Xie, Bowen Qu +30
We introduce PerceptionBench, a benchmark specifically designed to evaluate the atomic visual perception capabilities of Multimodal Large Language Models (MLLMs). Existing benchmar…
Towards Predictive, Aligned, and Scalable Robot Learning
Peijun Tang, Shangjin Xie, Baifu Huang +6
Learning, at its core, extends beyond memorization to the ability to reason and solve novel problems by navigating a space of possibilities. We introduce Lumo-2, a latent world-act…
Kimi K2.5: Visual Agentic Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +333
We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that…
WorldVQA: Measuring Atomic World Knowledge in Multimodal Large Language Models
Runjie Zhou, Youbo Shao, Haoyu Lu +16
We introduce WorldVQA, a benchmark designed to evaluate the atomic visual world knowledge of Multimodal Large Language Models (MLLMs). Unlike current evaluations, which often confl…
Towards Pixel-Level VLM Perception via Simple Points Prediction
Tianhui Song, Haoyu Lu, Hao Yang +8
We present SimpleSeg, a strikingly simple yet highly effective approach to endow Multimodal Large Language Models (MLLMs) with native pixel-level perception. Our method reframes se…
Operation Veja: Fixing Fundamental Concepts Missing from Modern Roleplaying Training Paradigms
Yueze Liu, Ajay Nagi Reddy Kumdam, Ronit Kanjilal +2
Modern roleplaying models are increasingly sophisticated, yet they consistently struggle to capture the essence of believable, engaging characters. We argue this failure stems from…
Mind to Hand: Purposeful Robotic Control via Embodied Reasoning
Peijun Tang, Shangjin Xie, Binyan Sun +5
Humans act with context and intention, with reasoning playing a central role. While internet-scale data has enabled broad reasoning capabilities in AI systems, grounding these abil…
UI-UG: A Unified MLLM for UI Understanding and Generation
Hao Yang, Weijie Qiu, Ru Zhang +8
Although Multimodal Large Language Models (MLLMs) have been widely applied across domains, they are still facing challenges in domain-specific tasks, such as User Interface (UI) un…
MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior Understanding
Renjie Li, Ruijie Ye, Mingyang Wu +4
Humans are integral components of the transportation ecosystem, and understanding their behaviors is crucial to facilitating the development of safe driving systems. Although recen…
Gated Multimodal Graph Learning for Personalized Recommendation
Sibei Liu, Yuanzhe Zhang, Xiang Li +3
Multimodal recommendation has emerged as a promising solution to alleviate the cold-start and sparsity problems in collaborative filtering by incorporating rich content information…
Online Iterative Self-Alignment for Radiology Report Generation
Ting Xiao, Lei Shi, Yang Zhang +3
Radiology Report Generation (RRG) is an important research topic for relieving radiologist' heavy workload. Existing RRG models mainly rely on supervised fine-tuning (SFT) based on…
Kimi-VL Technical Report
Kimi Team, Angang Du, Bohong Yin +92
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong…
DKPROMPT: Domain Knowledge Prompting Vision-Language Models for Open-World Planning
Xiaohan Zhang, Zainab Altaweel, Yohei Hayamizu +6
Vision-language models (VLMs) have been applied to robot task planning problems, where the robot receives a task in natural language and generates plans based on visual inputs. Whi…
DeepSeek-VL: Towards Real-World Vision-Language Understanding
Haoyu Lu, Wen Liu, Bo Zhang +12
We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. Our approach is structured around three ke…
On the Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applications
Chenjiao Tan, Qian Cao, Yiwei Li +15
The advent of large language models (LLMs) has heightened interest in their potential for multimodal applications that integrate language and vision. This paper explores the capabi…
Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap
Mengmi Zhang, Elisa Pavarino, Xiao Liu +20
As AI becomes increasingly embedded in daily life, ascertaining whether an agent is human is critical. We systematically benchmark AI's ability to imitate humans in three language…