21 citations · 55 across the 19 of their papers we have counts for
19 papers
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
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…
JigsawComm: Joint Semantic Feature Encoding and Transmission for Communication-Efficient Cooperative Perception
Chenyi Wang, Zhaowei Li, Ming F. Li +1
Multi-agent cooperative perception (CP) promises to overcome the inherent occlusion and range limitations of single-agent systems in autonomous driving, yet its practicality is sev…
UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets
Pengyu Wang, Shaojun Zhou, Chenkun Tan +7
Unified vision large language models (VLLMs) have recently achieved impressive advancements in both multimodal understanding and generation, powering applications such as visual qu…
Decoupled Proxy Alignment: Mitigating Language Prior Conflict for Multimodal Alignment in MLLM
Chenkun Tan, Pengyu Wang, Shaojun Zhou +6
Multimodal large language models (MLLMs) have gained significant attention due to their impressive ability to integrate vision and language modalities. Recent advancements in MLLMs…
QuantV2X: A Fully Quantized Multi-Agent System for Cooperative Perception
Seth Z. Zhao, Huizhi Zhang, Zhaowei Li +11
Cooperative perception through Vehicle-to-Everything (V2X) communication offers significant potential for enhancing vehicle perception by mitigating occlusions and expanding the fi…
Kimi K2: Open Agentic Intelligence
Kimi Team, Yifan Bai, Yiping Bao +195
We introduce Kimi K2, a Mixture-of-Experts (MoE) large language model with 32 billion activated parameters and 1 trillion total parameters. We propose the MuonClip optimizer, which…
Automated Sentiment Classification and Topic Discovery in Large-Scale Social Media Streams
Yiwen Lu, Siheng Xiong, Zhaowei Li
We present a framework for large-scale sentiment and topic analysis of Twitter discourse. Our pipeline begins with targeted data collection using conflict-specific keywords, follow…
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…
Vote-Tree-Planner: Optimizing Execution Order in LLM-based Task Planning Pipeline via Voting
Chaoyuan Zhang, Zhaowei Li, Wentao Yuan
Integrating large language models (LLMs) into closed-loop robotic task planning has become increasingly popular within embodied artificial intelligence. Previous efforts mainly foc…
Kimi k1.5: Scaling Reinforcement Learning with LLMs
Kimi Team, Angang Du, Bofei Gao +93
Language model pretraining with next token prediction has proved effective for scaling compute but is limited to the amount of available training data. Scaling reinforcement learni…
Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Models
Wei Wang, Zhaowei Li, Qi Xu +7
Multi-modal large language models (MLLMs) have achieved remarkable success in fine-grained visual understanding across a range of tasks. However, they often encounter significant c…
Understanding the Role of LLMs in Multimodal Evaluation Benchmarks
Botian Jiang, Lei Li, Xiaonan Li +5
The rapid advancement of Multimodal Large Language Models (MLLMs) has been accompanied by the development of various benchmarks to evaluate their capabilities. However, the true na…
UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model
Zhaowei Li, Wei Wang, YiQing Cai +7
Significant advancements has recently been achieved in the field of multi-modal large language models (MLLMs), demonstrating their remarkable capabilities in understanding and reas…
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models
Wei Wang, Zhaowei Li, Qi Xu +7
The deployment of large language models (LLMs) faces considerable challenges concerning resource constraints and inference efficiency. Recent research has increasingly focused on s…
SpeechAlign: Aligning Speech Generation to Human Preferences
Dong Zhang, Zhaowei Li, Shimin Li +4
Speech language models have significantly advanced in generating realistic speech, with neural codec language models standing out. However, the integration of human feedback to ali…
CMP: Cooperative Motion Prediction with Multi-Agent Communication
Zehao Wang, Yuping Wang, Zhuoyuan Wu +4
The confluence of the advancement of Autonomous Vehicles (AVs) and the maturity of Vehicle-to-Everything (V2X) communication has enabled the capability of cooperative connected and…
SpeechAgents: Human-Communication Simulation with Multi-Modal Multi-Agent Systems
Dong Zhang, Zhaowei Li, Pengyu Wang +3
Human communication is a complex and diverse process that not only involves multiple factors such as language, commonsense, and cultural backgrounds but also requires the participa…
GroundingGPT:Language Enhanced Multi-modal Grounding Model
Zhaowei Li, Qi Xu, Dong Zhang +9
Multi-modal large language models have demonstrated impressive performance across various tasks in different modalities. However, existing multi-modal models primarily emphasize ca…