12 citations · 18 across the 16 of their papers we have counts for
9 papers · 1 filter
ElasticMoE: An Efficient Auto Scaling Method for Mixture-of-Experts Models
Gursimran Singh, Timothy Yu, Haley Li +7
Mixture-of-Experts (MoE) models promise efficient scaling of large language models (LLMs) by activating only a small subset of experts per token, but their parallelized inference p…
MARS2 2025 Challenge on Multimodal Reasoning: Datasets, Methods, Results, Discussion, and Outlook
Peng Xu, Shengwu Xiong, Jiajun Zhang +125
This paper reviews the MARS2 2025 Challenge on Multimodal Reasoning. We aim to bring together different approaches in multimodal machine learning and LLMs via a large benchmark. We…
Evaluating LLMs Without Oracle Feedback: Agentic Annotation Evaluation Through Unsupervised Consistency Signals
Cheng Chen, Haiyan Yin, Ivor Tsang
Large Language Models (LLMs), when paired with prompt-based tasks, have significantly reduced data annotation costs and reliance on human annotators. However, evaluating the qualit…
UniSearch: Rethinking Search System with a Unified Generative Architecture
Jiahui Chen, Xiaoze Jiang, Zhibo Wang +18
Modern search systems play a crucial role in facilitating information acquisition. Traditional search engines typically rely on a cascaded architecture, where results are retrieved…
RouteNator: A Router-Based Multi-Modal Architecture for Generating Synthetic Training Data for Function Calling LLMs
Vibha Belavadi, Tushar Vatsa, Dewang Sultania +5
This paper addresses fine-tuning Large Language Models (LLMs) for function calling tasks when real user interaction data is unavailable. In digital content creation tools, where us…
NVSPolicy: Adaptive Novel-View Synthesis for Generalizable Language-Conditioned Policy Learning
Le Shi, Yifei Shi, Xin Xu +3
Recent advances in deep generative models demonstrate unprecedented zero-shot generalization capabilities, offering great potential for robot manipulation in unstructured environme…
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…
Can Post-Training Quantization Benefit from an Additional QLoRA Integration?
Xiliang Zhu, Elena Khasanova, Cheng Chen
Large language models (LLMs) have transformed natural language processing but pose significant challenges for real-world deployment. These models necessitate considerable computing…
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…