activity
20232026
most citedODE-based Recurrent Model-free Reinforcement Learning for POMDPs

2 citations · 3 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2026

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…

cs.CL2025

Automating Hardware Design and Verification from Architectural Papers via a Neural-Symbolic Graph Framework

Haoyue Yang, Xuanle Zhao, Yujie Liu +5

The reproduction of hardware architectures from academic papers remains a significant challenge due to the lack of publicly available source code and the complexity of hardware des…

cs.AI2025

AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage

Xuanle Zhao, Zilin Sang, Yuxuan Li +7

Efficient reproduction of research papers is pivotal to accelerating scientific progress. However, the increasing complexity of proposed methods often renders reproduction a labor-…

cs.CL2025

ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs' Capability via Chart Editing

Xuanle Zhao, Xuexin Liu, Haoyue Yang +5

Although multimodal large language models (MLLMs) show promise in generating chart rendering code, editing charts via code presents a greater challenge. This task demands MLLMs to…

cs.AI2025★ 1 cited

ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation

Xuanle Zhao, Xianzhen Luo, Qi Shi +4

Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in chart understanding tasks. However, interpreting charts with textual descriptions often leads…

cs.LG2023★ 2 cited

ODE-based Recurrent Model-free Reinforcement Learning for POMDPs

Xuanle Zhao, Duzhen Zhang, Liyuan Han +2

Neural ordinary differential equations (ODEs) are widely recognized as the standard for modeling physical mechanisms, which help to perform approximate inference in unknown physica…