5 citations · 16 across the 13 of their papers we have counts for
15 papers
Pixels or Code? Benchmarking Instruction-Guided Visual Artifact Operation
Shun Guo, Shuizhou Shi, Boao Wang +4
Visual artifacts such as charts, technical diagrams, webpages, and document pages underpin scientific, commercial, and engineering workflows. Editing these artifacts requires direc…
CineForge: Self-Improving Agents for Long-Horizon Video Generation
Junxiang Liu, Lin Wang, Haiyu Shi +10
Long-horizon story-driven video generation requires a production agent to coordinate narrative decomposition, state tracking, shot design, prompt construction, rendering, and revis…
FinDocMRE: A Benchmark for Document-Level Financial Multimodal Reasoning Evaluation
Jiayong Zhu, Jiangtong Li, Jinru Ding +3
While Large Multimodal Models (LMMs) excel in general visual tasks, their deployment in specialized financial contexts remains insufficient. Existing benchmarks prioritize isolated…
PRiSE-EEG: A Prior-Guided Foundation Model with Depth-Stratified Experts for Cross-Paradigm EEG Representation Learning
Wei Xiong, Jiangtong Li, Kun Zhu +2
EEG foundation models aim to learn reusable representations across heterogeneous paradigms, yet existing approaches often use uniform adaptation mechanisms and are typically report…
Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers
Dongyi Liu, Jiangtong Li
Graph Neural Networks(GNNs) are vulnerable to backdoor attacks, where adversaries implant malicious triggers to manipulate model predictions. Existing trigger generators are often…
DynaMind: Reconstructing Dynamic Visual Scenes from EEG by Aligning Temporal Dynamics and Multimodal Semantics to Guided Diffusion
Junxiang Liu, Junming Lin, Jiangtong Li +1
Reconstruction dynamic visual scenes from electroencephalography (EEG) signals remains a primary challenge in brain decoding, limited by the low spatial resolution of EEG, a tempor…
EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models
Wei Xiong, Jiangtong Li, Jie Li +2
Electroencephalography foundation models (EEG-FMs) have advanced brain signal analysis, but the lack of standardized evaluation benchmarks impedes model comparison and scientific p…
InspireDebate: Multi-Dimensional Subjective-Objective Evaluation-Guided Reasoning and Optimization for Debating
Fuyu Wang, Jiangtong Li, Kun Zhu +1
With the rapid advancements in large language models (LLMs), debating tasks, such as argument quality assessment and debate process simulation, have made significant progress. Howe…
FinLMM-R1: Enhancing Financial Reasoning in LMM through Scalable Data and Reward Design
Kai Lan, Jiayong Zhu, Jiangtong Li +3
Large Multimodal Models (LMMs) demonstrate significant cross-modal reasoning capabilities. However, financial applications face challenges due to the lack of high-quality multimoda…
CFBenchmark-MM: Chinese Financial Assistant Benchmark for Multimodal Large Language Model
Jiangtong Li, Yiyun Zhu, Dawei Cheng +2
Multimodal Large Language Models (MLLMs) have rapidly evolved with the growth of Large Language Models (LLMs) and are now applied in various fields. In finance, the integration of…
ALFEE: Adaptive Large Foundation Model for EEG Representation
Wei Xiong, Junming Lin, Jiangtong Li +2
While foundation models excel in text, image, and video domains, the critical biological signals, particularly electroencephalography(EEG), remain underexplored. EEG benefits neuro…
Attack by Yourself: Effective and Unnoticeable Multi-Category Graph Backdoor Attacks with Subgraph Triggers Pool
Jiangtong Li, Dungy Liu, Dawei Cheng +1
\textbf{G}raph \textbf{N}eural \textbf{N}etworks~(GNNs) have achieved significant success in various real-world applications, including social networks, finance systems, and traffi…
Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning
Yuxuan Bian, Xuan Ju, Jiangtong Li +3
In this study, we present aLLM4TS, an innovative framework that adapts Large Language Models (LLMs) for time-series representation learning. Central to our approach is that we reco…
CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model
Yang Lei, Jiangtong Li, Dawei Cheng +2
Large language models (LLMs) have demonstrated great potential in the financial domain. Thus, it becomes important to assess the performance of LLMs in the financial tasks. In this…
CFGPT: Chinese Financial Assistant with Large Language Model
Jiangtong Li, Yuxuan Bian, Guoxuan Wang +4
Large language models (LLMs) have demonstrated great potential in natural language processing tasks within the financial domain. In this work, we present a Chinese Financial Genera…