Large language models (LLMs) have emerged as a promising foundation to build generally-capable agents (LLM-based agents) that can handle multi-turn decision-making tasks across various environments. However, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents, and enables exploration and learning for their self-improvement. To address this, we propose AgentGym, a framework featuring 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. We construct expanded instruction set, high-quality trajectories, and comprehensive benchmarking suite for developing LLM-based agents. Moreover, AgentGym supports interactive exploration and learning for agents through multi-turn interactions and real-time feedback. Based on AgentGym, we take the initial step to develop LLM-based agents that can handle diverse tasks via methods like self-improvement or reinforcement learning. Experimental results show that the trained agents can achieve results comparable to commercial models. We hope our work can help the community develop more advanced LLM-based agents. We release the code, dataset, benchmark, and checkpoints at https://agentgym.github.io/.

Joint relation extraction models effectively mitigate the error propagation problem inherently present in pipeline models. Nevertheless, joint models face challenges including high computational complexity, complex network architectures, difficult parameter tuning, and notably, limited interpretability. In contrast, recent advances in pipeline relation extraction models (PURE, PL-Marker) have attracted considerable attention due to their lightweight design and high extraction accuracy. A key advancement is the introduction of a marker mechanism, which enhances relation extraction (RE) process by highlighting entities. However, these models primarily focus on generating correct labels. In doing so, they neglect the label selection process. Moreover, they fail to adequately capture the intricate interactions between entity pairs. To overcome these limitations, we develop a Candidate Label Markers (CLMs) mechanism that prioritizes strategic label selection over simple label generation. Furthermore, we facilitate interactions among diverse relation pairs, enabling the identification of more intricate relational patterns. Experimental results show that we achieve a new SOTA performance. Specifically, based on the same Named Entity Recognition (NER) results as theirs, we improve the SOTA methods by 2.5%, 1.9%, 1.2% in terms of strict F1 scores on SciERC, ACE05 and ACE04.

Multimodal large language models (MLLMs) hold great promise for automating complex financial analysis. To comprehensively evaluate their capabilities, we introduce VisFinEval, the first large-scale Chinese benchmark that spans the full front-middle-back office lifecycle of financial tasks. VisFinEval comprises 15,848 annotated question–answer pairs drawn from eight common financial image modalities (e.g., K-line charts, financial statements, official seals), organized into three hierarchical scenario depths: Financial Knowledge & Data Analysis, Financial Analysis & Decision Support, and Financial Risk Control & Asset Optimization. We evaluate 21 state-of-the-art MLLMs in a zero-shot setting. The top model, Qwen-VL-max, achieves an overall accuracy of 76.3%, outperforming non-expert humans but trailing financial experts by over 14 percentage points. Our error analysis uncovers six recurring failure modes—including cross-modal misalignment, hallucinations, and lapses in business-process reasoning—that highlight critical avenues for future research. VisFinEval aims to accelerate the development of robust, domain-tailored MLLMs capable of seamlessly integrating textual and visual financial information. The data and the code are available at https://github.com/SUFE-AIFLM-Lab/VisFinEval.

Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored, and their performance on complex tasks like financial agent remains unknown. This paper presents FinEval, a benchmark designed to evaluate LLMs’ financial domain knowledge and practical abilities. The dataset contains 8,351 questions categorized into four different key areas: Financial Academic Knowledge, Financial Industry Knowledge, Financial Security Knowledge, and Financial Agent. Financial Academic Knowledge comprises 4,661 multiple-choice questions spanning 34 subjects such as finance and economics. Financial Industry Knowledge contains 1,434 questions covering practical scenarios like investment research. Financial Security Knowledge assesses models through 1,640 questions on topics like application security and cryptography. Financial Agent evaluates tool usage and complex reasoning with 616 questions. FinEval has multiple evaluation settings, including zero-shot, five-shot with chain-of-thought, and assesses model performance using objective and subjective criteria. Our results show that Claude 3.5-Sonnet achieves the highest weighted average score of 72.9 across all financial domain categories under zero-shot setting. Our work provides a comprehensive benchmark closely aligned with Chinese financial domain. The data and the code are available at https://github.com/SUFE-AIFLMLab/FinEval.

知识图谱问题生成任务是从给定的知识图谱中生成与其相关的问题。目前,知识图谱问题生成模型主要使用基于RNN或Transformer对知识图谱子图进行编码,但这种方式丢失了显式的图结构化信息,在解码器中忽视了局部信息对节点的重要性。本文提出迭代信息传递图编码器来编码子图,获取子图显式的图结构化信息,此外,我们还使用滑动窗口注意力机制提高RNN解码器,提升子图局部信息对节点的重要度。从WQ和PQ数据集上的实验结果看,我们提出的模型比KTG模型在BLEU4指标上分别高出2.16和15.44,证明了该模型的有效性。

Catastrophic forgetting in neural networks indicates the performance decreasing of deep learning models on previous tasks while learning new tasks. To address this problem, we propose a novel Continual Learning Long Short Term Memory (CL-LSTM) cell in Recurrent Neural Network (RNN) in this paper. CL-LSTM considers not only the state of each individual task’s output gates but also the correlation of the states between tasks, so that the deep learning models can incrementally learn new tasks without catastrophically forgetting previously tasks. Experimental results demonstrate significant improvements of CL-LSTM over state-of-the-art approaches on spoken language understanding (SLU) tasks.