4 citations · 6 across the 10 of their papers we have counts for
12 papers
FINCOSMOS: An Open-World Evaluation Paradigm for Agents in Finance
Yupeng Cao, Xueqing Peng, Lingfei Qian +14
Language agents increasingly work for users on the open Web, and finance is where trusting them is hardest. The data must come from the Web, the strategy lives in the user's head,…
LLMs Don't Know What They Know: Benchmarking Temporal Self-Knowledge for Financial Backtesting
Lingfei Qian, Yan Wang, Xueqing Peng +12
Financial backtests of large language models (LLMs) are meaningful only if the model did not already know the outcomes it is asked to predict. Practitioners guard against this by t…
Rethinking Groups in Critic-Free RLVR
Yihong Wu, Liheng Ma, Lingfeng Xiao +4
Reinforcement learning (RL) has become a central paradigm for post-training large language models. Existing critic-free RL methods typically generate a group of rollouts for the sa…
Learning Agent-Compatible Context Management for Long-Horizon Tasks
Lu Yi, Runlin Lei, Liuyi Yao +6
LLM agents increasingly face long-horizon tasks such as web search and deep research in real-world applications, where accumulated context can cause long-context degradation and re…
Can LLM Agents Be CFOs? Benchmarking Long-Horizon Resource Allocation in an Uncertain Enterprise Environment
Yi Han, Yan Wang, Lingfei Qian +12
Large language model (LLM) agents are increasingly tested on complex tasks, but their ability to allocate scarce resources over long horizons remains unclear. Unlike reactive tasks…
Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation
Yan Wang, Yi Han, Lingfei Qian +11
Most recommendation benchmarks evaluate how well a model imitates user behavior. In financial advisory, however, observed actions can be noisy or short-sighted under market volatil…
When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents
Lingfei Qian, Xueqing Peng, Yan Wang +14
Although Large Language Model (LLM)-based agents are increasingly used in financial trading, it remains unclear whether they can reason and adapt in live markets, as most studies t…
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application
Xueqing Peng, Lingfei Qian, Yan Wang +44
Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing eval…
FinAudio: A Benchmark for Audio Large Language Models in Financial Applications
Yupeng Cao, Haohang Li, Yangyang Yu +10
Audio Large Language Models (AudioLLMs) have received widespread attention and have significantly improved performance on audio tasks such as conversation, audio understanding, and…
Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance
Lingfei Qian, Weipeng Zhou, Yan Wang +6
As the fundamental capability behind decision-making in finance, financial reasoning poses distinct challenges for LLMs. Although reinforcement learning (RL) have boosted generic r…
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
Jimin Huang, Mengxi Xiao, Dong Li +41
Financial LLMs hold promise for advancing financial tasks and domain-specific applications. However, they are limited by scarce corpora, weak multimodal capabilities, and narrow ev…
DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Based Event Argument Extraction with Slot Querying
Guanghui Wang, Dexi Liu, Jian-Yun Nie +5
Recent advancements in event argument extraction (EAE) involve incorporating useful auxiliary information into models during training and inference, such as retrieved instances and…