7 citations · 11 across the 22 of their papers we have counts for
25 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…
In the Guise of Correction: How Framing and Cultural Adaptation Erode LLM Safety on Real-World Criminal Cases
Mohsinul Kabir, Md. Tariquzzaman, Karam S. R. Elradie +4
Existing safety benchmarks rely largely on synthetic prompts with loosely grounded taxonomies and offer little focused analysis of severe crime categories. We introduce GUISE, a be…
One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread
Zhuoran Lu, Weilong Wang, Yangyang Yu +4
Social media platforms increasingly use AI-based credibility indicators to help users judge misinformation. Unlike individual human-AI decision-making, these indicators are embedde…
NTDH: Complex Reasoning for Comprehensive Affective Analysis
Tianlei Zhu, Zhiwei Liu, Yuyan Wang +2
Comprehensive affective analysis is challenging for two reasons: it spans heterogeneous prediction tasks with continuous, ordinal, and multi-label outputs, and affective meaning is…
Can LLMs Write Reliable Rubrics? A Meta-Evaluation for Experiment Reproduction
Hanhua Hong, Yizhi Li, Jiaoyan Chen +4
Rubric-based evaluation is a promising approach for assessing open-ended outputs from LLM-based research agents, particularly in paper reproduction, where direct paper-to-repositor…
JANUS: Evaluating Goal-Conditioned Distortion in Factually Grounded LLM Communication
Polydoros Giannouris, Mohsinul Kabir, Sophia Ananiadou
LLM deception is often evaluated through fabricated claims, explicit lies, or strategic concealment. Yet misleading communication can also arise from selectively presenting true fa…
AuditFraudBench: Benchmarking Audit Judgment in Detecting Fraudulent Misstatements
Zhiwei Liu, Yueru He, Qing Ou +4
Large language models (LLMs) have shown strong performance in financial analysis and surface-level factual error detection, yet their ability to identify fraudulent financial misin…
Herculean: An Agentic Benchmark for Financial Intelligence
Xueqing Peng, Zhuohan Xie, Yupeng Cao +60
As AI agents advance, the central question is no longer whether they can solve isolated financial tasks, but whether their capabilities generalize across complete professional work…
Concordia: Self-Improving Synthetic Tables for Federated LLMs
Jimin Huang, Duanyu Feng, Nuo Chen +8
Federated learning (FL) enables training large language models (LLMs) without sharing raw data, but adapting LLMs under strict data isolation and non-IID client distributions remai…
AutoRedTrader: Autonomous Red Teaming of Trading Agents through Synthetic Misinformation Injection
Zhiwei Liu, Yangyang Yu, Yupeng Cao +8
LLM-based financial agents increasingly rely on both numerical market data and textual signals for sequential trading and stock prediction. However, financial misinformation often…
MOIRA: Policy Memory and Belief Revision for Language-Model Agents in Non-Stationary Pair Trading
Polydoros Giannouris, Yuechen Jiang, Lingfei Qian +5
Many sequential decision-making problems exhibit hierarchical structure, where high-level semantic choices constrain downstream actions and feedback is delayed and ambiguous. Learn…
SAHM: A Benchmark for Arabic Financial and Shari'ah-Compliant Reasoning
Rania Elbadry, Sarfraz Ahmad, Ahmed Heakl +12
English financial NLP has advanced rapidly through benchmarks targeting earnings analysis, market sentiment, tabular reasoning, and financial question answering, yet Arabic financi…
HiRAS: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution
Hanhua Hong, Yizhi LI, Jiaoyan Chen +4
Recent advances in large language models have highlighted their potential to automate computational research, particularly reproducing experimental results. However, existing appro…
MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model
Zhiwei Liu, Yuyan Wang, Yuechen Jiang +8
Financial misinformation poses significant threats to financial market stability and individuals' investment decisions. The multilingual environment and the inherent complexity of…
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…
XCR-Bench: Benchmarking Cross-Cultural Reasoning in LLMs via Culture-Specific Items and Hall's Triad
Mohsinul Kabir, Tasnim Ahmed, Md Mezbaur Rahman +5
Cross-cultural competence in large language models (LLMs) requires understanding and adapting Culture-Specific Items (CSIs) across varying cultural contexts. However, progress in e…
Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection
Zhiwei Liu, Yupen Cao, Yuechen Jiang +22
Large language models (LLMs) have been widely applied across various domains of finance. Since their training data are largely derived from human-authored corpora, LLMs may inherit…
NOMAD: A Multi-Agent LLM System for UML Class Diagram Generation from Natural Language Requirements
Polydoros Giannouris, Sophia Ananiadou
Large Language Models (LLMs) are increasingly utilised in software engineering, yet their ability to generate structured artefacts such as UML diagrams remains underexplored. In th…
FinCriticalED: A Visual Benchmark for Financial Fact-Level OCR
Yueru He, Xueqing Peng, Yupeng Cao +13
Recent progress in multimodal large language models (MLLMs) has substantially improved document understanding, yet strong optical character recognition (OCR) performance on surface…
Semantic Label Drift in Cross-Cultural Translation
Mohsinul Kabir, Tasnim Ahmed, Md Mezbaur Rahman +2
Machine Translation (MT) is widely employed to address resource scarcity in low-resource languages by generating synthetic data from high-resource counterparts. While sentiment pre…
THCM-CAL: Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction
Xin Zhang, Qiyu Wei, Yingjie Zhu +2
Automated clinical risk prediction from electronic health records (EHRs) demands modeling both structured diagnostic codes and unstructured narrative notes. However, most prior app…
From n-gram to Attention: How Model Architectures Learn and Propagate Bias in Language Modeling
Mohsinul Kabir, Tasfia Tahsin, Sophia Ananiadou
Current research on bias in language models (LMs) predominantly focuses on data quality, with significantly less attention paid to model architecture and temporal influences of dat…
Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs
Mohsinul Kabir, Ajwad Abrar, Sophia Ananiadou
A large number of studies rely on closed-style multiple-choice surveys to evaluate cultural alignment in Large Language Models (LLMs). In this work, we challenge this constrained e…