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20242026
most citedFino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance

2 citations · 6 across the 30 of their papers we have counts for

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9 papers · 1 filter

cs.AI2026

A safety-oriented hypothetico-deductive framework for AI-assisted differential diagnosis

Fan Ma, Mauro Giuffrè, Donald Wright +12

Diagnostic error is a major threat to patient safety, yet current large language model (LLM) systems often treat diagnosis as a one-shot prediction task, lacking safeguards against…

cs.AI2026

Can LLMs Be CEOs? Benchmarking Strategic Resource Reallocation with Multi-Role Agent Simulation

Yuyang Dai, Xueqing Peng, Lingfei Qian +1

Evaluating the decision-making capabilities of large language models (LLMs) is a growing research priority, yet existing benchmarks focus on isolated cognitive tasks such as reason…

cs.AI2026

AUDITFLOW: Executable Symbolic Environments for Structured Financial Reporting Verification

Yan Wang, Xuguang Ai, Jaisal Patel +7

Structured financial audit verification is difficult for language-model agents because correctness depends on structured evidence rather than text alone. A model must link reported…

cs.AI2026

Herculean: An Agentic Benchmark for Financial Intelligence

Xueqing Peng, Zhuohan Xie, Yupeng Cao +60

As AI agents improve, the central question is no longer whether they can solve isolated well-defined financial tasks, but whether they can reliably carry out financial professional…

cs.AI2026

Foundation Models to Unlock Real-World Evidence from Nationwide Medical Claims

Fan Ma, Yuntian Liu, Xiang Lan +22

Evidence derived from large-scale real-world data (RWD) is increasingly informing regulatory evaluation and healthcare decision-making. Administrative claims provide population-sca…

cs.AI2026

Moira: Language-driven Hierarchical Reinforcement Learning for 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…