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20242026
most citedAdvancing AI Research Assistants with Expert-Involved Learning

1 citations · 1 across the 13 of their papers we have counts for

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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

Step-level Optimization for Efficient Computer-use Agents

Jinbiao Wei, Kangqi Ni, Yilun Zhao +2

Computer-use agents provide a promising path toward general software automation because they can interact directly with arbitrary graphical user interfaces instead of relying on br…

cs.AI2026

Survey on Evaluation of LLM-based Agents

Asaf Yehudai, Lilach Eden, Alan Li +5

LLM-based agents represent a paradigm shift in AI, enabling autonomous systems to plan, reason, and use tools while interacting with dynamic environments. This paper provides the f…

cs.AI2026

A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning

Tianyu Yang, Sihong Wu, Yilun Zhao +6

Multimodal Mathematical Reasoning (MMR) has recently attracted increasing attention for its capability to solve mathematical problems involving both textual and visual modalities.…

cs.AI2026

ANCHOR: Branch-Point Data Generation for GUI Agents

Jinbiao Wei, Yilun Zhao, Kangqi Ni +1

End-to-end GUI agents for real desktop environments require large amounts of high-quality interaction data, yet collecting human demonstrations is expensive and existing synthetic…

cs.AI20261 cited

Advancing AI Research Assistants with Expert-Involved Learning

Tianyu Liu, Simeng Han, Hanchen Wang +27

Large language models (LLMs) and large multimodal models (LMMs) promise to accelerate biomedical discovery, yet their reliability remains unclear. We introduce ARIEL (AI Research A…