Author

Zhengyuan Liu

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#artificial intelligence Review Sep 2026

When Does AI Augment Work? A Workflow-Level Framework for Human-Agent Collaboration

This work establishes a precise definition of AI augmentation comprising six conditions, spanning durable net value, meaningful human control, accountability and recovery, and long-term human development through learning, career pathways, and job purpose, and outlines how organisations, researchers, and government lead...

Civic-Ai Collaboration Jiaying Wu, Caleb Ziems, Raymond Chan et al. · 0 citations

#artificial intelligence Preprint Aug 2026

MNIST-PRO: MNIST is Back as a Partially Observable World for AI Agents

MnIST-PRO is addressed, a benchmark that isolates agentic perception by converting MNIST digit recognition into a sequential, glimpse-based search task with lookback constraints, and ten multimodal models are evaluated, showing that simply acquiring visual evidence is not enough and agents must also be able to build an...

Vernon Toh, N. Majumder, Zhengyuan Liu et al. · 0 citations

Preprint Aug 2026

ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step

ScrambleToolBench is introduced, an interactive terminal benchmark designed to isolate behavioral reasoning by removing semantic cues and enforcing a continuous task curriculum, which requires agents to uncover hidden tool behaviors entirely through trial-and-error interaction.

Vernon Toh, Navonil Majumder, Zhengyuan Liu et al. · 1 citation