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Ao Qu

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Book Open access Aug 2026

MicrobeQuest: A Context-Aware Multimodal Benchmark for Microbiology Information Extraction

This work introduces MicrobeQuest, the first comprehensive multimodal benchmark for microbial information extraction tasks, comprising 11,877 expert-validated, context-aware query–response pairs, and formalizes microbiology-specific IE as a class of tasks that require structured data extraction, joint multimodal unders...

Ou Zheng, Xue Ren, Xuexia Su et al. · 0 citations

CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery

Evaluated on diverse mathematical, algorithmic, and systems optimization tasks, CORAL sets new state-of-the-art results on 10 tasks, achieving 3-10 times higher improvement rates with far fewer evaluations than fixed evolutionary search baselines across tasks.

Ao Qu, Handi Zheng, Zi-Jian Zhou et al. · 38 citations · ⚡7
Preprint Aug 2026

SPADE: Self-Play in Adaptive Synthetic Executable Environments

SPADE (Self-Play in Adaptive Synthetic Executable Executable Environments), a self-play RL framework in which a single LLM plays two roles: an Environment Designer that writes complete, long-horizon training environments as executable code with an OpenAI Gym-style reset()/step() interface, and a Reasoning Agent that le...

Bo Liu, Simon Yu, Yiding Jiang et al. · 7 citations
#artificial intelligence Preprint Oct 2025

HugAgent: A Human Simulation Benchmark for Individual-Level Reasoning

The HUman-Grounded AGENT Benchmark is introduced, which rethinks human reasoning simulation along three dimensions: (i) from averaged to individualized reasoning, (ii) from behavioral mimicry to cognitive alignment, and (iii) from vignette-based to open-ended data.

Chance Jiajie Li, Zhenze Mo, Yuhan Tang et al. · 2 citations
#artificial intelligence Preprint Aug 2026

Deploying and Evaluating a Smart-Agriculture Agentic Engine for Full-Season Soybean Farm Operations

FAIRY is developed to execute and evaluate agentic agronomic operations on full-season spatiotemporal workflows that span ridge preparation, planting, irrigation, fertilization, pest and disease treatment, harvest, grain handling, drying, and storage, and an evaluation suite that combines agentic success, full-path spa...

Ao Qu, Panagiotis Michelakis, Lin-Yuan Han et al. · 0 citations
#artificial intelligence Preprint Aug 2026

SPADE: Self-Play in Adaptive Synthetic Executable Environments

SPADE (Self-Play in Adaptive Synthetic Executable Executable Environments), a self-play RL framework in which a single LLM plays two roles: an Environment Designer that writes complete, long-horizon training environments as executable code with an OpenAI Gym-style reset()/step() interface, and a Reasoning Agent that le...

Bo Liu, Simon Yu, Yiding Jiang et al. · 1 citation

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