Agentic Discovery with Active Hypothesis Exploration for Visual Recognition
News Release Summary
Researchers at Rice University have developed a system called HypoExplore that automates the process of designing neural network architectures for image recognition by treating the search as a structured scientific experiment rather than blind trial and error. The core problem the system addresses is that finding good neural architectures for specialized tasks — like medical imaging — still typically requires significant human expertise and repeated manual iteration. Instead of starting from an existing network and tweaking it, HypoExplore begins from scratch with only a high-level research direction, using a large language model to generate architectural ideas framed as explicit testable hypotheses. The system tracks every experiment in a branching tree structure and maintains a memory bank that records how much evidence has accumulated for or against each hypothesis, using those confidence scores to guide what to try next — balancing exploitation of ideas that have worked against exploration of uncertain ones. Running on CIFAR-10, the system evolved from a starting accuracy of 18.91% to 94.11% over 50 iterations, ultimately discovering a compact 0.9-million-parameter architecture called the Global Shape Token Network that matched or outperformed several well-known manually engineered networks while using far fewer parameters. The system also achieved state-of-the-art results on medical imaging benchmarks when run independently on that domain. Notably, the researchers showed that the hypothesis confidence scores became genuinely predictive over time — high-confidence hypotheses correctly forecast experimental outcomes 80% of the time — suggesting the system was building real transferable knowledge about architecture design rather than just stumbling onto good solutions.
abstract
We introduce HypoExplore, an agentic framework that formulates neural architecture discovery for visual recognition as a hypothesis-driven scientific inquiry. Given a human-specified high-level research direction, HypoExplore ideates, implements, evaluates, and improves neural architectures through evolutionary branching. New hypotheses are created using a large language model by selecting a parent hypothesis to build upon, guided by a dual strategy that balances exploiting validated principles with resolving uncertain ones. Our proposed framework maintains a Trajectory Tree that records the lineage of all proposed architectures, and a Hypothesis Memory Bank that actively tracks confidence scores acquired through experimental evidence. After each experiment, multiple feedback agents analyze the results from different perspectives and consolidate their findings into hypothesis confidence updates. Our framework is tested on discovering lightweight vision architectures on CIFAR-10, with the best achieving 94.11% accuracy evolved from a root node baseline that starts at 18.91%, and generalizes to CIFAR-100 and Tiny-ImageNet. We further demonstrate applicability to a specialized domain by conducting independent architecture discovery runs on MedMNIST, which yield a state-of-the-art performance. We show that hypothesis confidence scores grow increasingly predictive as evidence accumulates, and that the learned principles transfer across independent evolutionary lineages, suggesting that HypoExplore not only discovers stronger architectures, but can help build a genuine understanding of the design space.
citation
@article{kooagentic,
title = {Agentic Discovery with Active Hypothesis Exploration for Visual Recognition},
author = {Koo, Jaywon and Hernandez, Jefferson and He, Ruozhen and Chen, Hanjie and Wei, Chen and Ordonez, Vicente},
journal = {arXiv preprint arXiv:2604.12999},
url = {https://arxiv.org/abs/2604.12999},
}