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This paper introduces an algebraic framework using neural ideals and neural codes to classify and interpret features in neural networks, with practical applications on the MNIST dataset and an open-source visualization tool.
Agentic interpretability is emerging as a research direction where AI agents autonomously form hypotheses, design experiments, and refine explanations for model internals. Three works—SAGE, Agentic-iModels, and HYVE—exemplify this shift toward autonomous, hypothesis-driven interpretability, improving feature autointerpretation, model design, and circuit explanation.