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arXiv AI By Amirhossein Sadough, Freek Hens, Aleksa Bok\v{s}an, Mohammad Mahdi Dehshibi, Mahyar Shahsavari

TRIPROBE: Probing Task Separability Beyond Classification for XAI

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TRIPROBE is a multi-level probing framework designed to diagnose task separability in machine learning pipelines. It evaluates separability across input spaces, learned feature representations, and classifier outputs by decomposing multi-task problems into binary subtasks and applying Foundational, Latent, and Final probes. Using Maximum Fisher's Discriminant Ratio, TRIPROBE identifies bottlenecks and task pairs that affect performance, as demonstrated on the Roshambo sEMG benchmark.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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