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These phenomena motivate our method

Final Collapse:At the top layer, h(L−1)(x)≈v c for allxin classc, wherev c is the prototype vector for classc

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MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper

cs.LG · 2025-08-31 · conditional · novelty 5.0

MEPT routes each input through one of several per-layer prompt experts plus a shared expert, reporting modest accuracy gains and a 79% cut in activated prompt parameters on SuperGLUE.

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  • MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper cs.LG · 2025-08-31 · conditional · none · ref 3

    MEPT routes each input through one of several per-layer prompt experts plus a shared expert, reporting modest accuracy gains and a 79% cut in activated prompt parameters on SuperGLUE.