# Selective Router Repair (SRR)

The work introduces a routing analysis toolkit and, across DeepSeekMoE, OLMoE and Qwen3-MoE, crosses source and merged router inputs and parameters and runs paired token- and task-level evaluations, finding that most expert reassignments are input-shift-induced rather than parameter-induced, that source-relative routing differences poorly predict next-token likelihood gains from source-route restoration, and that different expert selections can produce directionally similar mixture outputs; it therefore redefines routing failure as task loss recoverable under a specified routing intervention with non-routing parameters fixed, and proposes Selective Router Repair as a case study that finds no reliable evidence that source-specialist token-likelihood advantages identify beneficial local corr

The work introduces a routing analysis toolkit and, across DeepSeekMoE, OLMoE and Qwen3-MoE, crosses source and merged router inputs and parameters and runs paired token- and task-level evaluations, finding that most expert reassignments are input-shift-induced rather than parameter-induced, that source-relative routing differences poorly predict next-token likelihood gains from source-route restoration, and that different expert selections can produce directionally similar mixture outputs; it therefore redefines routing failure as task loss recoverable under a specified routing intervention with non-routing parameters fixed, and proposes Selective Router Repair as a case study that finds no reliable evidence that source-specialist token-likelihood advantages identify beneficial local corr